This package provides functions to train self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM.

This package provides functions that implement the known population median test.

This package provides simple mechanisms for defining and interpreting package options. It provides helpers for interpreting environment variables, global options, defining default values and more.

This package implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging algorithm using classification trees as individual classifiers. Once these classifiers have been trained, they can be used to predict on new data. Also, cross validation estimation of the error can be done.

This package helps you to automate R package and project setup tasks that are otherwise performed manually. This includes setting up unit testing, test coverage, continuous integration, Git, GitHub integration, licenses, Rcpp, RStudio projects, and more.

mlr3tuning implements methods for hyperparameter tuning, e.g. Grid Search, Random Search, or Simulated Annealing. Various termination criteria can be set and combined. The class AutoTuner provides a convenient way to perform nested resampling in combination with mlr3.

This package provides R bindings to OpenSSL libssl and libcrypto, plus custom SSH pubkey parsers. It supports RSA, DSA and NIST curves P-256, P-384 and P-521. Cryptographic signatures can either be created and verified manually or via x509 certificates. AES block cipher is used in CBC mode for symmetric encryption; RSA for asymmetric (public key) encryption. High-level envelope functions combine RSA and AES for encrypting arbitrary sized data. Other utilities include key generators, hash functions (md5, sha1, sha256, etc), base64 encoder, a secure random number generator, and bignum math methods for manually performing crypto calculations on large multibyte integers.

This package provides MathJax and macros to enable its use within Rd files for rendering equations in the HTML help files.

This package lets you edit and simplify geojson, Spatial, and sf objects. This is a wrapper around the mapshaper JavaScript library to perform topologically-aware polygon simplification, as well as other operations such as clipping, erasing, dissolving, and converting multi-part to single-part geometries.

OpenTelemetry is a collection of tools, APIs, and SDKs used to instrument, generate, collect, and export telemetry data (metrics, logs, and traces) for analysis in order to understand your software's performance and behavior. This package implements the OpenTelemetry API. Use this package as a dependency if you want to instrument your R package for OpenTelemetry.

This package provides smooth additive quantile regression models, fitted using the methods of Fasiolo et al. (2017). Differently from quantreg, the smoothing parameters are estimated automatically by marginal loss minimization, while the regression coefficients are estimated using either PIRLS or Newton algorithm. The learning rate is determined so that the Bayesian credible intervals of the estimated effects have approximately the correct coverage. The main function is qgam() which is similar to gam() in the mgcv package, but fits non-parametric quantile regression models.

Facilitates easy analysis of factorial experiments, including purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss designs, and mixed within-and-between-Ss designs. The functions in this package aim to provide simple, intuitive and consistent specification of data analysis and visualization. Visualization functions also include design visualization for pre-analysis data auditing, and correlation matrix visualization. Finally, this package includes functions for non-parametric analysis, including permutation tests and bootstrap resampling. The bootstrap function obtains predictions either by cell means or by more advanced/powerful mixed effects models, yielding predictions and confidence intervals that may be easily visualized at any level of the experiment's design.

This package provides basic wavelet routines for time series (1D), image (2D) and array (3D) analysis. The code provided here is based on wavelet methodology developed in Percival and Walden (2000); Gencay, Selcuk and Whitcher (2001); the dual-tree complex wavelet transform (DTCWT) from Kingsbury (1999, 2001) as implemented by Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002).

This is a package for parallel computing with a network of local and remote workers. It enables fast exchange of results between the workers through a Redis database. Key features include task queues, local caching, and sophisticated error handling.

Dimensionality reduction (DR) is widely used in many domains for analyzing and visualizing high-dimensional data. The tidydr package provides uniform output and is compatible with multiple methods, including prcomp, mds, Rtsne, etc.

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 provides interactive visualizations for profiling R code.

This package contains routines and documentation for solving quadratic programming problems.

This package provides several cluster-robust variance estimators (i.e., sandwich estimators) for ordinary and weighted least squares linear regression models, including the bias-reduced linearization estimator introduced by Bell and McCaffrey (2002) http://www.statcan.gc.ca/pub/12-001-x/2002002/article/9058-eng.pdf and developed further by Pustejovsky and Tipton (2017) doi:10.1080/07350015.2016.1247004. The package includes functions for estimating the variance- covariance matrix and for testing single- and multiple-contrast hypotheses based on Wald test statistics. Tests of single regression coefficients use Satterthwaite or saddle-point corrections. Tests of multiple-contrast hypotheses use an approximation to Hotelling's T-squared distribution. Methods are provided for a variety of fitted models, including lm() and mlm objects, glm(), ivreg (from package AER), plm() (from package plm), gls() and lme() (from nlme), robu() (from robumeta), and rma.uni() and rma.mv() (from metafor).

This package provides a cross-platform Zip compression library for R. It is a replacement for the zip function, that does not require any additional external tools on any platform.

This package provides the header files for a stripped-down version of the plog header-only C++ logging library, and a method to log to R's standard error stream.

This package provides functions to compute the distribution function of quadratic forms in normal variables using Imhof's method, Davies's algorithm, Farebrother's algorithm or Liu et al.'s algorithm.

This package provides high performance container data types such as queues, stacks, deques, dicts and ordered dicts.

This package provides a fast, scalable, and versatile framework for simulating large systems with Gillespie's Stochastic Simulation Algorithm (SSA). This package is the spiritual successor to the GillespieSSA package. Benefits of this package include major speed improvements (>100x), easier to understand documentation, and many unit tests that try to ensure the package works as intended.