This package provides functions for extracting feature contributions from a random forest model from package randomForest. Feature contributions provide detailed information about the relationship between data variables and the predicted value returned by random forest model.

This package provides tools for manipulating, exploring, and visualising multiple-response data, including scored or ranked responses. Conversions to and from factors, lists, strings, matrices; reordering, lumping, flattening; set operations; tables; frequency and co-occurrence plots.

This package provides tools for regression-based Boolean rule inference in artificial intelligence studies. The package fits ridge regression models on conjunction expansions and composes interpretable rule sets. Parallel execution is supported for multi-CPU environments.

This package provides a pure R implementation of the t-SNE algorithm.

Combining Univariate Association Test Results of Multiple Phenotypes for Detecting Pleiotropy.

Collection of functions for distributed lag linear and non-linear models.

This package provides a framework for creating plots with glowing points.

Visualizes multiple sequence alignments dynamically within the Shiny web application framework.

Package for fast computation of the maximum kernel likelihood estimator (mkle).

This package provides functions for measuring population divergence from genotypic data.

Construct a Hidden Markov Model with states learnt by unsupervised classification.

This package contains functions for visualization univariate data: ccdplot and qddplot.

This package finds the k nearest neighbours for every point in a given dataset in O(N log N) time using Arya and Mount's ANN library. Provides approximate, exact searches, fixed radius searches, bd and kb trees.

This package provides a cross-validated minimal-optimal feature selection algorithm. It utilises popularity counting, hierarchical clustering with feature dissimilarity measures, and prefiltering with all-relevant feature selection method to obtain the minimal-optimal set of features.

Random vectors, called rvecs. An rvec holds multiple draws, but tries to behave like a standard R vector, including working well in data frames. Rvecs are useful for analysing output from a simulation or a Bayesian analysis.

This package provides methods for Resampling-based False Discovery Proportion control. A function is provided that provides simultaneous, multi-resolution False Discovery Exceedance (FDX) control as described in Hemerik (2025) <doi:10.48550/arXiv.2509.02376>.

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.

This package provides methods and classes for object-oriented programming in R with or without references. Large effort has been made on making definition of methods as simple as possible with a minimum of maintenance for package developers.

This package provides an R wrapper for BWA alignment algorithms. Both BWA-backtrack and BWA-MEM are available. Convenience function to build a BWA index from a reference genome is also provided. Currently not supported for Windows machines.

Estimates robust rank-based fixed effects and predicts robust random effects in two- and three- level random effects nested models. The methodology is described in Bilgic & Susmann (2013) <https://journal.r-project.org/archive/2013/RJ-2013-027/>.

Convenience functions to make some common tasks with right-to-left string printing easier, more convenient and with no need to remember long Unicode characters. Specifically helpful for right-to-left languages such as Arabic, Persian and Hebrew.

Implementation of Robust Regression tailored to deal with Asymmetric noise Distribution, which was originally proposed by Takeuchi & Bengio & Kanamori (2002) <doi:10.1162/08997660260293300>. In addition, this implementation is extended as introducing potential feature regularization by LASSO etc.

Pointwise generation and display of attractors (prefractals) of the random iterated function system (RIFS) for various combinations of probabilistic and geometric parameters of some fixed point sets (protofractals), described by Bukhovets A.G. (2012) <doi:10.1134/S0005117912020154>.

This package provides graphical user interfaces to organize and visualize Nanopore sequencing data.