This package provides a minimal set of predicates and assertions used by the assertive package. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

This package provides a drop-in replacement for rasterize from the raster package that takes sf-type objects, and is much faster. There is support for the main options provided by the rasterize function, including setting the field used and background value, and options for aggregating multi-layer rasters.

The Rcpp package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about Rcpp is provided by several vignettes included in this package, via the Rcpp Gallery site at <http://gallery.rcpp.org>, the paper by Eddelbuettel and Francois (2011, JSS), and the book by Eddelbuettel (2013, Springer); see citation("Rcpp") for details on these last two.

Computes local polynomial estimators for the regression and also density. It comprises several different utilities to handle kernel estimators.

The r-mhsmm package implements estimation and prediction methods for hidden Markov and semi-Markov models for multiple observation sequences. Such techniques are of interest when observed data is thought to be dependent on some unobserved (or hidden) state. Also, this package is suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions.

This package provides a unified interface to interact with Docker and Singularity containers. You can execute a command inside a container, mount a volume or copy a file.

This package guesses the MIME type from a filename extension using the data derived from /etc/mime.types in UNIX-type systems.

This package contains functionality for importing and managing of downloaded genome annotation data from the Ensembl genome browser (European Bioinformatics Institute) and from the UCSC genome browser (University of California, Santa Cruz) and annotation routines for genomic positions and splice site positions.

This package provides functions and data sets for actuarial science: modeling of loss distributions; risk theory and ruin theory; simulation of compound models, discrete mixtures and compound hierarchical models; credibility theory. It boasts support for many additional probability distributions to model insurance loss amounts and loss frequency: 19 continuous heavy tailed distributions; the Poisson-inverse Gaussian discrete distribution; zero-truncated and zero-modified extensions of the standard discrete distributions. It also supports phase-type distributions commonly used to compute ruin probabilities.

This package provides an API for efficient .hic file data extraction with programmatic matrix access. It doesn't store the pointer data for all the matrices, only the one queried, and currently it only supports matrices.

This package contains functions to estimate L-moments and trimmed L-moments from the data. It also contains functions to estimate the parameters of the normal polynomial quantile mixture and the Cauchy polynomial quantile mixture from L-moments and trimmed L-moments.

This package provides an mlr3 extension that provides various resampling-based confidence interval (CI) methods for estimating the generalization error. These CI methods are implemented as mlr3 measures, enabling the evaluation of individual algorithms on specific tasks as well as the comparison of different learning algorithms.

This package provides the usual distribution functions, maximum likelihood inference and model diagnostics for univariate stationary extreme value mixture models. Also, there are provided kernel density estimation including various boundary corrected kernel density estimation methods and a wide choice of kernels, with cross-validation likelihood based bandwidth estimator. Reasonable consistency with the base functions in the evd package is provided, so that users can safely interchange most code.

This package loads electrophysiology data from ABF2 files, as created by Axon Instruments/Molecular Devices software. Only files recorded in gap-free mode are currently supported.

This package contains a simple SMTP client which provides a portable solution for sending email, including attachments, from within R.

Create interactive 3D scatter plots, network plots, and globes in R using the three.js visualization library.

This package provides a simple interface for creating active bindings where the bound function accepts additional arguments.

This package provides extensions to ggplot2, respecting the grammar of its graphics paradigm.

This R package caches the results of a function so that when you call it again with the same arguments it returns the pre-computed value.

This package contains third-party map tile provider information from Leaflet.js, to be used with the leaflet R package. Additionally, leaflet.providers enables users to retrieve up-to-date provider information between package updates.

This package provides functions for applying a wide range of fisheries stock assessment methods.

Function-oriented Make-like declarative pipelines for statistics and data science are supported in the targets R package. As an extension to targets, the tarchetypes package provides convenient user-side functions to make targets easier to use. By establishing reusable archetypes for common kinds of targets and pipelines, these functions help express complicated reproducible pipelines concisely and compactly. The methods in this package were influenced by the drake R package by Will Landau (2018) <doi:10.21105/joss.00550>.

The main function archetypes implements a framework for archetypal analysis supporting arbitrary problem solving mechanisms for the different conceptual parts of the algorithm.

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.