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.

This package provides tools to obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models. It can be used to compute contrasts or linear functions of EMMs, trends, and comparisons of slopes.

The rasdaemon daemon monitors platform RAS reports from Linux kernel trace events. These trace events are logged in /sys/kernel/debug/tracing and reported through standard log mechanisms like syslog.

This package provides a Ruby implementation of RFC5869: HKDF. The goal of HKDF is to take some source key material and generate suitable cryptographic keys from it.

This package provides functions for creating various visualizations, convenient wrappers, and quality-of-life utilities for single cell experiment objects. It offers a streamlined approach to visualize results and integrates different tools for easy use.

This package provides functions for fitting MOSAiCS and MOSAiCS-HMM, a statistical framework to analyze one-sample or two-sample ChIP-seq data of transcription factor binding and histone modification.

Build and train a variational autoencoder (VAE) for mixed-type tabular data (continuous, binary, categorical). Models are implemented using TensorFlow and Keras via the reticulate interface, enabling reproducible VAE training for heterogeneous tabular datasets.

An extension to the DPQ package with computations for DPQ (Density (pdf), Probability (cdf) and Quantile) functions, where the functions here partly use the Rmpfr package and hence the underlying MPFR and GMP C libraries.

End-member modelling analysis of grain-size data is an approach to unmix a data set's underlying distributions and their contribution to the data set. EMMAgeo provides deterministic and robust protocols for that purpose.

Generate citations and references for R packages from CRAN or Bioconductor. Supports RIS and BibTeX formats with automatic DOI retrieval from GitHub repositories and published papers. Includes command-line interface for batch processing.

This package provides an efficient C++ code for computing an optimal segmentation model with Poisson loss, up-down constraints, and label constraints, as described by Kaufman et al. (2024) <doi:10.1080/10618600.2023.2293216>.

YACFP (Yet Another Convenience Function Package). get_age() is a fast & accurate tool for measuring fractional years between two dates. stale_package_check() tries to identify any library() calls to unused packages.

Efficient computation of the Liu regression coefficient paths, Liu-related statistics and information criteria for a grid of the regularization parameter. The computations are based on the C++ library Armadillo through the R package Rcpp'.

Allows maximum likelihood fitting of cluster-weighted models, a class of mixtures of regression models with random covariates. Methods are described in Angelo Mazza, Antonio Punzo, Salvatore Ingrassia (2018) <doi:10.18637/jss.v086.i02>.

This package provides functions for downloading of geographic data for use in spatial analysis and mapping. The package facilitates access to climate, crops, elevation, land use, soil, species occurrence, accessibility, administrative boundaries and other data.

This package provides a collection of tools which extract a model documentation from GAMS code and comments. In order to use the package you need to install pandoc and pandoc-citeproc first (<https://pandoc.org/>).

Estimates the parameters of a GARCH-X model with exogenous covariates, performs hypothesis tests for the parameters returning the p-values, and uses False Discovery Rate p-value corrections to select the exogenous variables.

This package contains all the data and functions used in Generalized Linear Models, 2nd edition, by Jeff Gill and Michelle Torres. Examples to create all models, tables, and plots are included for each data set.

This package provides functions to support the ICES Transparent Assessment Framework <https://taf.ices.dk> to organize data, methods, and results used in ICES assessments. ICES is an organization facilitating international collaboration in marine science.

This package implements a generalization of the Cochran-Armitage trend test to multinomial data. In addition to an overall test, multiple testing adjusted p-values for trend in individual outcomes and power calculation is available.

Bayesian estimation of inverse variance weighted (IVW), Burgess et al. (2013) <doi:10.1002/gepi.21758>, and MR-Egger, Bowden et al. (2015) <doi:10.1093/ije/dyv080>, summary data models for Mendelian randomization analyses.

This package provides users to call MATLAB from using the "system" command. Allows users to submit lines of code or MATLAB m files. This is in comparison to R.matlab', which creates a MATLAB server.

This package provides residuals and overdispersion metrics to assess the fit of N-mixture models obtained using the package unmarked'. Details on the methods are given in Knape et al. (2017) <doi:10.1101/194340>.

This package provides a near drop-in replacement for base::Sys.sleep() that allows more types of input to produce delays in the execution of code and can silence/prevent typical sources of error.