This package provides implementations of apply(), eapply(), lapply(), Map(), mapply(), replicate(), sapply(), tapply(), and vapply() that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.

This package allows for data objects in R to be rendered as HTML tables using the JavaScript library DataTables (typically via R Markdown or Shiny). The DataTables library has been included in this R package.

This package provides an interface to the C code for Latent Dirichlet Allocation (LDA) models and Correlated Topics Models (CTM) by David M. Blei and co-authors and the C++ code for fitting LDA models using Gibbs sampling by Xuan-Hieu Phan and co-authors.

This package provides functionality for creating Quantile-Quantile (QQ) and Probability-Probability (PP) plots with simultaneous testing bands to asses significance of sample deviation from a reference distribution.

This package provides functions to manipulate binary fingerprints of arbitrary length. A fingerprint is represented by an object of S4 class fingerprint. The bitwise logical functions in R are overridden so that they can be used directly with fingerprint objects. A number of distance metrics are also available. Fingerprints can be converted to Euclidean vectors (i.e., points on the unit hypersphere) and can also be folded. Arbitrary fingerprint formats can be handled via line handlers. Currently handlers are provided for CDK, MOE and BCI fingerprint data.

Kernel factory is an ensemble method where each base classifier (random forest) is fit on the kernel matrix of a subset of the training data.

This package provides a collection of convenient functions for common statistical computations, which are not directly provided by R's base or stats packages. This package aims at providing, first, shortcuts for statistical measures, which otherwise could only be calculated with additional effort. Second, these shortcut functions are generic, and can be applied not only to vectors, but also to other objects as well. The focus of most functions lies on summary statistics or fit measures for regression models, including generalized linear models, mixed effects models and Bayesian models.

The Structural Topic Model (STM) allows researchers to estimate topic models with document-level covariates. The package also includes tools for model selection, visualization, and estimation of topic-covariate regressions.

This package provides methods for species distribution modeling, i.e., predicting the environmental similarity of any site to that of the locations of known occurrences of a species.

This package implements a general framework for finite mixtures of regression models using the EM algorithm. FlexMix provides the E-step and all data handling, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering.

This package provides David Scott's ASH routines ported from S-PLUS to R.

Tools for integrating spatially-misaligned GIS datasets. Part of the Sub-National Geospatial Data Archive System.

This package provides a simple and light-weight API for memory profiling of R expressions. The profiling is built on top of R's built-in memory profiler utils::Rprofmem(), which records every memory allocation done by R (also native code).

The fst package for R provides a fast, easy and flexible way to serialize data frames. With access speeds of multiple GB/s, fst is specifically designed to unlock the potential of high speed solid state disks. Data frames stored in the fst format have full random access, both in column and rows. The fst format allows for random access of stored data and compression with the LZ4 and ZSTD compressors.

Meta-analysis is widely used to summarize estimated effects sizes across multiple statistical tests. Standard fixed and random effect meta-analysis methods assume that the estimated of the effect sizes are statistically independent. Here we relax this assumption and enable meta-analysis when the correlation matrix between effect size estimates is known.

Webshot makes it easy to take screenshots of web pages from within R. It can also run Shiny applications locally and take screenshots of the application; and it can render and screenshot static as well as interactive R Markdown documents.

This package provides a set of tools to permute multisets without loops or hash tables and to generate integer partitions. Cool-lex order is similar to colexicographical order.

This is package for regression modeling using rules with added instance-based corrections.

The package converts the input in any one of character, integer, numeric, factor, or an ordered type into POSIXct (or Date) objects, using one of a number of predefined formats, and relying on Boost facilities for date and time parsing.

This package provides an implementation of the Uniform Manifold Approximation and Projection dimensionality reduction by McInnes et al. (2018). It also provides means to transform new data and to carry out supervised dimensionality reduction. An implementation of the related LargeVis method of Tang et al. (2016) is also provided.

This package performs several conventional cross-validation statistical methods for climate-growth model in the climate reconstruction from tree rings, including Sign Test statistic, Reduction of Error statistic, Product Mean Test, Durbin-Watson statistic etc.

This package provides an efficient algorithm for solving ultra-sparse regularized regression models using a variational Bayes algorithm with a spike prior. The algorithm is solved on a path, with coordinate updates, and is capable of generating very sparse models. Very general model diagnostics for controlling type-1 errors are also provided.

This package handles very large numbers in R. Real numbers are held using their natural logarithms, plus a logical flag indicating sign. The package includes a vignette that gives a step-by-step introduction to using S4 methods.

This package performs optimization in R using C++. A unified wrapper interface is provided to call C functions of the five optimization algorithms (Nelder-Mead, BFGS, CG, L-BFGS-B and SANN) underlying optim().