Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis.

The package is an R wrapper for Progenetix REST API built upon the Beacon v2 protocol. Its purpose is to provide a seamless way for retrieving genomic data from Progenetix database—an open resource dedicated to curated oncogenomic profiles. Empowered by this package, users can effortlessly access and visualize data from Progenetix.

This package provides a client for the gypsum REST API (https://gypsum.artifactdb.com), a cloud-based file store in the ArtifactDB ecosystem. This package provides functions for uploads, downloads, and various administrative and management tasks. Check out the documentation at https://github.com/ArtifactDB/gypsum-worker for more details.

BAnOCC is a package designed for compositional data, where each sample sums to one. It infers the approximate covariance of the unconstrained data using a Bayesian model coded with rstan. It provides as output the stanfit object as well as posterior median and credible interval estimates for each correlation element.

This package is designed to be used with Rscript to write shebang scripts that accept short and long options. Many users will prefer to use the packages optparse or argparse which add extra features like automatically generated help options and usage texts, support for default values, positional argument support, etc.

This package provides tools for calculating the Delaunay triangulation and the Dirichlet or Voronoi tessellation (with respect to the entire plane) of a planar point set. It plots triangulations and tessellations in various ways, clips tessellations to sub-windows, calculates perimeters of tessellations, and summarizes information about the tiles of the tessellation.

This package provides a dplyr back end for databases that allows you to work with remote database tables as if they are in-memory data frames. Basic features works with any database that has a DBI back end; more advanced features require SQL translation to be provided by the package author.

The grammar of graphics as implemented in ggplot2 is a poor fit for graph and network visualizations due to its reliance on tabular data input. The ggraph package is an extension of the ggplot2 API tailored to graph visualizations and provides the same flexible approach to building up plots layer by layer.

This package provides a collection of miscellaneous statistical functions for:

• probability distributions,

• probability density estimation,

• most frequent value estimation,

• other statistical measures of location,

• construction of histograms,

• calculation of the Hellinger distance,

• use of classical kernels, and

• univariate piecewise-constant regression.

This package provides a set of handy functions. It includes a versatile one line progress bar, one line function timer with detailed output, time delay function, text histogram, object preview, CRAN package search, simpler package installer, Linux command install check, a flexible Mode function, top function, simulation of correlated data, and more.

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.

An interface between the GRASS geographical information system ('GIS') and R', based on starting R from within the GRASS GIS environment, or running a free-standing R session in a temporary GRASS location; the package provides facilities for using all GRASS commands from the R command line. The original interface package for GRASS 5 (2000-2010) is described in Bivand (2000) <doi:10.1016/S0098-3004(00)00057-1> and Bivand (2001) <https://www.r-project.org/conferences/DSC-2001/Proceedings/Bivand.pdf>. This was succeeded by spgrass6 for GRASS 6 (2006-2016) and rgrass7 for GRASS 7 (2015-present). The rgrass package modernizes the interface for GRASS 8 while still permitting the use of GRASS 7'.

Allows the user to connect with the World Spider Catalogue (WSC; <https://wsc.nmbe.ch/>) and the World Spider Trait (WST; <https://spidertraits.sci.muni.cz/>) databases. Also performs several basic functions such as checking names validity, retrieving coordinate data from the Global Biodiversity Information Facility (GBIF; <https://www.gbif.org/>), and mapping.

This package contains a shiny application called AdEPro (Animation of Adverse Event Profiles) which (audio-)visualizes adverse events occurring in clinical trials. As this data is usually considered sensitive, this tool is provided as a stand-alone application that can be launched from any local machine on which the data is stored.

This package provides easy installation and loading of core ArcGIS location services packages arcgislayers', arcgisutils', arcgisgeocode', and arcgisplaces'. Enabling developers to interact with spatial data and services from ArcGIS Online', ArcGIS Enterprise', and ArcGIS Platform'. Learn more about the arcgis meta-package at <https://developers.arcgis.com/r-bridge/>.

This package provides a cross-platform representation of models as sets of equations that facilitates modularity in model building and allows users to harness modern techniques for numerical integration and data visualization. Documentation is provided by several vignettes included in this package; also see Lochocki et al. (2022) <doi:10.1093/insilicoplants/diac003>.

Allows the reenactment of the R programs used in the book Bayesian Essentials with R without further programming. R code being available as well, they can be modified by the user to conduct one's own simulations. Marin J.-M. and Robert C. P. (2014) <doi:10.1007/978-1-4614-8687-9>.

This package provides functions for performing experimental comparisons of algorithms using adequate sample sizes for power and accuracy. Implements the methodology originally presented in Campelo and Takahashi (2019) <doi:10.1007/s10732-018-9396-7> for the comparison of two algorithms, and later generalised in Campelo and Wanner (Submitted, 2019) <arxiv:1908.01720>.

This package implements common measures of diversity and spatial segregation. This package has tools to compute the majority of measures are reviewed in Massey and Denton (1988) <doi:10.2307/2579183>. Multiple common measures of within-geography diversity are implemented as well. All functions operate on data frames with a tidyselect based workflow.

Companion package to the paper: An analytic approach for interpretable predictive models in high dimensional data, in the presence of interactions with exposures. Bhatnagar, Yang, Khundrakpam, Evans, Blanchette, Bouchard, Greenwood (2017) <DOI:10.1101/102475>. This package includes an algorithm for clustering high dimensional data that can be affected by an environmental factor.

This package provides a program to generate smoothed quantiles for the Fst-heterozygosity distribution. Designed for use with large numbers of loci (e.g., genome-wide SNPs). The best case for analyzing the Fst-heterozygosity distribution is when many populations (>10) have been sampled. See Flanagan & Jones (2017) <doi:10.1093/jhered/esx048>.

This package provides functions for creating flashcard decks of terms and definitions. This package creates HTML slides using revealjs that can be viewed in the RStudio viewer or a web browser. Users can create flashcards from either existing built-in decks or create their own from CSV files or vectors of function names.

Extremely efficient procedures for fitting regularization path with l0, l1, and truncated lasso penalty for linear regression and logistic regression models. This version is a completely new version compared with our previous version, which was mainly based on R. New core algorithms are developed and are now written in C++ and highly optimized.

We provides functions that employ splines to estimate generalized partially linear single index models (GPLSIM), which extend the generalized linear models to include nonlinear effect for some predictors. Please see Y. (2017) at <doi:10.1007/s11222-016-9639-0> and Y., and R. (2002) at <doi:10.1198/016214502388618861> for more details.