This package provides a replication of key functionality from dplyr and the wider tidyverse using only base.

This package provides tools to estimate tail area-based false discovery rates as well as local false discovery rates for a variety of null models (p-values, z-scores, correlation coefficients, t-scores). The proportion of null values and the parameters of the null distribution are adaptively estimated from the data. In addition, the package contains functions for non-parametric density estimation (Grenander estimator), for monotone regression (isotonic regression and antitonic regression with weights), for computing the greatest convex minorant (GCM) and the least concave majorant (LCM), for the half-normal and correlation distributions, and for computing empirical higher criticism (HC) scores and the corresponding decision threshold.

This package provides a quantitative financial modelling framework to allow users to specify, build, trade, and analyse quantitative financial trading strategies.

This package provides a stepwise approach to identifying recombination breakpoints in a genomic sequence alignment.

This package provides tools to access and manipulate Word and PowerPoint documents from R. The package focuses on tabular and graphical reporting from R; it also provides two functions that let users get document content into data objects. A set of functions lets add and remove images, tables and paragraphs of text in new or existing documents. When working with PowerPoint presentations, slides can be added or removed; shapes inside slides can also be added or removed. When working with Word documents, a cursor can be used to help insert or delete content at a specific location in the document.

This package OrgMassSpecR is an extension of the R statistical computing language. It contains functions to assist with organic or biological mass spectrometry data analysis. Mass spectral libraries are available as companion packages.

This package provides an R client for jq, a JSON processor. jq allows the following with JSON data: index into, parse, do calculations, cut up and filter, change key names and values, perform conditionals and comparisons, and more.

This package provides an implementation of efficient approximate leave-one-out (LOO) cross-validation for Bayesian models fit using Markov chain Monte Carlo, as described in doi:10.1007/s11222-016-9696-4. The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of the calculations, we also obtain approximate standard errors for estimated predictive errors and for the comparison of predictive errors between models. The package also provides methods for using stacking and other model weighting techniques to average Bayesian predictive distributions.

This package provides R bindings to the Sundown Markdown rendering library (https://github.com/vmg/sundown). Markdown is a plain-text formatting syntax that can be converted to XHTML or other formats.

This package is a collection of several algorithms to obtain archetypoids with small and large databases and with both classical multivariate data and functional data (univariate and multivariate). Some of these algorithms also detect anomalies (outliers).

This package provides Wiener process distribution functions, namely the Wiener first passage time density, CDF, quantile and random functions. It additionally supplies a modelling function (wdm) and further methods for the resulting object.

This package implements the RUV (Remove Unwanted Variation) algorithms. These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data. The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well. The algorithms require the user to specify a set of negative control variables, as described in the references. The algorithms included in this package are RUV-2, RUV-4, RUV-inv, RUV-rinv, RUV-I, and RUV-III, along with various supporting algorithms.

This package implements the Differential Evolution algorithm. This algorithm is used for the global optimization of a real-valued function of a real-valued parameter vector. The implementation of DifferentialEvolution in DEoptim interfaces with C code for efficiency.

This package contains all the datasets for the spatstat package.

There are a number of binary files associated with the Webdriver/Selenium project (see http://www.seleniumhq.org/download/, https://sites.google.com/a/chromium.org/chromedriver/, https://github.com/mozilla/geckodriver, http://phantomjs.org/download.html, and https://github.com/SeleniumHQ/selenium/wiki/InternetExplorerDriver for more information). This package provides functions to download these binaries and to manage processes involving them.

This package generates graphics with embedded details from statistical tests. Statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous or categorical data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses.

This package provides data structures and basic operations for ordinary sets, generalizations such as fuzzy sets, multisets, and fuzzy multisets, customizable sets, and intervals.

This package provides a collection of functions useful in learning and practicing Item Response Theory (IRT), which can be combined into larger programs. It provides basic CTT analysis, a simple common interface to the estimation of item parameters in IRT models for binary responses with three different programs (ICL, BILOG-MG, and ltm), ability estimation (MLE, BME, EAP, WLE, plausible values), item and person fit statistics, scaling methods (MM, MS, Stocking-Lord, and the complete Hebaera method), and a rich array of parametric and non-parametric (kernel) plots. It estimates and plots Haberman's interaction model when all items are dichotomously scored.

This package provides an interface to Amazon Web Services customer engagement services, including Simple Email Service, Connect contact center service, and more.

This package provides functionality for client-side navigation of the server side file system in shiny apps. In case the app is running locally this gives the user direct access to the file system without the need to "download" files to a temporary location. Both file and folder selection as well as file saving is available.

This is a package for fast and user-friendly estimation of econometric models with multiple fixed-effects. It includes ordinary least squares (OLS), generalized linear models (GLM) and the negative binomial. The core of the package is based on optimized parallel C++ code, scaling especially well for large data sets. The method to obtain the fixed-effects coefficients is based on Berge (2018). It further provides tools to export and view the results of several estimations with intuitive design to cluster the standard-errors.

This package provides a unified pipeline to clean, prepare, plot, and run basic analyses on pupillometry experiments.

This package provides various tools for developers of R packages interfacing with Stan, including functions to set up the required package structure, S3 generics and default methods to unify function naming across Stan-based R packages, and vignettes with recommendations for developers.

This is a package for computation and visualization of the empirical attainment function (EAF) for the analysis of random sets in multi-criterion optimization.