This package provides the functions for Brunner-Munzel test and permuted Brunner-Munzel test, which enable to use formula, matrix, and table as argument. These functions are based on Brunner and Munzel (2000) <doi:10.1002/(SICI)1521-4036(200001)42:1%3C17::AID-BIMJ17%3E3.0.CO;2-U> and Neubert and Brunner (2007) <doi:10.1016/j.csda.2006.05.024>, and are written with FORTRAN.

This package provides methods to compute linear h-step ahead prediction coefficients based on localised and iterated Yule-Walker estimates and empirical mean squared and absolute prediction errors for the resulting predictors. Also, functions to compute autocovariances for AR(p) processes, to simulate tvARMA(p,q) time series, and to verify an assumption from Kley et al. (2019), Electronic of Statistics, forthcoming. Preprint <arXiv:1611.04460>.

Segmentation and classification procedures for data from the Activinsights GENEActiv <https://activinsights.com/technology/geneactiv/> accelerometer that provides the user with a model to guess behaviour from test data where behaviour is missing. Includes a step counting algorithm, a function to create segmented data with custom features and a function to use recursive partitioning provided in the function rpart() of the rpart package to create classification models.

Metaprogramming utilities for converting R regression model formulae to equivalents in Julia <doi:10.1137/141000671>, via modifications to the abstract syntax tree. Supports translations in zero correlation random effects syntax, protection of expressions to be evaluated as-is, interaction terms, and more. Accepts strings or R formula objects and returns modified R formula objects where possible (or a modified string, if not a valid formula in R).

This package provides a nonparametric, multicore-capable plausible naive Bayes classifier based on the Pareto density estimation (PDE) featuring a plausible approach to a pitfall in the Bayesian theorem covering low evidence cases. Stier, Q., Hoffmann, J., and Thrun, M.C.: "Classifying with the Fine Structure of Distributions: Leveraging Distributional Information for Robust and Plausible Naive Bayes" (2026), Machine Learning and Knowledge Extraction (MAKE), <DOI:10.3390/make8010013>.

Sudoku designs (Bailey et al., 2008<doi:10.1080/00029890.2008.11920542>) can be used as experimental designs which tackle one extra source of variation than conventional Latin square designs. Although Sudoku designs are similar to Latin square designs, only addition is the region concept. Some very important functions related to row-column designs as well as block designs along with basic functions are included in this package.

The scDiagnostics package provides diagnostic plots to assess the quality of cell type assignments from single cell gene expression profiles. The implemented functionality allows to assess the reliability of cell type annotations, investigate gene expression patterns, and explore relationships between different cell types in query and reference datasets allowing users to detect potential misalignments between reference and query datasets. The package also provides visualization capabilities for diagnostics purposes.

RStudio allows to show and navigate for the outline of a R Markdown file, but not for R Markdown projects with multiple files. For this reason, I have developed several RStudio addins capable of show project outline. Each addin is specialized in showing projects of different types: R Markdown project, bookdown package project and LaTeX project. There is a configuration file that allows you to customize additional searches.

This package provides a collection of methods to determine the required sample size for the evaluation of inequality constrained hypotheses by means of a Bayes factor. Alternatively, for a given sample size, the unconditional error probabilities or the expected conditional error probabilities can be determined. Additional material on the methods in this package is available in Klaassen, F., Hoijtink, H. & Gu, X. (2019) <doi:10.31219/osf.io/d5kf3>.

Colocalisation analysis tests whether two traits share a causal genetic variant in a specified genomic region. Proportional testing for colocalisation has been previously proposed [Wallace (2013) <doi:10.1002/gepi.21765>], but is reimplemented here to overcome barriers to its adoption. Its use is complementary to the fine- mapping based colocalisation method in the coloc package, and may be used in particular to identify false "H3" conclusions in coloc'.

This package provides landscape genomic functions to analyse SNP (single nuclear polymorphism) data, such as least cost path analysis and isolation by distance. Therefore each sample needs to have coordinate data attached (lat/lon) to be able to run most of the functions. dartR.spatial is a package that belongs to the dartRverse suit of packages and depends on dartR.base and dartR.data'.

DEploid (Zhu et.al. 2018 <doi:10.1093/bioinformatics/btx530>) is designed for deconvoluting mixed genomes with unknown proportions. Traditional phasing programs are limited to diploid organisms. Our method modifies Li and Stephenâ s algorithm with Markov chain Monte Carlo (MCMC) approaches, and builds a generic framework that allows haloptype searches in a multiple infection setting. This package provides R functions to support data analysis and results interpretation.

Researchers can characterize and learn about the properties of research designs before implementation using `DeclareDesign`. Ex ante declaration and diagnosis of designs can help researchers clarify the strengths and limitations of their designs and to improve their properties, and can help readers evaluate a research strategy prior to implementation and without access to results. It can also make it easier for designs to be shared, replicated, and critiqued.

This package provides a set of tools for network analysis using mass spectrometry-based proteomics data and network databases. The package takes as input the output of MSstats differential abundance analysis and provides functions to perform enrichment analysis and visualization in the context of prior knowledge from past literature. Notably, this package integrates with INDRA, which is a database of biological networks extracted from the literature using text mining techniques.

Manage keys, certificates, secrets, and storage accounts in Microsoft's Key Vault service: <https://azure.microsoft.com/products/key-vault/>. Provides facilities to store and retrieve secrets, use keys to encrypt, decrypt, sign and verify data, and manage certificates. Integrates with the AzureAuth package to enable authentication with a certificate, and with the openssl package for importing and exporting cryptographic objects. Part of the AzureR family of packages.

An add-on package to DImodels for the fitting of biodiversity and ecosystem function relationship study data with multiple ecosystem function responses and/or time points. This package uses the multivariate and repeated measures Diversity-Interactions (DI) methods developed by Kirwan et al. (2009) <doi:10.1890/08-1684.1>, Finn et al. (2013) <doi:10.1111/1365-2664.12041>, and Dooley et al. (2015) <doi:10.1111/ele.12504>.

This package provides a drop-in replacement for flexdashboard Rmd documents, which implements an after-knit-hook to split the generated single page application in one document per main section to reduce rendering load in the web browser displaying the document. Put all JavaScript stuff needed in all sections before the first headline featuring navigation menu attributes. This package is experimental and maybe replaced by a solution inside flexdashboard'.

The HJ-Biplot is a multivariate method that represents high-dimensional data in a low-dimensional subspace, capturing most of the informationâ s variability in just a few dimensions. This package implements three new regularized versions of the HJ-Biplot: Ridge, LASSO, and Elastic Net. These versions introduce restrictions that shrink or zero-out variable weights to improve interpretability based on regularization theory. All methods provide graphical representations using ggplot2'.

This package provides a comprehensive and user-friendly interface for accessing, manipulating, and analyzing country-level data from around the world. It allows users to retrieve detailed information on countries, including names, regions, continents, populations, currencies, calling codes, and more, all in a tidy data format. The package is designed to work seamlessly within the tidyverse ecosystem, making it easy to filter, arrange, and visualize country-level data in R.

Ensmallen is a templated C++ mathematical optimization library (by the MLPACK team) that provides a simple set of abstractions for writing an objective function to optimize. Provided within are various standard and cutting-edge optimizers that include full-batch gradient descent techniques, small-batch techniques, gradient-free optimizers, and constrained optimization. The RcppEnsmallen package includes the header files from the Ensmallen library and pairs the appropriate header files from armadillo through the RcppArmadillo package. Therefore, users do not need to install Ensmallen nor Armadillo to use RcppEnsmallen'. Note that Ensmallen is licensed under 3-Clause BSD, Armadillo starting from 7.800.0 is licensed under Apache License 2, RcppArmadillo (the Rcpp bindings/bridge to Armadillo') is licensed under the GNU GPL version 2 or later. Thus, RcppEnsmallen is also licensed under similar terms. Note that Ensmallen requires a compiler that supports C++14 and Armadillo 10.8.2 or later.

The package provides a comprehensive mapping table of metabolites and proteins linked to PathBank pathways. The tables include HMDB, KEGG, ChEBI, CAS, Drugbank, Uniprot IDs. The tables are provided for each of the 10 species ("Homo sapiens", "Escherichia coli", "Mus musculus", "Arabidopsis thaliana", "Saccharomyces cerevisiae", "Bos taurus", "Caenorhabditis elegans", "Rattus norvegicus", "Drosophila melanogaster", and "Pseudomonas aeruginosa"). These table information can be used for Metabolite Set (and other) Enrichment Analysis.

This package provides tools to process the information obtained from experiments conducted in the Barnes Maze. These tools enable the detection of trajectories generated by subjects during trials, as well as the acquisition of precise coordinates and relevant statistical data regarding the results. Through this approach, it aims to facilitate the analysis and interpretation of observed behaviors, thereby contributing to a deeper understanding of learning and memory processes in such experiments.

Analysis workflow for finding geographic boundaries of ecological or landscape traits and comparing the placement of geographic boundaries of two traits. If data are trait values, trait data are transformed to boundary intensities based on approximate first derivatives across latitude and longitude. The package includes functions to create custom null models based on the input data. The boundary statistics are described in: Fortin, Drapeau, and Jacquez (1996) <doi:10.2307/3545584>.

This package provides Bayesian quantile regression models for complex survey data under informative sampling using survey-weighted estimators. Both single- and multiple-output models are supported. To accelerate computation, all algorithms are implemented in C++ using Rcpp', RcppArmadillo', and RcppEigen', and are called from R'. See Nascimento and Gonçalves (2024) <doi:10.1093/jssam/smae015> and Nascimento and Gonçalves (2025, in press) <https://academic.oup.com/jssam>.