Shows information on the latest package additions to CRAN.
whatsNew(last.days = 0, brief = TRUE, index = NULL)
| last.days | The length of the period (in days) for which package additions to CRAN shall be presented. |
|---|---|
| brief | Determines if all avalilable package description fields shall be shown ( |
| index | Either a path (or URL) to a search index, or a search index that is already loaded. If no index is provided, |
Number of packages covered by the period specified in last.days.
Joachim Zuckarelli joachim@zuckarelli.de
# \donttest{ whatsNew(last.days = 3)#> #> Published today (8 packages): #> #> Package bardr #> #> Title: Complete Works of William Shakespeare in Tidy Format #> #> Long description: #> Provides R data structures for Shakespeare's complete works, #> as provided by Project Gutenberg <https:www.gutenberg.org/ebooks/100>. #> #> Maintainer: Zane Billings <wz.billings@gmail.com> #> #> #> Package chicane #> #> Title: Capture Hi-C Analysis Engine #> #> Long description: #> Toolkit for processing and calling interactions in capture Hi-C data. Converts BAM files into counts of reads linking restriction fragments, and identifies pairs of fragments that interact more than expected by chance. Significant interactions are identified by comparing the observed read count to the expected background rate from a count regression model. #> #> Maintainer: Erle Holgersen <erle.holgersen@gmail.com> #> #> #> Package GetDFPData2 #> #> Title: Reading Annual and Quarterly Financial Reports from B3 #> #> Long description: #> Reads annual and quarterly financial reports from companies traded at B3, the Brazilian exchange #> <http://www.b3.com.br/>. #> All data is downloaded and imported from CVM's public ftp site <http://dados.cvm.gov.br/dados/CIA_ABERTA/>. #> #> Maintainer: Marcelo Perlin <marceloperlin@gmail.com> #> #> #> Package lacunaritycovariance #> #> Title: Gliding Box Lacunarity and Other Metrics for 2D Random Closed #> Sets #> #> Long description: #> Functions for estimating the gliding box lacunarity (GBL), #> covariance, and pair-correlation of a random closed set (RACS) in 2D #> from a binary coverage map (e.g. presence-absence land cover maps). #> Contains a number of newly-developed covariance-based estimators of #> GBL (Hingee et al., 2019) <doi:10.1007/s13253-019-00351-9> and #> balanced estimators, proposed by Picka (2000) #> <http://www.jstor.org/stable/1428408>, for covariance, centred #> covariance, and pair-correlation. Also contains methods for #> estimating contagion-like properties of RACS and simulating 2D Boolean #> models. Binary coverage maps are usually represented as raster images #> with pixel values of TRUE, FALSE or NA, with NA representing #> unobserved pixels. A demo for extracting such a binary map from a #> geospatial data format is provided. Binary maps may also be #> represented using polygonal sets as the foreground, however for most #> computations such maps are converted into raster images. The package #> is based on research conducted during the author's PhD studies. #> #> Maintainer: Kassel Liam Hingee <kassel.hingee@gmail.com> #> #> #> Package plumber #> #> Title: An API Generator for R #> #> Long description: #> Gives the ability to automatically generate and serve an HTTP API #> from R functions using the annotations in the R documentation around your #> functions. #> #> Maintainer: Barret Schloerke <barret@rstudio.com> #> #> #> Package rcompendium #> #> Title: Create a Package or Research Compendium Structure #> #> Long description: #> Makes easier the creation of R package or research compendium #> (i.e. a predefined files/folders structure) so that users can focus on the #> code/analysis instead of wasting time organizing files. A full #> ready-to-work structure is set up with some additional features: version #> control, remote repository creation, CI/CD configuration (check package #> integrity under several OS, test code with 'testthat', and build and deploy #> website using 'pkgdown'). This package heavily relies on the R packages #> 'devtools' and 'usethis' and follows recommendations made by Wickham H. #> (2015) <ISBN:9781491910597> and Marwick B. et al. (2018) #> <doi:10.7287/peerj.preprints.3192v2>. #> #> Maintainer: Nicolas Casajus <nicolas.casajus@fondationbiodiversite.fr> #> #> #> Package strucchangeRcpp #> #> Title: Testing, Monitoring, and Dating Structural Changes: C++ Version #> #> Long description: #> A fast implementation with additional experimental features for #> testing, monitoring and dating structural changes in (linear) #> regression models. 'strucchangeRcpp' features tests/methods from #> the generalized fluctuation test framework as well as from #> the F test (Chow test) framework. This includes methods to #> fit, plot and test fluctuation processes (e.g. cumulative/moving #> sum, recursive/moving estimates) and F statistics, respectively. #> These methods are described in Zeileis et al. (2002) #> <doi:10.18637/jss.v007.i02>. #> Finally, the breakpoints in regression models with structural #> changes can be estimated together with confidence intervals, #> and their magnitude as well as the model fit can be evaluated #> using a variety of statistical measures. #> #> Maintainer: Dainius Masiliunas <pastas4@gmail.com> #> #> #> Package TREXr #> #> Title: Tree Sap Flow Extractor #> #> Long description: #> Performs data assimilation, processing and analyses on sap flow data obtained #> with the thermal dissipation method (TDM). The package includes functions for gap filling #> time-series data, detecting outliers, calculating data-processing uncertainties and #> generating uniform data output and visualisation. The package is designed to deal with #> large quantities of data and to apply commonly used data-processing methods. The functions #> have been validated on data collected from different tree species across #> the northern hemisphere (Peters et al. 2018 <doi:10.1111/nph.15241>). #> #> Maintainer: Richard Peters <richardlouispeters3@hotmail.com> #> #> #> Published yesterday (64 packages): #> #> Package ADMUR #> #> Title: Ancient Demographic Modelling Using Radiocarbon #> #> Long description: #> Provides tools to directly model underlying population dynamics using date datasets (radiocarbon and other) with a Continuous Piecewise Linear (CPL) model framework. Various other model types included. Taphonomic loss included optionally as a power function. Model comparison framework using BIC. Package also calibrates 14C samples, generates Summed Probability Distributions (SPD), and performs SPD simulation analysis to generate a Goodness-of-fit test for the best selected model. Details about the method can be found in Timpson A., Barberena R., Thomas M. G., Mendez C., Manning K. (2020) <doi:10.1098/rstb.2019.0723>. #> #> Maintainer: Adrian Timpson <a.timpson@ucl.ac.uk> #> #> #> Package asteRisk #> #> Title: Computation of Satellite Position #> #> Long description: #> Provides basic functionalities to calculate the position of #> satellites given a known state vector. The package includes implementations #> of the SGP4 and SDP4 simplified perturbation models to propagate orbital #> state vectors, as well as utilities to read TLE files and convert coordinates #> between different frames of reference. #> Felix R. Hoots, Ronald L. Roehrich and T.S. Kelso (1988) <https://celestrak.com/NORAD/documentation/spacetrk.pdf>. #> David Vallado, Paul Crawford, Richard Hujsak and T.S. Kelso (2012) <doi:10.2514/6.2006-6753>. #> Felix R. Hoots, Paul W. Schumacher Jr. and Robert A. Glover (2014) <doi:10.2514/1.9161>. #> #> Maintainer: Rafael Ayala <rafael.ayala@oist.jp> #> #> #> Package bannerCommenter #> #> Title: Make Banner Comments with a Consistent Format #> #> Long description: #> A convenience package for use while drafting code. #> It facilitates making stand-out comment lines decorated with #> bands of characters. The input text strings are converted into #> R comment lines, suitably formatted. These are then displayed in #> a console window and, if possible, automatically transferred to a #> clipboard ready for pasting into an R script. Designed to save #> time when drafting R scripts that will need to be navigated and #> maintained by other programmers. #> #> Maintainer: Bill Venables <Bill.Venables@gmail.com> #> #> #> Package BFS #> #> Title: Search and Download Data from the Swiss Federal Statistical #> Office (BFS) #> #> Long description: #> Search and download data from the Swiss Federal Statistical Office <https://www.bfs.admin.ch/>. #> #> Maintainer: Félix Luginbuhl <felix.luginbuhl@protonmail.ch> #> #> #> Package bgumbel #> #> Title: Bimodal Gumbel Distribution #> #> Long description: #> Bimodal Gumbel distribution. General functions for performing extreme value analysis. #> #> Maintainer: Pedro C. Brom <pcbrom@gmail.com> #> #> #> Package bpnreg #> #> Title: Bayesian Projected Normal Regression Models for Circular Data #> #> Long description: #> Fitting Bayesian multiple and mixed-effect regression models for #> circular data based on the projected normal distribution. Both continuous #> and categorical predictors can be included. Sampling from the posterior is #> performed via an MCMC algorithm. Posterior descriptives of all parameters, #> model fit statistics and Bayes factors for hypothesis tests for inequality #> constrained hypotheses are provided. See Cremers, Mulder & Klugkist (2018) #> <doi:10.1111/bmsp.12108> and Nuñez-Antonio & Guttiérez-Peña (2014) #> <doi:10.1016/j.csda.2012.07.025>. #> #> Maintainer: Jolien Cremers <joliencremers@gmail.com> #> #> #> Package ClimMobTools #> #> Title: API Client for the 'ClimMob' Platform #> #> Long description: #> API client for 'ClimMob', an open source software for crowdsourcing #> citizen science in agriculture under the 'tricot' method <https://climmob.net/>. #> Developed by van Etten et al. (2019) <doi:10.1017/S0014479716000739>, it turns the #> research paradigm on its head; instead of a few researchers designing complicated #> trials to compare several technologies in search of the best solutions, #> it enables many farmers to carry out reasonably simple experiments that #> taken together can offer even more information. 'ClimMobTools' enables project #> managers to deep explore and analyse their 'ClimMob' data in R. #> #> Maintainer: Kauê de Sousa <kaue.desousa@inn.no> #> #> #> Package CLVTools #> #> Title: Tools for Customer Lifetime Value Estimation #> #> Long description: #> #> A set of state-of-the-art probabilistic modeling approaches to derive estimates of individual customer lifetime values (CLV). #> Commonly, probabilistic approaches focus on modelling 3 processes, i.e. individuals' attrition, transaction, and spending process. #> Latent customer attrition models, which are also known as "buy-'til-you-die models", model the attrition as well as the transaction process. #> They are used to make inferences and predictions about transactional patterns of individual customers such as their future purchase behavior. #> Moreover, these models have also been used to predict individuals’ long-term engagement in activities such as playing an online game or #> posting to a social media platform. The spending process is usually modelled by a separate probabilistic model. Combining these results yields in #> lifetime values estimates for individual customers. #> This package includes fast and accurate implementations of various probabilistic models for non-contractual settings #> (e.g., grocery purchases or hotel visits). All implementations support time-invariant covariates, which can be used to control for e.g., #> socio-demographics. If such an extension has been proposed in literature, we further provide the possibility to control for time-varying #> covariates to control for e.g., seasonal patterns. #> Currently, the package includes the following latent attrition models to model individuals' attrition and transaction process: #> [1] Pareto/NBD model (Pareto/Negative-Binomial-Distribution), #> [2] the Extended Pareto/NBD model (Pareto/Negative-Binomial-Distribution with time-varying covariates), #> [3] the BG/NBD model (Beta-Gamma/Negative-Binomial-Distribution) and the #> [4] GGom/NBD (Gamma-Gompertz/Negative-Binomial-Distribution). #> Further, we provide an implementation of the Gamma/Gamma model to model the spending process of individuals. #> #> Maintainer: Patrick Bachmann <patrick.bachmann@business.uzh.ch> #> #> #> Package Compositional #> #> Title: Compositional Data Analysis #> #> Long description: #> Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included. #> The standard textbook for such data is John Aitchison's (1986) "The statistical analysis of compositional data". Relevant papers include: #> a) Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. Fourth International International Workshop on Compositional Data Analysis. #> b) Tsagris M. (2014). The k-NN algorithm for compositional data: a revised approach with and without zero values present. Journal of Data Science, 12(3):519--534. #> c) Tsagris M. (2015). A novel, divergence based, regression for compositional data. Proceedings of the 28th Panhellenic Statistics Conference, 15-18 April 2015, Athens, Greece, 430--444. #> d) Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2):47--57. #> e) Tsagris M., Preston S. and Wood A.T.A. (2016). Improved supervised classification for compositional data using the alpha-transformation. Journal of Classification, 33(2):243--261. <doi:10.1007/s00357-016-9207-5>. #> f) Tsagris M., Preston S. and Wood A.T.A. (2017). Nonparametric hypothesis testing for equality of means on the simplex. Journal of Statistical Computation and Simulation, 87(2): 406--422. <doi:10.1080/00949655.2016.1216554>. #> g) Tsagris M. and Stewart C. (2018). A Dirichlet regression model for compositional data with zeros. Lobachevskii Journal of Mathematics,39(3): 398--412. <doi:10.1134/S1995080218030198>. #> h) Alenazi A. (2019). Regression for compositional data with compositional data as predictor variables with or without zero values. Journal of Data Science, 17(1): 219--238. <doi:10.6339/JDS.201901_17(1).0010>. #> i) Tsagris M. and Stewart C. (2020). A folded model for compositional data analysis. Australian and New Zealand Journal of Statistics, 62(2):249--277. <doi:10.1111/anzs.12289>. #> j) Tsagris M., Alenazi A. and Stewart C. (2020). The alpha-k-NN regression for compositional data. <arXiv:2002.05137>. #> We further include functions for percentages (or proportions). #> #> Maintainer: Michail Tsagris <mtsagris@uoc.gr> #> #> #> Package cops #> #> Title: Cluster Optimized Proximity Scaling #> #> Long description: #> Cluster optimized proximity scaling (COPS) refers to multidimensional scaling (MDS) methods that aim at pronouncing the clustered appearance of the configuration (Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027> ). They achieve this by transforming proximities/distances with power functions and augment the fitting criterion with a clusteredness index, the OPTICS Cordillera (Rusch, Hornik & Mair, 2018, <doi:10.1080/10618600.2017.1349664> ). There are two variants: One for finding the configuration directly (COPS-C) for ratio, power, interval and non-metric MDS (Borg & Groenen, 2005, ISBN:978-0-387-28981-6), and one for using the augmented fitting criterion to find optimal parameters (P-COPS). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different MDS models in a COPS framework like ratio, interval and non-metric MDS for COPS-C and P-COPS with Torgerson scaling (Torgerson, 1958, ISBN:978-0471879459), scaling by majorizing a complex function (SMACOF; de Leeuw, 1977, <https://escholarship.org/uc/item/4ps3b5mj> ), Sammon mapping (Sammon, 1969, <doi:10.1109/T-C.1969.222678> ), elastic scaling (McGee, 1966, <doi:10.1111/j.2044-8317.1966.tb00367.x> ), s-stress (Takane, Young & de Leeuw, 1977, <doi:10.1007/BF02293745> ), r-stress (de Leeuw, Groenen & Mair, 2016, <https://rpubs.com/deleeuw/142619>), power-stress (Buja & Swayne, 2002 <doi:10.1007/s00357-001-0031-0>), restricted power stress, approximated power stress, power elastic scaling, power Sammon mapping (Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027> ). All of these models can also solely be fit as MDS with power transformations. The package further contains a function for pattern search optimization, the ``Adaptive Luus-Jakola Algorithm'' (Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027> ). #> #> Maintainer: Thomas Rusch <thomas.rusch@wu.ac.at> #> #> #> Package CORElearn #> #> Title: Classification, Regression and Feature Evaluation #> #> Long description: #> A suite of machine learning algorithms written in C++ with the R #> interface contains several learning techniques for classification and regression. #> Predictive models include e.g., classification and regression trees with #> optional constructive induction and models in the leaves, random forests, kNN, #> naive Bayes, and locally weighted regression. All predictions obtained with these #> models can be explained and visualized with the 'ExplainPrediction' package. #> This package is especially strong in feature evaluation where it contains several variants of #> Relief algorithm and many impurity based attribute evaluation functions, e.g., Gini, #> information gain, MDL, and DKM. These methods can be used for feature selection #> or discretization of numeric attributes. #> The OrdEval algorithm and its visualization is used for evaluation #> of data sets with ordinal features and class, enabling analysis according to the #> Kano model of customer satisfaction. #> Several algorithms support parallel multithreaded execution via OpenMP. #> The top-level documentation is reachable through ?CORElearn. #> #> Maintainer: "Marko Robnik-Sikonja" <marko.robnik@fri.uni-lj.si> #> #> #> Package covid19.analytics #> #> Title: Load and Analyze Live Data from the CoViD-19 Pandemic #> #> Long description: #> Load and analyze updated time series worldwide data of reported cases for the Novel CoronaVirus Disease (CoViD-19) from different sources, including the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) data repository <https://github.com/CSSEGISandData/COVID-19>, "Our World in Data" <https://github.com/owid/> among several others. The datasets reporting the CoViD19 cases are available in two main modalities, as a time series sequences and aggregated data for the last day with greater spatial resolution. Several analysis, visualization and modelling functions are available in the package that will allow the user to compute and visualize total number of cases, total number of changes and growth rate globally or for an specific geographical location, while at the same time generating models using these trends; generate interactive visualizations and generate Susceptible-Infected-Recovered (SIR) model for the disease spread. #> #> Maintainer: Marcelo Ponce <mponce@scinet.utoronto.ca> #> #> #> Package cricketr #> #> Title: Analyze Cricketers and Cricket Teams Based on ESPN Cricinfo #> Statsguru #> #> Long description: #> Tools for analyzing performances of cricketers based on stats in #> ESPN Cricinfo Statsguru. The toolset can be used for analysis of Tests,ODIs #> and Twenty20 matches of both batsmen and bowlers. The package can also be used to #> analyze team performances. #> #> Maintainer: Tinniam V Ganesh <tvganesh.85@gmail.com> #> #> #> Package Cyclops #> #> Title: Cyclic Coordinate Descent for Logistic, Poisson and Survival #> Analysis #> #> Long description: #> This model fitting tool incorporates cyclic coordinate descent and #> majorization-minimization approaches to fit a variety of regression models #> found in large-scale observational healthcare data. Implementations focus #> on computational optimization and fine-scale parallelization to yield #> efficient inference in massive datasets. Please see: #> Suchard, Simpson, Zorych, Ryan and Madigan (2013) <doi:10.1145/2414416.2414791>. #> #> Maintainer: Marc A. Suchard <msuchard@ucla.edu> #> #> #> Package DHARMa #> #> Title: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) #> Regression Models #> #> Long description: #> The 'DHARMa' package uses a simulation-based approach to create #> readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed #> models. Currently supported are linear and generalized linear (mixed) models from 'lme4' #> (classes 'lmerMod', 'glmerMod'), 'glmmTMB' 'GLMMadaptive' and 'spaMM', generalized additive models #> ('gam' from 'mgcv'), 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and #> 'lm' model classes. Moreover, externally created simulations, e.g. posterior predictive simulations #> from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well. #> The resulting residuals are standardized to values between 0 and 1 and can be interpreted #> as intuitively as residuals from a linear regression. The package also provides a number of #> plot and test functions for typical model misspecification problems, such as #> over/underdispersion, zero-inflation, and residual spatial and temporal autocorrelation. #> #> Maintainer: Florian Hartig <florian.hartig@biologie.uni-regensburg.de> #> #> #> Package dyndimred #> #> Title: Dimensionality Reduction Methods in a Common Format #> #> Long description: #> #> Provides a common interface for applying dimensionality reduction methods, #> such as Principal Component Analysis ('PCA'), Independent Component Analysis ('ICA'), diffusion maps, #> Locally-Linear Embedding ('LLE'), t-distributed Stochastic Neighbor Embedding ('t-SNE'), #> and Uniform Manifold Approximation and Projection ('UMAP'). #> Has built-in support for sparse matrices. #> #> Maintainer: Robrecht Cannoodt <rcannood@gmail.com> #> #> #> Package dynwrap #> #> Title: Representing and Inferring Single-Cell Trajectories #> #> Long description: #> Provides functionality to infer trajectories from single-cell data, #> represent them into a common format, and adapt them. Other biological information #> can also be added, such as cellular grouping, RNA velocity and annotation. #> Saelens et al. (2019) <doi:10.1038/s41587-019-0071-9>. #> #> Maintainer: Robrecht Cannoodt <rcannood@gmail.com> #> #> #> Package EdSurvey #> #> Title: Analysis of NCES Education Survey and Assessment Data #> #> Long description: #> Read in and analyze functions for education survey and assessment data from the National Center for Education Statistics (NCES) <https://nces.ed.gov/>, including National Assessment of Educational Progress (NAEP) data <https://nces.ed.gov/nationsreportcard/> and data from the International Assessment Database: Organisation for Economic Co-operation and Development (OECD) <https://www.oecd.org/>, including Programme for International Student Assessment (PISA), Teaching and Learning International Survey (TALIS), Programme for the International Assessment of Adult Competencies (PIAAC), and International Association for the Evaluation of Educational Achievement (IEA) <https://www.iea.nl/>, including Trends in International Mathematics and Science Study (TIMSS), TIMSS Advanced, Progress in International Reading Literacy Study (PIRLS), International Civic and Citizenship Study (ICCS), International Computer and Information Literacy Study (ICILS), and Civic Education Study (CivEd). #> #> Maintainer: Paul Bailey <pbailey@air.org> #> #> #> Package EIX #> #> Title: Explain Interactions in 'XGBoost' #> #> Long description: #> Structure mining from 'XGBoost' and 'LightGBM' models. #> Key functionalities of this package cover: visualisation of tree-based ensembles models, #> identification of interactions, measuring of variable importance, #> measuring of interaction importance, explanation of single prediction #> with break down plots (based on 'xgboostExplainer' and 'iBreakDown' packages). #> To download the 'LightGBM' use the following link: <https://github.com/Microsoft/LightGBM>. #> 'EIX' is a part of the 'DrWhy.AI' universe. #> #> Maintainer: Szymon Maksymiuk <sz.maksymiuk@gmail.com> #> #> #> Package ExamPAData #> #> Title: Data Sets for Predictive Analytics Exam #> #> Long description: #> Contains all data sets for Exam PA: Predictive Analytics at #> <https://exampa.net/>. #> #> Maintainer: Guanglai Li <liguanglai@gmail.com> #> #> #> Package FITSio #> #> Title: FITS (Flexible Image Transport System) Utilities #> #> Long description: #> Utilities to read and write files in the FITS (Flexible #> Image Transport System) format, a standard format in astronomy (see #> e.g. <https://en.wikipedia.org/wiki/FITS> for more information). #> Present low-level routines allow: reading, parsing, and modifying #> FITS headers; reading FITS images (multi-dimensional arrays); #> reading FITS binary and ASCII tables; and writing FITS images #> (multi-dimensional arrays). Higher-level functions allow: reading #> files composed of one or more headers and a single (perhaps #> multidimensional) image or single table; reading tables into #> data frames; generating vectors for image array axes; scaling and #> writing images as 16-bit integers. Known incompletenesses are #> reading random group extensions, as well as #> bit, complex, and array descriptor data types in binary tables. #> #> Maintainer: Andrew Harris <harris@astro.umd.edu> #> #> #> Package gerbil #> #> Title: Generalized Efficient Regression-Based Imputation with Latent #> Processes #> #> Long description: #> Implements a new multiple imputation method that draws #> imputations from a latent joint multivariate normal model which #> underpins generally structured data. This model is constructed using a #> sequence of flexible conditional linear models that enables the #> resulting procedure to be efficiently implemented on high dimensional #> datasets in practice. #> #> Maintainer: Michael Robbins <mrobbins@rand.org> #> #> #> Package gimme #> #> Title: Group Iterative Multiple Model Estimation #> #> Long description: #> Automated identification and estimation of group- and #> individual-level relations in time series data. #> #> Maintainer: KM Gates <gateskm@email.unc.edu> #> #> #> Package gm #> #> Title: Generate Musical Scores Easily and Show Them Anywhere #> #> Long description: #> Provides a simple and intuitive language, with which you can #> create complex music easily. Takes care of all the dirty technical #> details in converting your music to musical scores and audio files. #> Works in 'R Markdown' documents <https://rmarkdown.rstudio.com/>, #> R 'Jupyter Notebooks' <https://jupyter.org/>, and 'RStudio' #> <https://rstudio.com/>, so you can embed musical scores and audio files #> anywhere. Internally, uses 'MusicXML' <https://www.musicxml.com/> to #> represent musical scores, and 'MuseScore' <https://musescore.org/> to #> convert 'MusicXML'. #> #> Maintainer: Renfei Mao <renfeimao@gmail.com> #> #> #> Package hsrecombi #> #> Title: Estimation of Recombination Rate and Maternal LD in Half-Sibs #> #> Long description: #> Paternal recombination rate and maternal linkage disequilibrium #> (LD) are estimated for pairs of biallelic markers such as single nucleotide #> polymorphisms (SNPs) from progeny genotypes and sire haplotypes. The #> implementation relies on paternal half-sib families. If maternal half-sib #> families are used, the roles of sire/dam are swapped. Multiple families can #> be considered. For parameter estimation, at least one sire has to be double #> heterozygous at the investigated pairs of SNPs. #> Based on recombination rates, genetic distances between markers can be #> estimated. Markers with unusually large recombination rate to markers in #> close proximity can be discarded in this derivation. #> *A pipeline is available at github* #> <https://github.com/wittenburg/hsrecombi> #> Hampel, Teuscher, Gomez-Raya, Doschoris, Wittenburg (2018) "Estimation of #> recombination rate and maternal linkage disequilibrium in half-sibs" #> <doi:10.3389/fgene.2018.00186>. #> Gomez-Raya (2012) "Maximum likelihood estimation of linkage disequilibrium #> in half-sib families" <doi:10.1534/genetics.111.137521>. #> #> Maintainer: Dörte Wittenburg <wittenburg@fbn-dummerstorf.de> #> #> #> Package iccde #> #> Title: Computation of the Double-Entry Intraclass Correlation #> #> Long description: #> The function computes the double-entry intraclass correlation, which is an index of profile similarity (Furr, 2010; McCrae, 2008). The double-entry intraclass correlation is a more precise index of the agreement of two empirically observed profiles than the often-used intraclass correlation (McCrae, 2008). The function transforms profiles comprising correlations according to the Fisher z-transformation before the double-entry intraclass correlation is calculated. If the profiles comprise scores such as sum scores from various personality scales, it is recommended to standardize each individual score before entering into the function (McCrae, 2008). In case of missing values, the function will automatically use pairwise deletion. See Furr (2010) <doi:10.1080/00223890903379134> or McCrae (2008) <doi:10.1080/00223890701845104> for details. #> #> Maintainer: Christian Blötner <c.bloetner@gmail.com> #> #> #> Package IRISSeismic #> #> Title: Classes and Methods for Seismic Data Analysis #> #> Long description: #> Provides classes and methods for seismic data analysis. The #> base classes and methods are inspired by the python code found in #> the 'ObsPy' python toolbox <https://github.com/obspy/obspy>. Additional classes and #> methods support data returned by web services provided by the 'IRIS DMC' #> <http://service.iris.edu/>. #> #> Maintainer: Gillian Sharer <gillian@iris.washington.edu> #> #> #> Package isoSurv #> #> Title: Isotonic Regression on Survival Analysis #> #> Long description: #> Nonparametric estimation on survival analysis under order restrictions. It estimates monotone increasing or decreasing covariate effects in the proportional hazards model. Yunro Chung et al. (2018) <doi:10.1093/biomet/asx064>. #> #> Maintainer: Yunro Chung <yunro.chung@asu.edu> #> #> #> Package lambdaTS #> #> Title: Variational Seq2Seq Model with Lambda Transformer for Time #> Series Analysis #> #> Long description: #> Time series analysis based on lambda transformer and variational seq2seq, built on 'Torch'. #> #> Maintainer: Giancarlo Vercellino <giancarlo.vercellino@gmail.com> #> #> #> Package lpirfs #> #> Title: Local Projections Impulse Response Functions #> #> Long description: #> Provides functions to estimate and plot linear as well as nonlinear impulse #> responses based on local projections by Jordà (2005) <doi:10.1257/0002828053828518>. #> #> Maintainer: Philipp Adämmer <adaemmer@hsu-hh.de> #> #> #> Package mcreplicate #> #> Title: Multi-Core Replicate #> #> Long description: #> Multi-core replication function to make it easier to do fast #> Monte Carlo simulation. Based on the mcreplicate() function from the #> 'rethinking' package. The 'rethinking' package requires installing 'rstan', #> which is onerous to install, while also not adding capabilities to this #> function. #> #> Maintainer: Christopher Gandrud <christopher.gandrud@gmail.com> #> #> #> Package mobsim #> #> Title: Spatial Simulation and Scale-Dependent Analysis of Biodiversity #> Changes #> #> Long description: #> Tools for the simulation, analysis and sampling of spatial #> biodiversity data (May et al. 2017) <doi:10.1101/209502>. #> In the simulation tools user define the numbers of #> species and individuals, the species abundance distribution and species #> aggregation. Functions for analysis include species rarefaction #> and accumulation curves, species-area relationships and the distance #> decay of similarity. #> #> Maintainer: Felix May <felix.may@posteo.de> #> #> #> Package MultOrdRS #> #> Title: Model Multivariate Ordinal Responses Including Response Styles #> #> Long description: #> In the case of multivariate ordinal responses, parameter estimates can be severely biased if personal response styles are ignored. This packages provides methods to account for personal response styles and to explain the effects of covariates on the response style, as proposed by Schauberger and Tutz 2021 <doi:10.1177/1471082X20978034>. The method is implemented both for the multivariate cumulative model and the multivariate adjacent categories model. #> #> Maintainer: Gunther Schauberger <gunther.schauberger@tum.de> #> #> #> Package nflfastR #> #> Title: Functions to Efficiently Access NFL Play by Play Data #> #> Long description: #> A set of functions to access National Football #> League play-by-play data from <https://www.nfl.com/>. #> #> Maintainer: Ben Baldwin <bbaldwin206@gmail.com> #> #> #> Package pavo #> #> Title: Perceptual Analysis, Visualization and Organization of Spectral #> Colour Data #> #> Long description: #> A cohesive framework for parsing, analyzing and #> organizing colour from spectral data. #> #> Maintainer: Thomas White <thomas.white026@gmail.com> #> #> #> Package pcsstools #> #> Title: Tools for Regression Using Pre-Computed Summary Statistics #> #> Long description: #> Defines functions to describe regression models using only #> pre-computed summary statistics (i.e. means, variances, and covariances) #> in place of individual participant data. #> Possible models include linear models for linear combinations, products, #> and logical combinations of phenotypes. #> Implements methods presented in #> Wolf et al. (2021) <doi:10.1101/2021.03.08.433979> #> Wolf et al. (2020) <doi:10.1142/9789811215636_0063> and #> Gasdaska et al. (2019) <doi:10.1142/9789813279827_0036>. #> #> Maintainer: Jack Wolf <jackwolf910@gmail.com> #> #> #> Package PHInfiniteEstimates #> #> Title: Tools for Inference in the Presence of a Monotone Likelihood #> #> Long description: #> Proportional hazards estimation in the presence of a partially monotone likelihood has difficulties, in that finite estimators do not exist. These difficulties are related to those arising from logistic and multinomial regression. References for methods are given in the separate function documents. #> #> Maintainer: John E. Kolassa <kolassa@stat.rutgers.edu> #> #> #> Package powerEQTL #> #> Title: Power and Sample Size Calculation for eQTL Analysis #> #> Long description: #> Power and sample size calculation for eQTL analysis #> based on ANOVA or simple linear regression. It can also calculate power/sample size #> for testing the association of a SNP to a continuous type phenotype. #> #> Maintainer: Weiliang Qiu <weiliang.qiu@gmail.com> #> #> #> Package powerMediation #> #> Title: Power/Sample Size Calculation for Mediation Analysis #> #> Long description: #> Functions to #> calculate power and sample size for testing #> (1) mediation effects; #> (2) the slope in a simple linear regression; #> (3) odds ratio in a simple logistic regression; #> (4) mean change for longitudinal study with 2 time points; #> (5) interaction effect in 2-way ANOVA; and #> (6) the slope in a simple Poisson regression. #> #> Maintainer: Weiliang Qiu <weiliang.qiu@gmail.com> #> #> #> Package processx #> #> Title: Execute and Control System Processes #> #> Long description: #> Tools to run system processes in the background. #> It can check if a background process is running; wait on a background #> process to finish; get the exit status of finished processes; kill #> background processes. It can read the standard output and error of #> the processes, using non-blocking connections. 'processx' can poll #> a process for standard output or error, with a timeout. It can also #> poll several processes at once. #> #> Maintainer: Gábor Csárdi <csardi.gabor@gmail.com> #> #> #> Package qape #> #> Title: Quantile of Absolute Prediction Errors #> #> Long description: #> Estimates QAPE using bootstrap procedures. The residual, parametric and double bootstrap is used. The test of normality using Cholesky decomposition is added. #> #> Maintainer: Alicja Wolny-Dominiak <alicja.wolny-dominiak@ue.katowice.pl> #> #> #> Package rADA #> #> Title: Statistical Analysis and Cut-Point Determination of Immunoassays #> #> Long description: #> Systematically transform immunoassay data, evaluate if the data is normally distributed, and pick the right method for cut point determination based on that evaluation. This package can also produce plots that are needed for reports, so data analysis and visualization can be done easily. #> #> Maintainer: Emma Gail <emmahelengail@gmail.com> #> #> #> Package RClimacell #> #> Title: R Wrapper for the 'Climacell' API #> #> Long description: #> 'Climacell' is a weather platform that provides hyper-local forecasts and weather #> data. This package enables the user to query the core layers of the #> time line interface of the 'Climacell' v4 API <https://www.climacell.co/weather-api/>. #> This package requires a valid API key. See vignettes for instructions on use. #> #> Maintainer: Nikhil Agarwal <gitnik@niks.me> #> #> #> Package rcompanion #> #> Title: Functions to Support Extension Education Program Evaluation #> #> Long description: #> Functions and datasets to support "Summary and Analysis of #> Extension Program Evaluation in R" and "An R #> Companion for the Handbook of Biological Statistics". #> Vignettes are available at <http://rcompanion.org>. #> #> Maintainer: Salvatore Mangiafico <mangiafico@njaes.rutgers.edu> #> #> #> Package RImageJROI #> #> Title: Read 'ImageJ' Region of Interest (ROI) Files #> #> Long description: #> Provides functions to read 'ImageJ' (<http://imagej.nih.gov/ij/>) #> Region of Interest (ROI) files, to plot the ROIs and to convert them to #> 'spatstat' (<http://spatstat.org/>) spatial patterns. #> #> Maintainer: David C Sterratt <david.c.sterratt@ed.ac.uk> #> #> #> Package riskyr #> #> Title: Rendering Risk Literacy more Transparent #> #> Long description: #> Risk-related information (like the prevalence of conditions, the sensitivity and specificity of diagnostic tests, or the effectiveness of interventions or treatments) can be expressed in terms of frequencies or probabilities. By providing a toolbox of corresponding metrics and representations, 'riskyr' computes, translates, and visualizes risk-related information in a variety of ways. Adopting multiple complementary perspectives provides insights into the interplay between key parameters and renders teaching and training programs on risk literacy more transparent. #> #> Maintainer: Hansjoerg Neth <h.neth@uni.kn> #> #> #> Package rlas #> #> Title: Read and Write 'las' and 'laz' Binary File Formats Used for #> Remote Sensing Data #> #> Long description: #> Read and write 'las' and 'laz' binary file formats. The LAS file format is a public file format for the interchange of 3-dimensional point cloud data between data users. The LAS specifications are approved by the American Society for Photogrammetry and Remote Sensing <https://www.asprs.org/divisions-committees/lidar-division/laser-las-file-format-exchange-activities>. The LAZ file format is an open and lossless compression scheme for binary LAS format versions 1.0 to 1.3 <https://laszip.org/>. #> #> Maintainer: Jean-Romain Roussel <jean-romain.roussel.1@ulaval.ca> #> #> #> Package Rmpfr #> #> Title: R MPFR - Multiple Precision Floating-Point Reliable #> #> Long description: #> Arithmetic (via S4 classes and methods) for #> arbitrary precision floating point numbers, including transcendental #> ("special") functions. To this end, the package interfaces to #> the 'LGPL' licensed 'MPFR' (Multiple Precision Floating-Point Reliable) Library #> which itself is based on the 'GMP' (GNU Multiple Precision) Library. #> #> Maintainer: Martin Maechler <maechler@stat.math.ethz.ch> #> #> #> Package rnpn #> #> Title: Interface to the National 'Phenology' Network 'API' #> #> Long description: #> Programmatic interface to the #> Web Service methods provided by the National 'Phenology' Network #> (<https://usanpn.org/>), which includes data on various life history #> events that occur at specific times. #> #> Maintainer: Lee Marsh <lee@usanpn.org> #> #> #> Package RSA #> #> Title: Response Surface Analysis #> #> Long description: #> Advanced response surface analysis. The main function RSA computes #> and compares several nested polynomial regression models (full second- or #> third-order polynomial, shifted and rotated squared difference model, #> rising ridge surfaces, basic squared difference model, asymmetric or #> level-dependent congruence effect models). The package provides plotting #> functions for 3d wireframe surfaces, interactive 3d plots, and contour plots. #> Calculates many surface parameters (a1 to a5, principal axes, stationary point, #> eigenvalues) and provides standard, robust, or bootstrapped standard errors #> and confidence intervals for them. #> #> Maintainer: Felix Schönbrodt <felix@nicebread.de> #> #> #> Package sfsmisc #> #> Title: Utilities from 'Seminar fuer Statistik' ETH Zurich #> #> Long description: #> Useful utilities ['goodies'] from Seminar fuer Statistik ETH Zurich, #> some of which were ported from S-plus in the 1990s. #> For graphics, have pretty (Log-scale) axes, an enhanced Tukey-Anscombe #> plot, combining histogram and boxplot, 2d-residual plots, a 'tachoPlot()', #> pretty arrows, etc. #> For robustness, have a robust F test and robust range(). #> For system support, notably on Linux, provides 'Sys.*()' functions with #> more access to system and CPU information. #> Finally, miscellaneous utilities such as simple efficient prime numbers, #> integer codes, Duplicated(), toLatex.numeric() and is.whole(). #> #> Maintainer: Martin Maechler <maechler@stat.math.ethz.ch> #> #> #> Package slider #> #> Title: Sliding Window Functions #> #> Long description: #> Provides type-stable rolling window functions over any R data #> type. Cumulative and expanding windows are also supported. For more #> advanced usage, an index can be used as a secondary vector that #> defines how sliding windows are to be created. #> #> Maintainer: Davis Vaughan <davis@rstudio.com> #> #> #> Package sparklyr #> #> Title: R Interface to Apache Spark #> #> Long description: #> R interface to Apache Spark, a fast and general #> engine for big data processing, see <http://spark.apache.org>. This #> package supports connecting to local and remote Apache Spark clusters, #> provides a 'dplyr' compatible back-end, and provides an interface to #> Spark's built-in machine learning algorithms. #> #> Maintainer: Yitao Li <yitao@rstudio.com> #> #> #> Package spatstat.core #> #> Title: Core Functionality of the 'spatstat' Family #> #> Long description: #> Functionality for data analysis and modelling of #> spatial data, mainly spatial point patterns, #> in the 'spatstat' family of packages. #> (Excludes analysis of spatial data on a linear network, #> which is covered by the separate package 'spatstat.linnet'.) #> Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. #> Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. #> A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. #> Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. #> #> Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au> #> #> #> Package spork #> #> Title: Generalized Label Formatting #> #> Long description: #> The 'spork' syntax describes #> label formatting concisely, supporting #> mixed nesting of subscripts and superscripts #> to arbitrary depth. It intends to be easy #> to read and write in plain text, and easy #> to convert to equivalent presentations #> in 'plotmath' and 'latex'. Greek symbols #> and a multiplication symbol are explicitly #> supported. See ?as_spork and ?as_previews. #> #> Maintainer: Tim Bergsma <bergsmat@gmail.com> #> #> #> Package SPUTNIK #> #> Title: SPatially aUTomatic deNoising for Ims toolKit #> #> Long description: #> A set of tools for the peak filtering of mass spectrometry #> imaging data (MSI or IMS) based on spatial distribution of signal. Given a #> region-of-interest (ROI), representing the spatial region where the informative #> signal is expected to be localized, a series of filters determine which peak #> signals are characterized by an implausible spatial distribution. The filters #> reduce the dataset dimensionality and increase its information vs noise ratio, #> improving the quality of the unsupervised analysis results, reducing data #> dimensionality and simplifying the chemical interpretation. #> #> Maintainer: Paolo Inglese <p.inglese@outlook.com> #> #> #> Package stlnpp #> #> Title: Spatio-Temporal Analysis of Point Patterns on Linear Networks #> #> Long description: #> Statistical analysis of spatio-temporal point processes on linear networks. This packages provides tools to visualise and analyse spatio-temporal point patterns on linear networks using first- and second-order summary statistics. #> #> Maintainer: Mehdi Moradi <m2.moradi@yahoo.com> #> #> #> Package SurviMChd #> #> Title: High Dimensional Survival Data Analysis with Markov Chain Monte #> Carlo #> #> Long description: #> High dimensional survival data analysis with Markov Chain Monte Carlo(MCMC). #> Currently support frailty data analysis. Allows for Weibull and #> Exponential distribution. Includes function for interval censored data. #> #> Maintainer: Atanu Bhattacharjee <atanustat@gmail.com> #> #> #> Package tensorflow #> #> Title: R Interface to 'TensorFlow' #> #> Long description: #> Interface to 'TensorFlow' <https://www.tensorflow.org/>, #> an open source software library for numerical computation using data #> flow graphs. Nodes in the graph represent mathematical operations, #> while the graph edges represent the multidimensional data arrays #> (tensors) communicated between them. The flexible architecture allows #> you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop, #> server, or mobile device with a single 'API'. 'TensorFlow' was originally #> developed by researchers and engineers working on the Google Brain Team #> within Google's Machine Intelligence research organization for the #> purposes of conducting machine learning and deep neural networks research, #> but the system is general enough to be applicable in a wide variety #> of other domains as well. #> #> Maintainer: Daniel Falbel <daniel@rstudio.com> #> #> #> Package tidyHeatmap #> #> Title: A Tidy Implementation of Heatmap #> #> Long description: #> This is a tidy implementation for heatmap. At the #> moment it is based on the (great) package 'ComplexHeatmap'. The goal #> of this package is to interface a tidy data frame with this powerful #> tool. Some of the advantages are: Row and/or columns colour #> annotations are easy to integrate just specifying one parameter #> (column names). Custom grouping of rows is easy to specify providing #> a grouped tbl. For example: df %>% group_by(...). Labels size #> adjusted by row and column total number. Default use of Brewer and #> Viridis palettes. #> #> Maintainer: Stefano Mangiola <mangiolastefano@gmail.com> #> #> #> Package tidyndr #> #> Title: Analysis of the Nigeria National Data Repository (NDR) #> #> Long description: #> The goal is to simplify routine analysis of the Nigeria National Data Repository (NDR) <https://ndr.shieldnigeriaproject.com> using the PEPFAR Monitoring, Evaluation, and Reporting (MER) indicators (see <https://datim.zendesk.com/hc/en-us/articles/360000084446-MER-Indicator-Reference-Guides>). It is designed to import in to R patient-level line-list downloaded as 'csv' file from the front-end of the NDR. #> #> Maintainer: Stephen Balogun <stephentaiyebalogun@gmail.com> #> #> #> Package tweenr #> #> Title: Interpolate Data for Smooth Animations #> #> Long description: #> In order to create smooth animation between states of data, #> tweening is necessary. This package provides a range of functions for #> creating tweened data that can be used as basis for animation. Furthermore #> it adds a number of vectorized interpolaters for common R data #> types such as numeric, date and colour. #> #> Maintainer: Thomas Lin Pedersen <thomasp85@gmail.com> #> #> #> Package virtuoso #> #> Title: Interface to 'Virtuoso' using 'ODBC' #> #> Long description: #> Provides users with a simple and convenient #> mechanism to manage and query a 'Virtuoso' database using the 'DBI' (Data-Base Interface) #> compatible 'ODBC' (Open Database Connectivity) interface. #> 'Virtuoso' is a high-performance "universal server," which can act #> as both a relational database, supporting standard Structured Query #> Language ('SQL') queries, while also supporting data following the #> Resource Description Framework ('RDF') model for Linked Data. #> 'RDF' data can be queried using 'SPARQL' ('SPARQL' Protocol and 'RDF' Query Language) #> queries, a graph-based query that supports semantic reasoning. #> This allows users to leverage the performance of local or remote 'Virtuoso' servers using #> popular 'R' packages such as 'DBI' and 'dplyr', while also providing a #> high-performance solution for working with large 'RDF' 'triplestores' from 'R.' #> The package also provides helper routines to install, launch, and manage #> a 'Virtuoso' server locally on 'Mac', 'Windows' and 'Linux' platforms using #> the standard interactive installers from the 'R' command-line. By #> automatically handling these setup steps, the package can make using 'Virtuoso' #> considerably faster and easier for a most users to deploy in a local #> environment. Managing the bulk import of triples #> from common serializations with a single intuitive command is another key #> feature of this package. Bulk import performance can be tens to #> hundreds of times faster than the comparable imports using existing 'R' tools, #> including 'rdflib' and 'redland' packages. #> #> Maintainer: Carl Boettiger <cboettig@gmail.com> #> #> #> Package ymlthis #> #> Title: Write 'YAML' for 'R Markdown', 'bookdown', 'blogdown', and More #> #> Long description: #> Write 'YAML' front matter for R Markdown and related #> documents. yml_*() functions write 'YAML' and use_*() functions let #> you write the resulting 'YAML' to your clipboard or to .yml files #> related to your project. #> #> Maintainer: Malcolm Barrett <malcolmbarrett@gmail.com> #> #> #> Published on 2021-03-22 (69 packages): #> #> Package BinNonNor #> #> Title: Data Generation with Binary and Continuous Non-Normal Components #> #> Long description: #> Generation of multiple binary and continuous non-normal variables simultaneously #> given the marginal characteristics and association structure based on the methodology #> proposed by Demirtas et al. (2012) <DOI:10.1002/sim.5362>. #> #> Maintainer: Ran Gao <rgao8@uic.edu> #> #> #> Package bpca #> #> Title: Biplot of Multivariate Data Based on Principal Components #> Analysis #> #> Long description: #> Implements biplot (2d and 3d) of multivariate data based #> on principal components analysis and diagnostic tools of the quality of the reduction. #> #> Maintainer: Jose Claudio Faria <joseclaudio.faria@gmail.com> #> #> #> Package BSW #> #> Title: Fitting a Log-Binomial Model using the Bekhit-Schöpe-Wagenpfeil #> (BSW) Algorithm #> #> Long description: #> Implements a modified Newton-type algorithm (BSW algorithm) for solving the maximum likelihood estimation problem in fitting a log-binomial model under linear inequality constraints. #> #> Maintainer: Adam Bekhit <imbei@med-imbei.uni-saarland.de> #> #> #> Package canvasXpress.data #> #> Title: Datasets for the 'canvasXpress' Package #> #> Long description: #> Contains the prepared data that is needed for the 'shiny' application examples in the #> 'canvasXpress' package. This package also includes datasets used for automated 'testthat' tests. #> Scotto L, Narayan G, Nandula SV, Arias-Pulido H et al. (2008) <doi:10.1002/gcc.20577>. #> Davis S, Meltzer PS (2007) <doi:10.1093/bioinformatics/btm254>. #> #> Maintainer: Connie Brett <connie@aggregate-genius.com> #> #> #> Package CME.assistant #> #> Title: Reusable Assisting Functions for Child Mortality Estimation #> #> Long description: Provide helper functions for UNICEF child mortality estimation. #> #> Maintainer: Yang Liu <lyhello@gmail.com> #> #> #> Package decorators #> #> Title: Extend the Behaviour of a Function without Explicitly Modifying #> it #> #> Long description: #> A decorator is a function that receives a function, extends its #> behaviour, and returned the altered function. Any caller that uses the #> decorated function uses the same interface as it were the original, #> undecorated function. Decorators serve two primary uses: (1) Enhancing the #> response of a function as it sends data to a second component; (2) #> Supporting multiple optional behaviours. An example of the first use is a #> timer decorator that runs a function, outputs its execution time on the #> console, and returns the original function's result. An example of the #> second use is input type validation decorator that during running time #> tests whether the caller has passed input arguments of a particular class. #> Decorators can reduce execution time, say by memoization, or reduce bugs #> by adding defensive programming routines. #> #> Maintainer: Harel Lustiger <tidylab@gmail.com> #> #> #> Package diffobj #> #> Title: Diffs for R Objects #> #> Long description: #> Generate a colorized diff of two R objects for an intuitive #> visualization of their differences. #> #> Maintainer: Brodie Gaslam <brodie.gaslam@yahoo.com> #> #> #> Package Dire #> #> Title: Linear Regressions with a Latent Outcome Variable #> #> Long description: #> Fit linear models, estimating score distributions for groups of people, following Cohen and Jiang (1999) <doi:10.2307/2669917>. In this model, the response is a latent trait (such as student ability) and raw item responses are combined with item difficulties in an item response theory (IRT) framework to form a density for each unit (student). This latent trait is then integrated out. This software is intended to fit the same models as the existing software 'AM' <http://am.air.org/>. #> #> Maintainer: Paul Bailey <pbailey@air.org> #> #> #> Package drimmR #> #> Title: Estimation, Simulation and Reliability of Drifting Markov Models #> #> Long description: #> Performs the drifting Markov models (DMM) which are #> non-homogeneous Markov models designed for modeling the heterogeneities of #> sequences in a more flexible way than homogeneous Markov chains or even #> hidden Markov models. In this context, we developed an R package dedicated to #> the estimation, simulation and the exact computation of associated reliability #> of drifting Markov models. The implemented methods are described in #> Vergne, N. (2008), <doi:10.2202/1544-6115.1326> and #> Barbu, V.S., Vergne, N. (2019) <doi:10.1007/s11009-018-9682-8> . #> #> Maintainer: Nicolas Vergne <nicolas.vergne@univ-rouen.fr> #> #> #> Package DSAIDE #> #> Title: #> Dynamical Systems Approach to Infectious Disease Epidemiology #> (Ecology/Evolution) #> #> Long description: #> Exploration of simulation models (apps) of various infectious disease transmission dynamics scenarios. #> The purpose of the package is to help individuals learn #> about infectious disease epidemiology (ecology/evolution) from a dynamical systems perspective. #> All apps include explanations of the underlying models and instructions on what to do with the models. #> #> Maintainer: Andreas Handel <ahandel@uga.edu> #> #> #> Package dynutils #> #> Title: Common Functionality for the 'dynverse' Packages #> #> Long description: #> #> Provides common functionality for the 'dynverse' packages. #> 'dynverse' is created to support the development, execution, and benchmarking of trajectory inference methods. #> For more information, check out <https://dynverse.org>. #> #> Maintainer: Robrecht Cannoodt <rcannood@gmail.com> #> #> #> Package essurvey #> #> Title: Download Data from the European Social Survey on the Fly #> #> Long description: #> Download data from the European Social Survey directly from their website <http://www.europeansocialsurvey.org/>. There are two families of functions that allow you to download and interactively check all countries and rounds available. #> #> Maintainer: Jorge Cimentada <cimentadaj@gmail.com> #> #> #> Package exploratory #> #> Title: Conduct Exploratory Analyses with a Point-and-Click Interface #> #> Long description: #> Conduct numerous exploratory analyses in an instant with a #> point-and-click interface. With one simple command, this tool #> launches a Shiny App on the local machine. Drag and drop variables #> in a data set to categorize them as possible independent, #> dependent, moderating, or mediating variables. Then run dozens #> (or hundreds) of analyses instantly to uncover any statistically #> significant relationships among variables. Any relationship #> thus uncovered should be tested in follow-up studies. #> This tool is designed only to facilitate exploratory #> analyses and should NEVER be used for p-hacking. Many of #> the functions used in this package are direct copies of functions #> in the R Package 'kim' and 'ezr'. #> Selected References: #> Chang et al. (2021) <https://CRAN.R-project.org/package=shiny>. #> Chang et al. (2018) <https://CRAN.R-project.org/package=shinydashboard>. #> Cohen (1988) <doi:10.4324/9780203771587>. #> Dowle et al. (2021) <https://CRAN.R-project.org/package=data.table>. #> Ioannidis (2005) <doi:10.1371/journal.pmed.0020124> #> Kim (2021) <doi:10.5281/zenodo.4619237>. #> Kim (2020) <https://CRAN.R-project.org/package=ezr>. #> Simmons et al. (2011) <doi:10.1177/0956797611417632> #> Tingley et al. (2019) <https://CRAN.R-project.org/package=mediation>. #> Wickham et al. (2020) <https://CRAN.R-project.org/package=ggplot2>. #> #> Maintainer: Jin Kim <jin.m.kim@yale.edu> #> #> #> Package eye #> #> Title: Analysis of Eye Data #> #> Long description: #> There is no ophthalmic researcher who has not had headaches from #> the handling of visual acuity entries. Different notations, untidy entries. #> This shall now be a matter of the past. Eye makes it as easy as pie to work #> with VA data - easy cleaning, easy conversion between #> Snellen, logMAR, ETDRS letters, and qualitative visual acuity #> shall never pester you again. The eye #> package automates the pesky task to count number of patients and eyes, #> and can help to clean data with easy re-coding for right and left eyes. #> It also contains functions to help reshaping eye side specific variables #> between wide and long format. Visual acuity conversion is based on #> Schulze-Bonsel et al. (2006) <doi:10.1167/iovs.05-0981>, #> Gregori et al. (2010) <doi:10.1097/iae.0b013e3181d87e04>, #> Beck et al. (2003) <doi:10.1016/s0002-9394(02)01825-1> and #> Bach (2007) <http:michaelbach.de/sci/acuity.html>. #> #> Maintainer: Tjebo Heeren <tjebo@gmx.de> #> #> #> Package fddm #> #> Title: Fast Implementation of the Diffusion Decision Model #> #> Long description: #> Provides the probability density function (PDF) of the diffusion decision #> model (DDM; e.g., Ratcliff & McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) #> with across-trial variability in the drift rate. #> Because the PDF of the DDM contains an infinite sum, it needs to be #> approximated. 'fddm' implements all published approximations (Navarro & Fuss, 2009, #> <doi:10.1016/j.jmp.2009.02.003>; Gondan, Blurton, & Kesselmeier, 2014, #> <doi:10.1016/j.jmp.2014.05.002>) plus new approximations. All approximations #> are implemented purely in 'C++' providing faster speed than existing packages. #> #> Maintainer: Henrik Singmann <singmann@gmail.com> #> #> #> Package feasts #> #> Title: Feature Extraction and Statistics for Time Series #> #> Long description: #> Provides a collection of features, decomposition methods, #> statistical summaries and graphics functions for the analysing tidy time #> series data. The package name 'feasts' is an acronym comprising of its key #> features: Feature Extraction And Statistics for Time Series. #> #> Maintainer: Mitchell O'Hara-Wild <mail@mitchelloharawild.com> #> #> #> Package GBcurves #> #> Title: Yield Curves of Brazil, China, and Russia #> #> Long description: #> Downloads and interpolates the Brazilian, Chinese, and Russian yield curves directly from <http://www.b3.com.br/>, <http://yield.chinabond.com.cn>, and <https://www.cbr.ru>, respectively. #> #> Maintainer: Werley Cordeiro <werleycordeiro@gmail.com> #> #> #> Package GEInter #> #> Title: Robust Gene-Environment Interaction Analysis #> #> Long description: #> For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that gene-environment interactions play important roles beyond the main genetic and environmental effects. In practical interaction analyses, outliers in response variables and covariates are not uncommon. In addition, missingness in environmental factors is routinely encountered in epidemiological studies. The developed package consists of five robust approaches to address the outliers problems, among which two approaches can also accommodate missingness in environmental factors. Both continuous and right censored responses are considered. The proposed approaches are based on penalization and sparse boosting techniques for identifying important interactions, which are realized using efficient algorithms. Beyond the gene-environment analysis, the developed package can also be adopted to conduct analysis on interactions between other types of low-dimensional and high-dimensional data. (Mengyun Wu et al (2017), <doi:10.1080/00949655.2018.1523411>; Mengyun Wu et al (2017), <doi:10.1002/gepi.22055>; Yaqing Xu et al (2018), <doi:10.1080/00949655.2018.1523411>; Yaqing Xu et al (2019), <doi:10.1016/j.ygeno.2018.07.006>). #> #> Maintainer: Xing Qin <qin.xing@163.sufe.edu.cn> #> #> #> Package genpwr #> #> Title: Power Calculations Under Genetic Model Misspecification #> #> Long description: #> Power and sample size calculations for genetic association studies allowing #> for misspecification of the model of genetic susceptibility. #> Power and/or sample size can be calculated for logistic (case/control study design) #> and linear (continuous phenotype) regression models, using additive, dominant, #> recessive or degree of freedom coding of the genetic covariate while assuming #> a true dominant, recessive or additive genetic effect. In addition, power and #> sample size calculations can be performed for gene by environment interactions. #> These methods are extensions of Gauderman (2002) #> <doi:10.1093/aje/155.5.478> and Gauderman (2002) <doi:10.1002/sim.973> #> and are described in: #> Moore CM, Jacobson S, Fingerlin TE. Power and Sample Size Calculations #> for Genetic Association Studies in the Presence of Genetic Model Misspecification. #> American Society of Human Genetics. #> October 2018, San Diego. #> #> Maintainer: Camille Moore <moorec@njhealth.org> #> #> #> Package gifski #> #> Title: Highest Quality GIF Encoder #> #> Long description: #> Multi-threaded GIF encoder written in Rust: <https://gif.ski/>. #> Converts images to GIF animations using pngquant's efficient cross-frame #> palettes and temporal dithering with thousands of colors per frame. #> #> Maintainer: Jeroen Ooms <jeroen@berkeley.edu> #> #> #> Package GWASinspector #> #> Title: Comprehensive and Easy to Use Quality Control of GWAS Results #> #> Long description: #> When evaluating the results of a genome-wide association study (GWAS), it is important to perform a quality control to ensure that the results are valid, complete, correctly formatted, and, in case of meta-analysis, consistent with other studies that have applied the same analysis. This package was developed to facilitate and streamline this process and provide the user with a comprehensive report. #> #> Maintainer: Alireza Ani <a.ani@umcg.nl> #> #> #> Package imp4p #> #> Title: Imputation for Proteomics #> #> Long description: #> Functions to analyse missing value mechanisms and to impute data sets in the context of bottom-up MS-based proteomics. #> #> Maintainer: Quentin Giai Gianetto <quentin2g@yahoo.fr> #> #> #> Package indiedown #> #> Title: Individual R Markdown Templates #> #> Long description: #> Simplifies the generation of customized R Markdown PDF templates. #> A template may include an individual logo, typography, geometry or color #> scheme. The package provides a skeleton with detailed instructions for #> customizations. The skeleton can be modified by changing defaults in the #> 'YAML' header, by adding additional 'LaTeX' commands or by applying dynamic #> adjustments in R. Individual corporate design elements, such as a title page, can be added as R functions that produce 'LaTeX' code. #> #> Maintainer: Christoph Sax <christoph.sax@gmail.com> #> #> #> Package inlabru #> #> Title: Bayesian Latent Gaussian Modelling using INLA and Extensions #> #> Long description: #> Facilitates spatial and general latent Gaussian modeling using #> integrated nested Laplace approximation via the INLA package (<https://www.r-inla.org>). #> Additionally, extends the GAM-like model class to more general nonlinear predictor #> expressions, and implements a log Gaussian Cox process likelihood for #> modeling univariate and spatial point processes based on ecological survey data. #> Model components are specified with general inputs and mapping methods to the #> latent variables, and the predictors are specified via general R expressions, #> with separate expressions for each observation likelihood model in #> multi-likelihood models. A prediction method based on fast Monte Carlo sampling #> allows posterior prediction of general expressions of the latent variables. #> Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019) #> <doi:10.1111/2041-210X.13168>. #> #> Maintainer: Finn Lindgren <finn.lindgren@gmail.com> #> #> #> Package insurancerating #> #> Title: Analytic Insurance Rating Techniques #> #> Long description: #> Methods for insurance rating. It helps actuaries to implement GLMs within all relevant steps needed to construct #> a risk premium from raw data. It provides a data driven strategy for the construction of insurance tariff classes. #> This strategy is based on the work by Antonio and Valdez (2012) <doi:10.1007/s10182-011-0152-7>. It also provides recipes #> on how to easily perform one-way, or univariate, analyses on an insurance portfolio. In addition it adds functionality #> to include reference categories in the levels of the coefficients in the output of a generalized linear regression analysis. #> #> Maintainer: Martin Haringa <mtharinga@gmail.com> #> #> #> Package isodistrreg #> #> Title: Isotonic Distributional Regression (IDR) #> #> Long description: #> Distributional regression under stochastic order restrictions for #> numeric and binary response variables and partially ordered covariates. See #> Henzi, Ziegel, Gneiting (2020) <arXiv:1909.03725>. #> #> Maintainer: Alexander Henzi <henzi.alexander@gmail.com> #> #> #> Package JMcmprsk #> #> Title: Joint Models for Longitudinal and Competing Risks Data #> #> Long description: #> Fit joint models of continuous or ordinal longitudinal data and time-to-event data with competing risks. For a detailed information, see Robert Elashoff, Gang Li and Ning Li (2016, ISBN:9781439807828); Robert M. Elashoff,Gang Li and Ning Li (2008) <doi:10.1111/j.1541-0420.2007.00952.x> ; Ning Li, Robert Elashoff, Gang Li and Jeffrey Saver (2010) <doi:10.1002/sim.3798> . #> #> Maintainer: Hong Wang <wh@csu.edu.cn> #> #> #> Package jsontools #> #> Title: Working with JSON Vectors #> #> Long description: #> A toolbox for working with JSON vectors similar to #> the functions 'Postgres' provides to work with JSON columns. It supports #> in parsing and formatting JSON, extracting data from JSON, and #> modifying JSON data. #> #> Maintainer: Maximilian Girlich <maximilian.girlich@outlook.com> #> #> #> Package libgeos #> #> Title: Open Source Geometry Engine ('GEOS') C API #> #> Long description: #> Provides the Open Source Geometry Engine ('GEOS') as a #> C API that can be used to write high-performance C and C++ #> geometry operations using R as an interface. Headers are provided #> to make linking to and using these functions from C++ code as #> easy and as safe as possible. This package contains an internal #> copy of the 'GEOS' library to guarantee the best possible #> consistency on multiple platforms. #> #> Maintainer: Dewey Dunnington <dewey@fishandwhistle.net> #> #> #> Package lightgbm #> #> Title: Light Gradient Boosting Machine #> #> Long description: #> Tree based algorithms can be improved by introducing boosting frameworks. #> 'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>. #> This package offers an R interface to work with it. #> It is designed to be distributed and efficient with the following advantages: #> 1. Faster training speed and higher efficiency. #> 2. Lower memory usage. #> 3. Better accuracy. #> 4. Parallel learning supported. #> 5. Capable of handling large-scale data. #> In recognition of these advantages, 'LightGBM' has been widely-used in many winning solutions of machine learning competitions. #> Comparison experiments on public datasets suggest that 'LightGBM' can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. In addition, parallel experiments suggest that in certain circumstances, 'LightGBM' can achieve a linear speed-up in training time by using multiple machines. #> #> Maintainer: Guolin Ke <guolin.ke@microsoft.com> #> #> #> Package LinearDetect #> #> Title: Change Point Detection in High-Dimensional Linear Regression #> Models #> #> Long description: #> A unified framework for simultaneous structural break detection and parameter estimation in high-dimensional linear models. The proposed method can handle a wide range of models, including change-in-mean model, multiple linear regression model, Vector auto-regressive model and Gaussian graphical model. #> #> Maintainer: Yue Bai <baiyue@ufl.edu> #> #> #> Package lqr #> #> Title: Robust Linear Quantile Regression #> #> Long description: #> It fits a robust linear quantile regression model using a new #> family of zero-quantile distributions for the error term. Missing values and censored observations can be handled as well. This family of #> distribution includes skewed versions of the Normal, Student's t, Laplace, Slash #> and Contaminated Normal distribution. It also performs logistic quantile regression for bounded responses #> as shown in Galarza et.al.(2020) <doi:10.1007/s13571-020-00231-0>. It provides estimates and full inference. #> It also provides envelopes plots for assessing the fit and confidences bands #> when several quantiles are provided simultaneously. #> #> Maintainer: Christian E. Galarza <cgalarza88@gmail.com> #> #> #> Package LSX #> #> Title: Model for Semisupervised Text Analysis Based on Word Embeddings #> #> Long description: #> A word embeddings-based semisupervised model for document scaling Watanabe (2020) <doi:10.1080/19312458.2020.1832976>. #> LSS allows users to analyze large and complex corpora on arbitrary dimensions with seed words exploiting efficiency of word embeddings (SVD, Glove). #> It can generate word vectors on a users-provided corpus or incorporate a pre-trained word vectors. #> #> Maintainer: Kohei Watanabe <watanabe.kohei@gmail.com> #> #> #> Package MATA #> #> Title: Model-Averaged Tail Area (MATA) Confidence Interval and #> Distribution #> #> Long description: #> Calculates Model-Averaged Tail Area Wald #> (MATA-Wald) confidence intervals, and MATA-Wald confidence densities #> and distributions, which are constructed using single-model #> frequentist estimators and model weights. See Turek and Fletcher #> (2012) <doi:10.1016/j.csda.2012.03.002> and Fletcher et al (2019) #> <doi:10.1007/s10651-019-00432-5> for details. #> #> Maintainer: Daniel Turek <danielturek@gmail.com> #> #> #> Package maxLik #> #> Title: Maximum Likelihood Estimation and Related Tools #> #> Long description: #> Functions for Maximum Likelihood (ML) estimation, non-linear #> optimization, and related tools. It includes a unified way to call #> different optimizers, and classes and methods to handle the results from #> the Maximum Likelihood viewpoint. It also includes a number of convenience tools for testing #> and developing your own models. #> #> Maintainer: Ott Toomet <otoomet@gmail.com> #> #> #> Package mitre #> #> Title: Cybersecurity MITRE Standards Data and Digraphs #> #> Long description: #> Extract, transform and load MITRE standards. #> This package gives you an approach to cybersecurity data sets. #> All data sets are build on runtime downloading raw data from MITRE public services. #> MITRE <https://www.mitre.org/> is a government-funded research organization #> based in Bedford and McLean. Current version includes most used standards as #> data frames. It also provide a list of nodes and edges with all relationships. #> #> Maintainer: Humbert Costas <humbert.costas@gmail.com> #> #> #> Package OEFPIL #> #> Title: Optimal Estimation of Function Parameters by Iterated #> Linearization #> #> Long description: #> Package for estimating the parameters of a nonlinear function using iterated linearization via Taylor series. Method is based on Kubacek (2000) ISBN: 80-244-0093-6. The algorithm is a generalization of the procedure given in Koning, R., Wimmer, G. and Witkovsky, V. (2014) <doi:10.1088/0957-0233/25/11/115001>. #> #> Maintainer: Stanislav Zamecnik <xzamecniks@math.muni.cz> #> #> #> Package oenb #> #> Title: Tools for the OeNB Data Web Service #> #> Long description: #> Tools to access data from the data web service of the Oesterreichische Nationalbank (OeNB), <https://www.oenb.at/en/Statistics/User-Defined-Tables/webservice.html>. #> #> Maintainer: Franz X. Mohr <franz.x.mohr@outlook.com> #> #> #> Package opentriviadb #> #> Title: Interface to the 'Open Trivia Database' #> #> Long description: #> The 'Open Trivia Database'<https://opentdb.com/> is a open source, #> user contributed, database of trivia questions and answers. This package #> provides an interface to download and access questions and answers from #> within R. #> #> Maintainer: Conor Neilson <condwanaland@gmail.com> #> #> #> Package osmdata #> #> Title: Import 'OpenStreetMap' Data as Simple Features or Spatial #> Objects #> #> Long description: #> Download and import of 'OpenStreetMap' ('OSM') data as 'sf' #> or 'sp' objects. 'OSM' data are extracted from the 'Overpass' web #> server (<https://overpass-api.de/>) and processed with very fast 'C++' #> routines for return to 'R'. #> #> Maintainer: Mark Padgham <mark.padgham@email.com> #> #> #> Package papeR #> #> Title: A Toolbox for Writing Pretty Papers and Reports #> #> Long description: #> A toolbox for writing 'knitr', 'Sweave' or other 'LaTeX'- or 'markdown'-based #> reports and to prettify the output of various estimated models. #> #> Maintainer: Benjamin Hofner <benjamin.hofner@pei.de> #> #> #> Package periscope #> #> Title: Enterprise Streamlined 'Shiny' Application Framework #> #> Long description: #> An enterprise-targeted scalable and UI-standardized 'shiny' framework #> including a variety of developer convenience functions with the goal of both #> streamlining robust application development while assisting with creating a #> consistent user experience regardless of application or developer. #> #> Maintainer: Constance Brett <connie@aggregate-genius.com> #> #> #> Package phangorn #> #> Title: Phylogenetic Reconstruction and Analysis #> #> Long description: #> Allows for estimation of phylogenetic trees and networks using #> Maximum Likelihood, Maximum Parsimony, distance methods and Hadamard #> conjugation. Offers methods for tree comparison, model selection and #> visualization of phylogenetic networks as described in Schliep et al. (2017) #> <doi:10.1111/2041-210X.12760>. #> #> Maintainer: Klaus Schliep <klaus.schliep@gmail.com> #> #> #> Package plumberDeploy #> #> Title: Plumber Deployment #> #> Long description: #> Gives the ability to automatically deploy a plumber API #> from R functions on 'DigitalOcean' and other cloud-based servers. #> #> Maintainer: Bruno Tremblay <cran@neoxone.com> #> #> #> Package PoisBinNonNor #> #> Title: Data Generation with Poisson, Binary and Continuous Components #> #> Long description: #> Generation of multiple count, binary and continuous variables simultaneously #> given the marginal characteristics and association structure. Throughout the package, #> the word 'Poisson' is used to imply count data under the assumption of Poisson distribution. #> The details of the method are explained in Amatya et al. (2015) <DOI:10.1080/00949655.2014.953534>. #> #> Maintainer: Ran Gao <rgao8@uic.edu> #> #> #> Package PoisBinOrd #> #> Title: Data Generation with Poisson, Binary and Ordinal Components #> #> Long description: #> Generation of multiple count, binary and ordinal variables simultaneously #> given the marginal characteristics and association structure. Throughout the package, #> the word 'Poisson' is used to imply count data under the assumption of Poisson distribution. The details of the method are explained in Amatya, A. and Demirtas, H. (2015) <DOI:10.1080/00949655.2014.953534>. #> #> Maintainer: Ran Gao <rgao8@uic.edu> #> #> #> Package PoisBinOrdNonNor #> #> Title: Generation of Up to Four Different Types of Variables #> #> Long description: #> Generation of a chosen number of count, binary, ordinal, and continuous random variables, with specified correlations and marginal properties. The details of the method are explained in Demirtas (2012) <DOI:10.1002/sim.5362>. #> #> Maintainer: Ran Gao <rgao8@uic.edu> #> #> #> Package polyRAD #> #> Title: #> Genotype Calling with Uncertainty from Sequencing Data in #> Polyploids and Diploids #> #> Long description: #> Read depth data from genotyping-by-sequencing (GBS) or restriction #> site-associated DNA sequencing (RAD-seq) are imported and used to make Bayesian #> probability estimates of genotypes in polyploids or diploids. The genotype #> probabilities, posterior mean genotypes, or most probable genotypes can then #> be exported for downstream analysis. 'polyRAD' is described by Clark et al. #> (2019) <doi:10.1534/g3.118.200913>. A variant calling pipeline for highly #> duplicated genomes is also included and is described by Clark et al. (2020) #> <doi:10.1101/2020.01.11.902890>. #> #> Maintainer: Lindsay V. Clark <lvclark@illinois.edu> #> #> #> Package RaMS #> #> Title: R Access to Mass-Spec Data #> #> Long description: #> R-based access to mass-spectrometry (MS) data. While many packages #> exist to process MS data, many of these make it difficult to #> access the underlying mass-to-charge ratio (m/z), intensity, and #> retention time of the files #> themselves. This package is designed to format MS data in a tidy fashion and #> allows the user perform the plotting and analysis. #> #> Maintainer: William Kumler <wkumler@uw.edu> #> #> #> Package rappsflyer #> #> Title: Work with AppsFlyer API #> #> Long description: #> Loading data from AppsFlyer Pull API #> <https://support.appsflyer.com/hc/en-us/articles/207034346-Using-Pull-API-aggregate-data>. #> #> Maintainer: Alexey Seleznev <selesnow@gmail.com> #> #> #> Package rasterVis #> #> Title: Visualization Methods for Raster Data #> #> Long description: #> Methods for enhanced visualization and interaction with raster data. It implements visualization methods for quantitative data and categorical data, both for univariate and multivariate rasters. It also provides methods to display spatiotemporal rasters, and vector fields. See the website for examples. #> #> Maintainer: Oscar Perpinan Lamigueiro <oscar.perpinan@upm.es> #> #> #> Package rEDM #> #> Title: Empirical Dynamic Modeling ('EDM') #> #> Long description: #> An implementation of 'EDM' algorithms based on research software developed for internal use at the Sugihara Lab ('UCSD/SIO'). The package is implemented with 'Rcpp' wrappers around the 'cppEDM' library. It implements the 'simplex' projection method from Sugihara & May (1990) <doi:10.1038/344734a0>, the 'S-map' algorithm from Sugihara (1994) <doi:10.1098/rsta.1994.0106>, convergent cross mapping described in Sugihara et al. (2012) <doi:10.1126/science.1227079>, and, 'multiview embedding' described in Ye & Sugihara (2016) <doi:10.1126/science.aag0863>. #> #> Maintainer: Joseph Park <JosephPark@IEEE.org> #> #> #> Package repmod #> #> Title: Create Report Table from Different Objects #> #> Long description: #> Tools for generating descriptives and report tables for different models, #> data.frames and tables and exporting them to different formats. #> #> Maintainer: David Hervas Marin <ddhervas@yahoo.es> #> #> #> Package Rfast2 #> #> Title: A Collection of Efficient and Extremely Fast R Functions II #> #> Long description: #> A collection of fast statistical and utility functions for data analysis. Functions for regression, maximum likelihood, column-wise statistics and many more have been included. C++ has been utilized to speed up the functions. #> #> Maintainer: Manos Papadakis <rfastofficial@gmail.com> #> #> #> Package rKolada #> #> Title: Access Data from the 'Kolada' Database #> #> Long description: #> Methods for downloading and processing data and metadata from 'Kolada', the official Swedish regions and municipalities database <https://kolada.se/>. #> #> Maintainer: Love Hansson <love.hansson@gmail.com> #> #> #> Package robustlm #> #> Title: Robust Variable Selection with Exponential Squared Loss #> #> Long description: #> Computationally efficient tool for performing variable selection and obtaining robust estimates, which implements robust variable selection procedure proposed by Wang, X., Jiang, Y., Wang, S., Zhang, H. (2013) <doi:10.1080/01621459.2013.766613>. Users can enjoy the near optimal, consistent, and oracle properties of the procedures. #> #> Maintainer: Jin Zhu <zhuj37@mail2.sysu.edu.cn> #> #> #> Package runexp #> #> Title: Softball Run Expectancy using Markov Chains and Simulation #> #> Long description: #> Implements two methods of estimating runs scored in a softball #> scenario: (1) theoretical expectation using discrete Markov chains and (2) empirical #> distribution using multinomial random simulation. Scores are based on player-specific input #> probabilities (out, single, double, triple, walk, and homerun). Optional inputs include probability #> of attempting a steal, probability of succeeding in an attempted steal, and an indicator of whether #> a player is "fast" (e.g. the player could stretch home). These probabilities may be #> calculated from common player statistics that are publicly available on team's webpages. #> Scores are evaluated based on a nine-player lineup and may be used to compare lineups, #> evaluate base scenarios, and compare the offensive potential of individual players. #> Manuscript forthcoming. See Bukiet & Harold (1997) <doi:10.1287/opre.45.1.14> for #> implementation of discrete Markov chains. #> #> Maintainer: Annie Sauer <anniees@vt.edu> #> #> #> Package sdpdth #> #> Title: M-Estimator for Threshold Spatial Dynamic Panel Data Model #> #> Long description: #> M-estimator for threshold and non-threshold spatial dynamic panel data model. Yang, Z (2018) <doi:10.1016/j.jeconom.2017.08.019>. Wu, J., Matsuda, Y (2021) <doi:10.1007/s43071-021-00008-1>. #> #> Maintainer: Junyue Wu <wu.junyue.p1@dc.tohoku.ac.jp> #> #> #> Package seminr #> #> Title: Domain-Specific Language for Building and Estimating Structural #> Equation Models #> #> Long description: #> A powerful, easy to syntax for specifying and estimating complex #> Structural Equation Models. Models can be estimated using Partial #> Least Squares Path Modeling or Covariance-Based Structural Equation #> Modeling or covariance based Confirmatory Factor Analysis. #> #> Maintainer: Nicholas Patrick Danks <nicholasdanks@hotmail.com> #> #> #> Package spatstat.geom #> #> Title: Geometrical Functionality of the 'spatstat' Family #> #> Long description: #> Defines types of spatial data such as point patterns, #> mainly in two dimensions, but also in higher dimensions. #> Provides class support, and functions for geometrical operations #> on spatial data, used in the 'spatstat' family of packages. #> Excludes spatial data on a linear network, which are covered by #> the separate package 'spatstat.linnet'. #> #> Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au> #> #> #> Package spotoroo #> #> Title: Spatiotemporal Clustering of Satellite Hot Spot Data #> #> Long description: An algorithm to cluster satellite hot spot data spatially and temporally. #> #> Maintainer: Weihao Li <llreczx@gmail.com> #> #> #> Package styler #> #> Title: Non-Invasive Pretty Printing of R Code #> #> Long description: Pretty-prints R code without changing the user's #> formatting intent. #> #> Maintainer: Lorenz Walthert <lorenz.walthert@icloud.com> #> #> #> Package Taxonstand #> #> Title: Taxonomic Standardization of Plant Species Names #> #> Long description: #> Automated standardization of taxonomic names and removal of orthographic errors in plant species names using 'The Plant List' website (www.theplantlist.org). #> #> Maintainer: Luis Cayuela <luis.cayuela@urjc.es> #> #> #> Package tidyseurat #> #> Title: Brings Seurat to the Tidyverse #> #> Long description: #> It creates an invisible layer that allow to see the 'Seurat' object #> as tibble and interact seamlessly with the tidyverse. #> #> Maintainer: Stefano Mangiola <mangiolastefano@gmail.com> #> #> #> Package traitdataform #> #> Title: Formatting and Harmonizing Ecological Trait-Data #> #> Long description: #> Assistance for handling ecological trait data and applying the #> Ecological Trait-Data Standard terminology (Schneider et al. 2019 #> <doi:10.1111/2041-210X.13288>). There are two major use cases: (1) preparation of #> own trait datasets for upload into public data bases, and (2) harmonizing #> trait datasets from different sources by re-formatting them into a unified #> format. See 'traitdataform' website for full documentation. #> #> Maintainer: Florian D. Schneider <florian.dirk.schneider@gmail.com> #> #> #> Package treefit #> #> Title: The First Software for Quantitative Trajectory Inference #> #> Long description: #> Perform two types of analysis: 1) checking the #> goodness-of-fit of tree models to your single-cell gene expression #> data; and 2) deciding which tree best fits your data. #> #> Maintainer: Kouhei Sutou <kou@clear-code.com> #> #> #> Package UCSCXenaTools #> #> Title: Download and Explore Datasets from UCSC Xena Data Hubs #> #> Long description: #> Download and explore datasets from UCSC Xena data hubs, which are #> a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others. #> Databases are normalized so they can be combined, linked, filtered, explored and downloaded. #> #> Maintainer: Shixiang Wang <w_shixiang@163.com> #> #> #> Package uni.survival.tree #> #> Title: A Survival Tree Based on Stabilized Score Tests for #> High-dimensional Covariates #> #> Long description: #> A classification (decision) tree is constructed from survival data with high-dimensional covariates. #> The method is a robust version of the logrank tree, where the variance is stabilized. #> The main function "uni.tree" returns a classification tree for a given survival dataset. #> The inner nodes (splitting criterion) are selected by minimizing the P-value of the two-sample the score tests. #> The decision of declaring terminal nodes (stopping criterion) is the P-value threshold given by an argument (specified by user). #> This tree construction algorithm is proposed by Emura et al. (2021, in review). #> #> Maintainer: Takeshi Emura <takeshiemura@gmail.com> #> #> #> Package weed #> #> Title: Wrangler for Emergency Events Database #> #> Long description: #> Makes research involving EMDAT and related datasets easier. These Datasets are manually filled and have several formatting and compatibility issues. Weed aims to resolve these with its functions. #> #> Maintainer: Ram Kripa <ram.m.kripa@berkeley.edu> #> #> #> Published on 2021-03-21 (40 packages): #> #> Package ASGS.foyer #> #> Title: Interface to the Australian Statistical Geography Standard #> #> Long description: #> The Australian Statistical Geography Standard ('ASGS') is #> a set of shapefiles by the Australian Bureau of Statistics. This package #> provides an interface to those shapefiles, as well as methods for converting #> coordinates to shapefiles. #> #> Maintainer: Hugh Parsonage <hugh.parsonage@gmail.com> #> #> #> Package asremlPlus #> #> Title: #> Augments 'ASReml-R' in Fitting Mixed Models and Packages #> Generally in Exploring Prediction Differences #> #> Long description: #> Assists in automating the selection of terms to include in mixed models when #> 'asreml' is used to fit the models. Also used to display, in tables and graphs, predictions #> obtained using any model fitting function and to explore differences between predictions. #> The content falls into the following natural groupings: (i) Data, (ii) Object #> manipulation functions, (iii) Model modification functions, (iv) Model testing functions, #> (v) Model diagnostics functions, (vi) Prediction production and presentation functions, #> (vii) Response transformation functions, and (viii) Miscellaneous functions (for further #> details see 'asremlPlus-package' in help). A history of #> the fitting of a sequence of models is kept in a data frame. Procedures are available for #> choosing models that conform to the hierarchy or marginality principle and for displaying #> predictions for significant terms in tables and graphs. The 'asreml' package provides a #> computationally efficient algorithm for fitting mixed models using Residual Maximum #> Likelihood. It is a commercial package that can be purchased from #> 'VSNi' <https://www.vsni.co.uk/> as 'asreml-R', who will supply a zip file for local #> installation/updating (see <https://asreml.kb.vsni.co.uk/>). It is not needed for functions that are #> methods for 'alldiffs' and 'data.frame' objects. The package 'asremPlus' can also be #> installed from <http://chris.brien.name/rpackages/>. #> #> Maintainer: Chris Brien <chris.brien@adelaide.edu.au> #> #> #> Package BBcor #> #> Title: Bayesian Bootstrapping Correlations #> #> Long description: #> Efficiently draw samples from the posterior distribution of various correlation coefficients #> with the Bayesian bootstrap described in Rubin (1981) <doi:10.1214/aos/1176345338>. #> There are six correlation coefficients, including #> Pearson, Kendall, Spearman, Gaussian Rank, Blomqvist, and polychoric. #> #> Maintainer: Donald Williams <drwwilliams@ucdavis.edu> #> #> #> Package benchmarkme #> #> Title: Crowd Sourced System Benchmarks #> #> Long description: #> Benchmark your CPU and compare against other CPUs. #> Also provides functions for obtaining system specifications, such as #> RAM, CPU type, and R version. #> #> Maintainer: Colin Gillespie <csgillespie@gmail.com> #> #> #> Package BET #> #> Title: Binary Expansion Testing #> #> Long description: #> Nonparametric detection of nonuniformity and dependence with Binary Expansion Testing (BET). See Kai Zhang (2019) BET on Independence, Journal of the American Statistical Association, 114:528, 1620-1637, <DOI:10.1080/01621459.2018.1537921> and Zhigen Zhao, Michael Baiocchi, Kai Zhang. SorBET: A Fast and Powerful Algorithm to Test Dependence of Variables. #> #> Maintainer: Wan Zhang <wanz63@live.unc.edu> #> #> #> Package BinOrdNonNor #> #> Title: Concurrent Generation of Binary, Ordinal and Continuous Data #> #> Long description: #> Generation of samples from a mix of binary, ordinal and continuous random variables with a pre-specified correlation matrix and marginal distributions. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>. #> #> Maintainer: Ran Gao <rgao8@uic.edu> #> #> #> Package BNPmix #> #> Title: Bayesian Nonparametric Mixture Models #> #> Long description: #> Functions to perform Bayesian nonparametric univariate and multivariate density estimation and clustering, by means of Pitman-Yor mixtures, and dependent Dirichlet process mixtures for partially exchangeable data. #> #> Maintainer: Riccardo Corradin <riccardo.corradin@gmail.com> #> #> #> Package CaPO4Sim #> #> Title: A Virtual Patient Simulator in the Context of Calcium and #> Phosphate Homeostasis #> #> Long description: #> Explore calcium (Ca) and phosphate (Pi) homeostasis with two novel 'Shiny' apps, #> building upon on a previously published mathematical model written in C, #> to ensure efficient computations. The underlying model is accessible #> here <https://pubmed.ncbi.nlm.nih.gov/28747359/)>. #> The first application explores the fundamentals of Ca-Pi homeostasis, #> while the second provides interactive case studies for in-depth exploration of the topic, #> thereby seeking to foster student engagement and an integrative understanding of Ca-Pi regulation. #> These applications are hosted at <https://rinterface.com/AppsPhysiol.html>. #> #> Maintainer: David Granjon <dgranjon@ymail.com> #> #> #> Package copent #> #> Title: Estimating Copula Entropy and Transfer Entropy #> #> Long description: #> The nonparametric methods for estimating copula entropy and transfer entropy are implemented. The method for estimating copula entropy composes of two simple steps: estimating empirical copula by rank statistic and estimating copula entropy with k-Nearest-Neighbour method. The method for estimating transfer entropy composes of two steps: estimating three copula entropy terms and then calculate transfer entropy from the estimated copula entropy terms. Copula Entropy is a mathematical concept for multivariate statistical independence measuring and testing, and proved to be equivalent to mutual information. Estimating copula entropy can be applied to many cases, including but not limited to variable selection and causal discovery (by estimating transfer entropy). Please refer to Ma and Sun (2011) <doi:10.1016/S1007-0214(11)70008-6> and Ma (2019) <arXiv:1910.04375> for more information. #> #> Maintainer: MA Jian <majian03@gmail.com> #> #> #> Package ctrdata #> #> Title: Retrieve and Analyze Clinical Trials in Public Registers #> #> Long description: #> Provides functions for querying, retrieving and analyzing #> protocol- and results-related information on clinical trials from #> two public registers, the 'European Union Clinical Trials Register' #> ('EUCTR', <https://www.clinicaltrialsregister.eu/>) and #> 'ClinicalTrials.gov' ('CTGOV', <https://clinicaltrials.gov/>). The #> trial information is transformed and stored in a database ('SQLite' #> or 'MongoDB', via 'nodbi'). Functions are provided to identify de- #> duplicated records, to easily find and extract variables (fields) #> of interest even from complex nesting as used by the registers, #> and to update previous queries that users retrieved in a database. #> The package can be used for meta analysis and trend-analysis of #> the design and conduct as well as results of clinical trials. #> #> Maintainer: Ralf Herold <ralf.herold@mailbox.org> #> #> #> Package DiPs #> #> Title: Directional Penalties for Optimal Matching in Observational #> Studies #> #> Long description: #> Improves the balance of optimal matching with near-fine balance by giving penalties on the unbalanced covariates with the unbalanced directions. Many directional penalties can also be viewed as Lagrange multipliers, pushing a matched sample in the direction of satisfying a linear constraint that would not be satisfied without penalization. #> Yu, R., and Rosenbaum, P. R. (2019). <doi:10.1111/biom.13098>. #> #> Maintainer: Ruoqi Yu <ruoqiyu@wharton.upenn.edu> #> #> #> Package dlnm #> #> Title: Distributed Lag Non-Linear Models #> #> Long description: Collection of functions for distributed lag linear and non-linear models. #> #> Maintainer: Antonio Gasparrini <antonio.gasparrini@lshtm.ac.uk> #> #> #> Package dlookr #> #> Title: Tools for Data Diagnosis, Exploration, Transformation #> #> Long description: #> A collection of tools that support data diagnosis, exploration, and transformation. #> Data diagnostics provides information and visualization of missing values and outliers and #> unique and negative values to help you understand the distribution and quality of your data. #> Data exploration provides information and visualization of the descriptive statistics of #> univariate variables, normality tests and outliers, correlation of two variables, and #> relationship between target variable and predictor. Data transformation supports binning #> for categorizing continuous variables, imputates missing values and outliers, resolving skewness. #> And it creates automated reports that support these three tasks. #> #> Maintainer: Choonghyun Ryu <choonghyun.ryu@gmail.com> #> #> #> Package emmeans #> #> Title: Estimated Marginal Means, aka Least-Squares Means #> #> Long description: #> Obtain estimated marginal means (EMMs) for many linear, generalized #> linear, and mixed models. Compute contrasts or linear functions of EMMs, #> trends, and comparisons of slopes. Plots and other displays. #> Least-squares means are discussed, and the term "estimated marginal means" #> is suggested, in Searle, Speed, and Milliken (1980) Population marginal means #> in the linear model: An alternative to least squares means, The American #> Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>. #> #> Maintainer: Russell V. Lenth <russell-lenth@uiowa.edu> #> #> #> Package excel.link #> #> Title: Convenient Data Exchange with Microsoft Excel #> #> Long description: #> Allows access to data in running instance of Microsoft Excel #> (e. g. 'xl[a1] = xl[b2]*3' and so on). Graphics can be transferred with #> 'xl[a1] = current.graphics()'. Additionally there are function for reading/writing #> 'Excel' files - 'xl.read.file'/'xl.save.file'. They are not fast but able to read/write #> '*.xlsb'-files and password-protected files. There is an Excel workbook with #> examples of calling R from Excel in the 'doc' folder. It tries to keep things as #> simple as possible - there are no needs in any additional #> installations besides R, only 'VBA' code in the Excel workbook. #> Microsoft Excel is required for this package. #> #> Maintainer: Gregory Demin <excel.link.feedback@gmail.com> #> #> #> Package GET #> #> Title: Global Envelopes #> #> Long description: #> Implementation of global envelopes for a set of general d-dimensional vectors T #> in various applications. A 100(1-alpha)% global envelope is a band bounded by two #> vectors such that the probability that T falls outside this envelope in any of the d #> points is equal to alpha. Global means that the probability is controlled simultaneously #> for all the d elements of the vectors. The global envelopes can be used for graphical #> Monte Carlo and permutation tests where the test statistic is a multivariate vector or #> function (e.g. goodness-of-fit testing for point patterns and random sets, functional #> analysis of variance, functional general linear model, n-sample test of correspondence #> of distribution functions), for central regions of functional or multivariate data (e.g. #> outlier detection, functional boxplot) and for global confidence and prediction bands #> (e.g. confidence band in polynomial regression, Bayesian posterior prediction). See #> Myllymäki and Mrkvička (2020) <arXiv:1911.06583>, #> Myllymäki et al. (2017) <doi: 10.1111/rssb.12172>, #> Mrkvička et al. (2017) <doi: 10.1007/s11222-016-9683-9>, #> Mrkvička et al. (2020) <doi: 10.14736/kyb-2020-3-0432>, #> Mrkvička et al. (2019) <doi: 10.1007/s11009-019-09756-y>, #> Mrkvička et al. (2019) <arXiv:1902.04926>, #> Mrkvička et al. (2016) <doi: 10.1016/j.spasta.2016.04.005>, and #> Myllymäki et al. (2020) <doi: 10.1016/j.spasta.2020.100436>. #> #> Maintainer: Mari Myllymäki <mari.myllymaki@luke.fi> #> #> #> Package GSODR #> #> Title: Global Surface Summary of the Day ('GSOD') Weather Data Client #> #> Long description: #> Provides automated downloading, parsing, cleaning, unit conversion #> and formatting of Global Surface Summary of the Day ('GSOD') weather data #> from the from the USA National Centers for Environmental Information #> ('NCEI'). Units are converted from from United States Customary System #> ('USCS') units to International System of Units ('SI'). Stations may be #> individually checked for number of missing days defined by the user, where #> stations with too many missing observations are omitted. Only stations with #> valid reported latitude and longitude values are permitted in the final #> data. Additional useful elements, saturation vapour pressure ('es'), actual #> vapour pressure ('ea') and relative humidity ('RH') are calculated from the #> original data using the improved August-Roche-Magnus approximation (Alduchov #> & Eskridge 1996) and included in the final data set. The resulting metadata #> include station identification information, country, state, latitude, #> longitude, elevation, weather observations and associated flags. For #> information on the 'GSOD' data from 'NCEI', please see the 'GSOD' #> 'readme.txt' file available from, #> <https://www1.ncdc.noaa.gov/pub/data/gsod/readme.txt>. #> #> Maintainer: Adam H. Sparks <adamhsparks@gmail.com> #> #> #> Package hydroscoper #> #> Title: Interface to the Greek National Data Bank for #> Hydrometeorological Information #> #> Long description: #> R interface to the Greek National Data Bank for Hydrological and #> Meteorological Information. It covers #> Hydroscope's data sources and provides functions to transliterate, #> translate and download them into tidy dataframes. #> #> Maintainer: Konstantinos Vantas <kon.vantas@gmail.com> #> #> #> Package lifecontingencies #> #> Title: Financial and Actuarial Mathematics for Life Contingencies #> #> Long description: #> Classes and methods that allow the user to manage life table, #> actuarial tables (also multiple decrements tables). Moreover, functions to easily #> perform demographic, financial and actuarial mathematics on life contingencies #> insurances calculations are contained therein. See Spedicato (2013) <doi:10.18637/jss.v055.i10>. #> #> Maintainer: Giorgio Alfredo Spedicato <spedicato_giorgio@yahoo.it> #> #> #> Package lightr #> #> Title: Read Spectrometric Data and Metadata #> #> Long description: #> Parse various reflectance/transmittance/absorbance spectra file #> formats to extract spectral data and metadata, as described in Gruson, White #> & Maia (2019) <doi:10.21105/joss.01857>. Among other formats, it can import #> files from 'Avantes' <https://www.avantes.com/>, 'CRAIC' #> <https://www.microspectra.com/>, and 'OceanInsight' (formerly 'OceanOptics') #> <https://www.oceaninsight.com/> brands. #> #> Maintainer: Hugo Gruson <hugo.gruson+R@normalesup.org> #> #> #> Package loon.ggplot #> #> Title: Making 'ggplot2' Plots Interactive with 'loon' and Vice Versa #> #> Long description: #> It provides a bridge between the 'loon' and 'ggplot2' packages. Data analysts who value the grammar pipeline provided by 'ggplot2' can turn these static plots into interactive 'loon' plots. Conversely, data analysts who explore data interactively with 'loon' can turn any 'loon' plot into a 'ggplot2' plot structure. The function 'loon.ggplot()' is applied to one plot structure will return the other. #> #> Maintainer: Zehao Xu <z267xu@uwaterloo.ca> #> #> #> Package mizer #> #> Title: Multi-Species sIZE Spectrum Modelling in R #> #> Long description: #> A set of classes and methods to set up and run multi-species, trait #> based and community size spectrum ecological models, focused on the marine #> environment. #> #> Maintainer: Gustav Delius <gustav.delius@york.ac.uk> #> #> #> Package Patterns #> #> Title: Deciphering Biological Networks with Patterned Heterogeneous #> Measurements #> #> Long description: #> A modeling tool dedicated to biological network modeling (Bertrand and others 2020, <doi:10.1093/bioinformatics/btaa855>). It allows for single or joint modeling of, for instance, genes and proteins. It starts with the selection of the actors that will be the used in the reverse engineering upcoming step. An actor can be included in that selection based on its differential measurement (for instance gene expression or protein abundance) or on its time course profile. Wrappers for actors clustering functions and cluster analysis are provided. It also allows reverse engineering of biological networks taking into account the observed time course patterns of the actors. Many inference functions are provided and dedicated to get specific features for the inferred network such as sparsity, robust links, high confidence links or stable through resampling links. Some simulation and prediction tools are also available for cascade networks (Jung and others 2014, <doi:10.1093/bioinformatics/btt705>). Example of use with microarray or RNA-Seq data are provided. #> #> Maintainer: Frederic Bertrand <frederic.bertrand@math.unistra.fr> #> #> #> Package pchc #> #> Title: Bayesian Network Learning with the PCHC and Related Algorithms #> #> Long description: #> Bayesian network learning using the PCHC algorithm. PCHC stands for PC Hill-Climbing. It is a new hybrid algorithm that used PC to construct the skeleton of the BN and then #> utilizes the Hill-Climbing greedy search. More algorithms and variants have been added, such as MMHC, FEDHC, and the Tabu search variants, PCTABU, MMTABU and FEDTABU. The relevant papers are #> a) Tsagris M. (2021). A new scalable Bayesian network learning algorithm with applications to economics. Computational Economics, 57(1): 341-367. <doi:10.1007/s10614-020-10065-7>. #> b) Tsagris M. (2020). The FEDHC Bayesian network learning algorithm. <arXiv:2012.00113>. #> #> Maintainer: Michail Tsagris <mtsagris@uoc.gr> #> #> #> Package PDFEstimator #> #> Title: Nonparametric Probability Density Estimator #> #> Long description: #> Farmer, J., D. Jacobs (2108) <DOI:10.1371/journal.pone.0196937>. A nonparametric density estimator based on the maximum-entropy method. Accurately predicts a probability density function (PDF) for random data using a novel iterative scoring function to determine the best fit without overfitting to the sample. #> #> Maintainer: Jenny Farmer <jfarmer6@uncc.edu> #> #> #> Package PoisBinOrdNor #> #> Title: Data Generation with Poisson, Binary, Ordinal and Normal #> Components #> #> Long description: #> Generation of multiple count, binary, ordinal and normal variables simultaneously given the marginal characteristics and association structure. #> The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>. #> #> Maintainer: Ran Gao <rgao8@uic.edu> #> #> #> Package PoisNonNor #> #> Title: Simultaneous Generation of Count and Continuous Data #> #> Long description: #> Generation of count (assuming Poisson distribution) and continuous data (using Fleishman polynomials) simultaneously. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>. #> #> Maintainer: Ran Gao <rgao8@uic.edu> #> #> #> Package PoisNor #> #> Title: Simultaneous Generation of Multivariate Data with Poisson and #> Normal Marginals #> #> Long description: #> Generates multivariate data with count and continuous variables with a pre-specified correlation matrix. The count and continuous variables are assumed to have Poisson and normal marginals, respectively. The data generation mechanism is a combination of the normal to anything principle and a connection between Poisson and normal correlations in the mixture. #> The details of the method are explained in Yahav et al. (2012) <DOI:10.1002/asmb.901>. #> #> Maintainer: Ran Gao <rgao8@uic.edu> #> #> #> Package poppr #> #> Title: Genetic Analysis of Populations with Mixed Reproduction #> #> Long description: #> Population genetic analyses for hierarchical analysis of partially #> clonal populations built upon the architecture of the 'adegenet' package. #> Originally described in Kamvar, Tabima, and Grünwald (2014) #> <doi:10.7717/peerj.281> with version 2.0 described in Kamvar, Brooks, and #> Grünwald (2015) <doi:10.3389/fgene.2015.00208>. #> #> Maintainer: Zhian N. Kamvar <zkamvar@gmail.com> #> #> #> Package pricesensitivitymeter #> #> Title: Van Westendorp Price Sensitivity Meter Analysis #> #> Long description: #> An implementation of the van Westendorp Price #> Sensitivity Meter in R, which is a survey-based approach #> to analyze consumer price preferences and sensitivity #> (van Westendorp 1976, isbn:9789283100386). #> #> Maintainer: Max Alletsee <max.alletsee@gmail.com> #> #> #> Package ROpenCVLite #> #> Title: Helper Package for Installing OpenCV with R #> #> Long description: #> Installs 'OpenCV' for use by other packages. 'OpenCV' <https://opencv.org/> #> is library of programming functions mainly aimed at real-time computer #> vision. This 'Lite' version contains the stable base version of 'OpenCV' and #> does not contain any of its externally contributed modules. #> #> Maintainer: Simon Garnier <garnier@njit.edu> #> #> #> Package RxODE #> #> Title: Facilities for Simulating from ODE-Based Models #> #> Long description: #> Facilities for running simulations from ordinary #> differential equation ('ODE') models, such as pharmacometrics and other #> compartmental models. A compilation manager translates the ODE model #> into C, compiles it, and dynamically loads the object code into R for #> improved computational efficiency. An event table object facilitates #> the specification of complex dosing regimens (optional) and sampling #> schedules. NB: The use of this package requires both C and #> Fortran compilers, for details on their use with R please see #> Section 6.3, Appendix A, and Appendix D in the "R Administration and #> Installation" manual. Also the code is mostly released under GPL. The #> 'VODE' and 'LSODA' are in the public domain. The information is available #> in the inst/COPYRIGHTS. #> #> Maintainer: Wenping Wang <wwang8198@gmail.com> #> #> #> Package scMappR #> #> Title: Single Cell Mapper #> #> Long description: #> The single cell mapper (scMappR) R package contains a suite of bioinformatic tools that provide experimentally relevant cell-type specific information to a list of differentially expressed genes (DEG). The function "scMappR_and_pathway_analysis" reranks DEGs to generate cell-type specificity scores called cell-weighted fold-changes. Users input a list of DEGs, normalized counts, and a signature matrix into this function. scMappR then re-weights bulk DEGs by cell-type specific expression from the signature matrix, cell-type proportions from RNA-seq deconvolution and the ratio of cell-type proportions between the two conditions to account for changes in cell-type proportion. With cwFold-changes calculated, scMappR uses two approaches to utilize cwFold-changes to complete cell-type specific pathway analysis. The "process_dgTMatrix_lists" function in the scMappR package contains an automated scRNA-seq processing pipeline where users input scRNA-seq count data, which is made compatible for scMappR and other R packages that analyze scRNA-seq data. We further used this to store hundreds up regularly updating signature matrices. The functions "tissue_by_celltype_enrichment", "tissue_scMappR_internal", and "tissue_scMappR_custom" combine these consistently processed scRNAseq count data with gene-set enrichment tools to allow for cell-type marker enrichment of a generic gene list (e.g. GWAS hits). Reference: Sokolowski,D.J., Faykoo-Martinez,M., Erdman,L., Hou,H., Chan,C., Zhu,H., Holmes,M.M., Goldenberg,A. and Wilson,M.D. (2021) Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes. NAR Genomics and Bioinformatics. 3(1). Iqab011. <doi:10.1093/nargab/lqab011>. #> #> Maintainer: Dustin Sokolowski <dustin.sokolowski@sickkids.ca> #> #> #> Package smoothedLasso #> #> Title: A Framework to Smooth L1 Penalized Regression Operators using #> Nesterov Smoothing #> #> Long description: #> We provide full functionality to smooth L1 penalized regression operators and to compute regression estimates thereof. For this, the objective function of a user-specified regression operator is first smoothed using Nesterov smoothing (see Y. Nesterov (2005) <doi:10.1007/s10107-004-0552-5>), resulting in a modified objective function with explicit gradients everywhere. The smoothed objective function and its gradient are minimized via BFGS, and the obtained minimizer is returned. Using Nesterov smoothing, the smoothed objective function can be made arbitrarily close to the original (unsmoothed) one. In particular, the Nesterov approach has the advantage that it comes with explicit accuracy bounds, both on the L1/L2 difference of the unsmoothed to the smoothed objective functions as well as on their respective minimizers (see G. Hahn, S.M. Lutz, N. Laha, C. Lange (2020) <doi:10.1101/2020.09.17.301788>). A progressive smoothing approach is provided which iteratively smoothes the objective function, resulting in more stable regression estimates. A function to perform cross validation for selection of the regularization parameter is provided. #> #> Maintainer: Georg Hahn <ghahn@hsph.harvard.edu> #> #> #> Package spatstat.data #> #> Title: Datasets for 'spatstat' Family #> #> Long description: Contains all the datasets for the 'spatstat' family of packages. #> #> Maintainer: Adrian Baddeley <Adrian.Baddeley@curtin.edu.au> #> #> #> Package tableHTML #> #> Title: A Tool to Create HTML Tables #> #> Long description: #> A tool to create and style HTML tables with CSS. These can #> be exported and used in any application that accepts HTML (e.g. 'shiny', #> 'rmarkdown', 'PowerPoint'). It also provides functions to create CSS files #> (which also work with shiny). #> #> Maintainer: Theo Boutaris <teoboot2007@hotmail.com> #> #> #> Package this.path #> #> Title: #> Get Executing Script's Path, from 'RStudio', 'RGui', 'Rterm' and #> 'Rscript' (Command-Line / / Terminal), and When Using 'source' #> #> Long description: #> Determine the full path of the executing script. Works when running #> a line or selection from an open R script in 'RStudio' and 'RGui', when #> using 'source' and 'sys.source' and 'debugSource' ('RStudio' exclusive) and #> 'testthat::source_file', and when running R from the Windows command-line / #> / Unix terminal. #> #> Maintainer: Andrew Simmons <akwsimmo@gmail.com> #> #> #> Package ttbary #> #> Title: Barycenter Methods for Spatial Point Patterns #> #> Long description: #> Computes a point pattern in R^2 or on a graph that is representative of a collection of many data patterns. The result is an approximate barycenter (also known as Fréchet mean or prototype) based on a transport-transform metric. Possible choices include Optimal SubPattern Assignment (OSPA) and Spike Time metrics. Details can be found in Müller, Schuhmacher and Mateu (2020) <doi:10.1007/s11222-020-09932-y>. #> #> Maintainer: Dominic Schuhmacher <dominic.schuhmacher@mathematik.uni-goettingen.de> #> #> #> Package twn #> #> Title: Taxa Waterbeheer Nederland voor R #> #> Long description: #> The TWN-list (Taxa Waterbeheer Nederland) is the Dutch standard for naming #> taxons in Dutch Watermanagement. This package makes it easier to use the #> TWN-list for ecological analyses. It consists of two parts. First it makes the #> TWN-list itself available in R. Second, it has a few functions that make it #> easy to perform some basic and often recurring tasks for checking and consulting #> taxonomic data from the TWN-list. #> #> Maintainer: Johan van Tent <tentvanjohan@hotmail.com> #> #> #> Package USgrid #> #> Title: The Demand and Supply for Electricity in the US #> #> Long description: #> Provides a set of regular time-series datasets, describing the US electricity grid. That includes the total demand and supply, and as well as the demand by energy source (coal, solar, wind, etc.). Source: US Energy Information Administration (Dec 2019) <https://www.eia.gov/>. #> #> Maintainer: Rami Krispin <rami.krispin@gmail.com> #># }