This package provides an easy to use library to setup, apply and make inference with discrete time and discrete space hidden Markov models.

Functions for modelling that help you seamlessly integrate modelling into a pipeline of data manipulation and visualisation.

This package provides an optimization method based on sequential quadratic programming for maximum likelihood estimation of the mixture proportions in a finite mixture model where the component densities are known. The algorithm is expected to obtain solutions that are at least as accurate as the state-of-the-art MOSEK interior-point solver, and they are expected to arrive at solutions more quickly when the number of samples is large and the number of mixture components is not too large.

This package provides binning and plotting functions for hexagonal bins. It uses and relies on grid graphics and formal (S4) classes and methods.

This package provides a fast C++ implementation to generate contour lines (isolines) and contour polygons (isobands) from regularly spaced grids containing elevation data.

Streaming JSON (ndjson) has one JSON record per-line and many modern ndjson files contain large numbers of records. These constructs may not be columnar in nature, but it is often useful to read in these files and "flatten" the structure out to enable working with the data in an R data.frame-like context. Functions are provided that make it possible to read in plain ndjson files or compressed (gz) ndjson files and either validate the format of the records or create "flat" data.table structures from them.

The base functions for set operations in R can be used for only two sets. This package RVenn provides functions for dealing with multiple sets. It uses purr to find the union, intersection and difference of three or more sets. This package also provides functions for pairwise set operations among several sets. Further, based on ggplot2 and ggforce, a Venn diagram can be drawn for two or three sets. For bigger data sets, a clustered heatmap showing the presence or absence of the elements of the sets can be drawn based on the pheatmap package. Finally, enrichment test can be applied to two sets whether an overlap is statistically significant or not.

Zero-variance control variates (ZV-CV) is a post-processing method to reduce the variance of Monte Carlo estimators of expectations using the derivatives of the log target. Once the derivatives are available, the only additional computational effort is in solving a linear regression problem. This method has been extended to higher dimensions using regularisation. This package can be used to easily perform ZV-CV or regularised ZV-CV when a set of samples, derivatives and function evaluations are available. Additional functions for applying ZV-CV to two estimators for the normalising constant of the posterior distribution in Bayesian statistics are also supplied.

Solve optimal transport problems. Compute Wasserstein distances (a.k.a. Kantorovitch, Fortet--Mourier, Mallows, Earth Mover's, or minimal L_p distances), return the corresponding transference plans, and display them graphically. Objects that can be compared include grey-scale images, (weighted) point patterns, and mass vectors.

This package implements time series clustering along with optimized techniques related to the dynamic time warping distance and its corresponding lower bounds. The implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole clustering are available. Functionality can be easily extended with custom distance measures and centroid definitions. Implementations of DTW barycenter averaging, a distance based on global alignment kernels, and the soft-DTW distance and centroid routines are also provided. All included distance functions have custom loops optimized for the calculation of cross-distance matrices, including parallelization support. Several cluster validity indices are included.

This package provides support for linear order and unimodal order (univariate) isotonic regression and bivariate isotonic regression with linear order on both variables.

This package provides a compilation of extra ggplot2 themes, scales and utilities, including a spell check function for plot label fields and an overall emphasis on typography.

This package provides an interface to figshare, a scientific repository to archive and assign DOIs to data, software, figures, and more.

This package provides a URL-safe base64 encoder and decoder. In contrast to RFC3548, the 62nd character (+) is replaced with -, the 63rd character (/) is replaced with _. Furthermore, the encoder does not fill the string with trailing =. The resulting encoded strings comply to the regular expression pattern [A-Za-z0-9_-] and thus are safe to use in URLs or for file names. The package also comes with a simple base32 encoder/decoder suited for case insensitive file systems.

This package allows you to create Q-Q and Manhattan plots for GWAS data from PLINK results.

This package allows you to render vector-based SVG images into high-quality custom-size bitmap arrays using the librsvg2 library. The resulting bitmap can be written to e.g. PNG, JPEG or WEBP format. In addition, the package can convert images directly to various formats such as PDF or PostScript.

This package implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.

This package provides a fast and user-friendly implementation of nonparametric estimators for censored event history (survival) analysis with the Kaplan-Meier and Aalen-Johansen methods.

This package provides routines for Maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Normal linear State Space models, also known as Dynamic Linear Models.

Inspired by the the futile.logger R package and logging Python module, this utility provides a flexible and extensible way of formatting and delivering log messages with low overhead.

This package provides a collection of high-performance utilities. It can be used to compute distances, correlations, autocorrelations, clustering, and other tasks. It also contains a graph clustering algorithm described in MetaCell analysis of single-cell RNA-seq data using K-nn graph partitions.

This package provides a suite of elliptic and related functions including Weierstrass and Jacobi forms. It also includes various tools for manipulating and visualizing complex functions.

This package implements several Approximate Bayesian Computation (ABC) algorithms for performing parameter estimation, model selection, and goodness-of-fit. Cross-validation tools are also available for measuring the accuracy of ABC estimates, and to calculate the misclassification probabilities of different models.

This package provides functions for estimating tolerance limits (intervals) for various univariate distributions (binomial, Cauchy, discrete Pareto, exponential, two-parameter exponential, extreme value, hypergeometric, Laplace, logistic, negative binomial, negative hypergeometric, normal, Pareto, Poisson-Lindley, Poisson, uniform, and Zipf-Mandelbrot), Bayesian normal tolerance limits, multivariate normal tolerance regions, nonparametric tolerance intervals, tolerance bands for regression settings (linear regression, nonlinear regression, nonparametric regression, and multivariate regression), and analysis of variance tolerance intervals. Visualizations are also available for most of these settings.