This package provides a collection of functions to help in the analysis of right-censored survival data. These extend the methods available in the survival package.

This package provides an R implementation of the Octave package signal, containing a variety of signal processing tools, such as signal generation and measurement, correlation and convolution, filtering, filter design, filter analysis and conversion, power spectrum analysis, system identification, decimation and sample rate change, and windowing.

The curl() and curl_download() functions provide highly configurable drop-in replacements for base url() and download.file() with better performance, support for encryption, gzip compression, authentication, and other libcurl goodies. The core of the package implements a framework for performing fully customized requests where data can be processed either in memory, on disk, or streaming via the callback or connection interfaces.

This package provides RStudio addins and R functions that make copy-pasting vectors and tables to text painless.

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 contains R-functions to perform an fMRI analysis as described in Polzehl and Tabelow (2019) <DOI:10.1007/978-3-030-29184-6>, Tabelow et al. (2006) <DOI:10.1016/j.neuroimage.2006.06.029>, Polzehl et al. (2010) <DOI:10.1016/j.neuroimage.2010.04.241>, Tabelow and Polzehl (2011) <DOI:10.18637/jss.v044.i11>.

Query, set, and delete credentials from the git credential store. Manage GitHub tokens and other git credentials. This package is to be used by other packages that need to authenticate to GitHub and/or other git repositories.

This package provides functions for analyzing multivariate data. Dependencies of the distribution of the specified variable (response variable) to other variables (explanatory variables) are derived and evaluated by the Akaike Information Criterion (AIC).

This package provides a file format for storing tensors that is secure (doesn't allow for code execution), fast and simple to implement. safetensors also enables cross language and cross frameworks compatibility making it an ideal format for storing machine learning model weights.

This package performs penalized multivariate analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis.

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

This is a package for pretty-printing R code without changing the user's formatting intent.

This package provides an interface to the rich display capabilities of Jupyter front-ends (e.g. Jupyter Notebook). It is designed to be used from a running IRkernel session.

This package provides an R module for display of maps. Projection code and larger maps are in separate packages (mapproj and mapdata).

This package allows the estimation of hierarchical F-statistics from haploid or diploid genetic data with any numbers of levels in the hierarchy, following the algorithm of Yang (Evolution, 1998, 52(4):950-956). Functions are also given to test via randomisations the significance of each F and variance components, using the likelihood-ratio statistics G.

This package provides tools to safely and efficiently organize and execute Monte Carlo simulation experiments in R. The package controls the structure and back-end of Monte Carlo simulation experiments by utilizing a generate-analyse-summarise workflow. The workflow safeguards against common simulation coding issues, such as automatically re-simulating non-convergent results, prevents inadvertently overwriting simulation files, catches error and warning messages during execution, implicitly supports parallel processing with high-quality random number generation, and provides tools for managing high-performance computing (HPC) array jobs submitted to schedulers such as SLURM. For a pedagogical introduction to the package see Sigal and Chalmers (2016) <doi:10.1080/10691898.2016.1246953>. For a more in-depth overview of the package and its design philosophy see Chalmers and Adkins (2020) <doi:10.20982/tqmp.16.4.p248>.

This package provides empirical likelihood ratio tests for means/quantiles/hazards from possibly censored and/or truncated data. It also does regression.

This package provides a multi-modal simulation engine for studying dynamic cellular processes at single-cell resolution.

This is a package for stubbing and setting expectations on HTTP requests. It includes tools for stubbing HTTP requests, including expected request conditions and response conditions. You can match on HTTP method, query parameters, request body, headers and more. It can be used for unit tests or outside of a testing context.

This package provides a series of additional Tcl commands and Tk widgets with style and various functions to supplement the tcltk package

This package offers an implementation of the Abnormal blood profile score (ABPS). The ABPS is a part of the Athlete biological passport program of the World anti-doping agency, which combines several blood parameters into a single score in order to detect blood doping. The package also contains functions to calculate other scores used in anti-doping programs, such as the ratio of hemoglobin to reticulocytes (OFF-score), as well as example data.

This package allows you to control the number of threads the BLAS library uses. It is also possible to control the number of threads in OpenMP.

This package provides estimators for multinomial logit models in their conditional logit and baseline logit variants, with or without random effects, with or without overdispersion. Random effects models are estimated using the PQL technique (based on a Laplace approximation) or the MQL technique (based on a Solomon-Cox approximation). Estimates should be treated with caution if the group sizes are small.

This package provides various methods for clustering and cluster validation. For example, it provides fixed point clustering, linear regression clustering, clustering by merging Gaussian mixture components, as well as symmetric and asymmetric discriminant projections for visualisation of the separation of groupings.