Helper function to install packages for R using an external requirements.txt or a string containing diverse packages from several resources like Github or CRAN.

This package provides a programmatic interface to web-services of YouTheria. YouTheria is an online database of mammalian trait data <http://www.utheria.org/>.

This package provides an accessible and efficient implementation of a randomized feature and bootstrap-enhanced Gaussian naive Bayes classifier. The method combines stratified bootstrap resampling with random feature subsampling and aggregates predictions via posterior averaging. Support is provided for mixed-type predictors and parallel computation. Methods are described in Srisuradetchai (2025) <doi:10.3389/fdata.2025.1706417> "Posterior averaging with Gaussian naive Bayes and the R package RandomGaussianNB for big-data classification".

Offers a handful of useful wrapper functions which streamline the reading, analyzing, and visualizing of variant call format (vcf) files in R. This package was designed to facilitate an explicit pipeline for optimizing Stacks (Rochette et al., 2019) (<doi:10.1111/mec.15253>) parameters during de novo (without a reference genome) assembly and variant calling of restriction-enzyme associated DNA sequence (RADseq) data. The pipeline implemented here is based on the 2017 paper "Lost in Parameter Space" (Paris et al., 2017) (<doi:10.1111/2041-210X.12775>) which establishes clear recommendations for optimizing the parameters m', M', and n', during the process of assembling loci.

Generate utils::globalVariables() from roxygen2 @global and @autoglobal tags.

Invoke a BUGS model in OpenBUGS or WinBUGS', a class "bugs" for BUGS results and functions to work with that class. Function write.model() allows a BUGS model file to be written. The class and auxiliary functions could be used with other MCMC programs, including JAGS'. The suggested package BRugs (only needed for function openbugs()) is only available from the CRAN archives, see <https://cran.r-project.org/package=BRugs>.

This package provides a robust and powerful approach is developed for replicability analysis of two Genome-wide association studies (GWASs) accounting for the linkage disequilibrium (LD) among genetic variants. The LD structure in two GWASs is captured by a four-state hidden Markov model (HMM). The unknowns involved in the HMM are estimated by an efficient expectation-maximization (EM) algorithm in combination with a non-parametric estimation of functions. By incorporating information from adjacent locations via the HMM, this approach identifies the entire clusters of genotype-phenotype associated signals, improving the power of replicability analysis while effectively controlling the false discovery rate.

Implementation of robust sparse PCA using the ROSPCA algorithm of Hubert et al. (2016) <DOI:10.1080/00401706.2015.1093962>.

This package contains logic for sample-level variable set scoring using randomized reduced rank reconstruction error. Frost, H. Robert (2023) "Reconstruction Set Test (RESET): a computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error" <doi:10.1101/2023.04.03.535366>.

Linear regression functions using Huber and bisquare psi functions. Optimal weights are calculated using IRLS algorithm.

This package provides functions to generate response-surface designs, fit first- and second-order response-surface models, make surface plots, obtain the path of steepest ascent, and do canonical analysis. A good reference on these methods is Chapter 10 of Wu, C-F J and Hamada, M (2009) "Experiments: Planning, Analysis, and Parameter Design Optimization" ISBN 978-0-471-69946-0. An early version of the package is documented in Journal of Statistical Software <doi:10.18637/jss.v032.i07>.

The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The rdpower package provides tools to perform power, sample size and MDE calculations in RD designs: rdpower() calculates the power of an RD design, rdsampsi() calculates the required sample size to achieve a desired power and rdmde() calculates minimum detectable effects. See Cattaneo, Titiunik and Vazquez-Bare (2019) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2019_Stata.pdf> for further methodological details.

After defining an R6 class, R62S3 is used to automatically generate optional S3/S4 generics and methods for dispatch. Also allows piping for R6 objects.

Parser for SQL statements. Currently, it supports parsing of only SELECT statements.

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.

Relational Class Analysis (RCA) is a method for detecting heterogeneity in attitudinal data (as described in Goldberg A., 2011, Am. J. Soc, 116(5)).

Calculates intra-regional and inter-regional similarities based on user-provided spatial vector objects (regions) and spatial raster objects (cells with values). Implemented metrics include inhomogeneity, isolation (Haralick and Shapiro (1985) <doi:10.1016/S0734-189X(85)90153-7>, Jasiewicz et al. (2018) <doi:10.1016/j.cageo.2018.06.003>), and distinction (Nowosad (2021) <doi:10.1080/13658816.2021.1893324>).

Easily Download Analysis-Ready Crash Data from the U.S. National Highway Traffic Safety Administration.

This package provides robust methods to detect change-points in uni- or multivariate time series. They can cope with corrupted data and heavy tails. Focus is on the detection of abrupt changes in location, but changes in the scale or dependence structure can be detected as well. This package provides tests for change detection in uni- and multivariate time series based on Huberized versions of CUSUM tests proposed in Duerre and Fried (2019) <DOI:10.48550/arXiv.1905.06201>, and tests for change detection in univariate time series based on 2-sample U-statistics or 2-sample U-quantiles as proposed by Dehling et al. (2015) <DOI:10.1007/978-1-4939-3076-0_12> and Dehling, Fried and Wendler (2020) <DOI:10.1093/biomet/asaa004>. Furthermore, the packages provides tests on changes in the scale or the correlation as proposed in Gerstenberger, Vogel and Wendler (2020) <DOI:10.1080/01621459.2019.1629938>, Dehling et al. (2017) <DOI:10.1017/S026646661600044X>, and Wied et al. (2014) <DOI:10.1016/j.csda.2013.03.005>.

Enhances the R Optimization Infrastructure ('ROI') package with the alabama solver for solving nonlinear optimization problems.

Enhances the R Optimization Infrastructure ('ROI') package by registering the ipop solver from package kernlab'.

Client for the web service methods provided by DataCite (<https://www.datacite.org/>), including functions to interface with their RESTful search API. The API is backed by Elasticsearch', allowing expressive queries, including faceting.

STK++ <http://www.stkpp.org> is a collection of C++ classes for statistics, clustering, linear algebra, arrays (with an Eigen'-like API), regression, dimension reduction, etc. The integration of the library to R is using Rcpp'. The rtkore package includes the header files from the STK++ core library. All files contain only template classes and/or inline functions. STK++ is licensed under the GNU LGPL version 2 or later. rtkore (the stkpp integration into R') is licensed under the GNU GPL version 2 or later. See file LICENSE.note for details.

This package provides 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 <https://rcompanion.org>.