Implementation of penalized regression with second-generation p-values for variable selection. The algorithm can handle linear regression, GLM, and Cox regression. S3 methods print(), summary(), coef(), predict(), and plot() are available for the algorithm. Technical details can be found at Zuo et al. (2021) <doi:10.1080/00031305.2021.1946150>.

Uses provenance post-execution to help the user understand and debug their script by providing functions to look at intermediate steps and data values, their forwards and backwards lineage, and to understand the steps leading up to warning and error messages. provDebugR uses provenance produced by rdtLite (available on CRAN), stored in PROV-JSON format.

Includes JavaScript files that allow plotly maps to render without an internet connection.

The first goal of this package is to provide a multitude of tree models, i.e., functions that generate rooted binary trees with a given number of leaves. Second, the package allows for an easy evaluation and comparison of tree shape statistics by estimating their power to differentiate between different tree models. Please note that this R package was developed alongside the manuscript Tree balance in phylogenetic models by S. J. Kersting, K. Wicke, and M. Fischer (2025) <doi:10.1098/rstb.2023.0303>, which provides further background and the respective mathematical definitions. This project was supported by the project ArtIGROW, which is a part of the WIR!-Alliance ArtIFARM â Artificial Intelligence in Farming funded by the German Federal Ministry of Education and Research (No. 03WIR4805).

Data sets for statistical inference modeling related to People Analytics. Contains various data sets from the book Handbook of Regression Modeling in People Analytics by Keith McNulty (2026).

Data and analysis from an experiment with improving touch typing speed, using the tDCS PlatoWork headset produced by PlatoScience.

R functions to access provenance information collected by rdt or rdtLite'. The information is stored inside a ProvInfo object and can be accessed through a collection of functions that will return the requested data. The exact format of the JSON created by rdt and rdtLite is described in <https://github.com/End-to-end-provenance/ExtendedProvJson>.

Allows for nonparametric regression where one assumes that the signal is given by the sum of a piecewise constant function and a smooth function. More precisely, it implements the estimator PCpluS (piecewise constant plus smooth regression estimator) from Pein and Shah (2025) <doi:10.48550/arXiv.2112.03878>.

Consider a possibly nonlinear nonparametric regression with p regressors. We provide evaluations by 13 methods to rank regressors by their practical significance or importance using various methods, including machine learning tools. Comprehensive methods are as follows. m6=Generalized partial correlation coefficient or GPCC by Vinod (2021)<doi:10.1007/s10614-021-10190-x> and Vinod (2022)<https://www.mdpi.com/1911-8074/15/1/32>. m7= a generalization of psychologists effect size incorporating nonlinearity and many variables. m8= local linear partial (dy/dxi) using the np package for kernel regressions. m9= partial (dy/dxi) using the NNS package. m10= importance measure using the NNS boost function. m11= Shapley Value measure of importance (cooperative game theory). m12 and m13= two versions of the random forest algorithm. Taraldsen's exact density for sampling distribution of correlations added.

This package provides tools to analyse animal movement and space-use patterns from telemetry data using methods derived from statistical physics. Methods span displacement-based approaches, distribution fitting, space-use metrics (including the influence of correlations on space-use), network-based community detection, and measures of entropy and predictability. The package enables characterisation of these patterns across spatial and temporal scales, including variation within and among individuals (inter- and intraspecific analyses). Outputs include interpretable metrics and visualisations to support ecological analysis and the investigation of fundamental movement processes. For applications of these methods in ecological studies see Rodrà guez et al. (2017) <doi:10.1038/s41598-017-00165-0> and Sequeira et al. (2018) <doi:10.1073/pnas.1716137115>.

Population genetic analyses for hierarchical analysis of partially clonal populations built upon the architecture of the adegenet package. Originally described in Kamvar, Tabima, and Grünwald (2014) <doi:10.7717/peerj.281> with version 2.0 described in Kamvar, Brooks, and Grünwald (2015) <doi:10.3389/fgene.2015.00208>.

Text mining of PubMed Abstracts (text and XML) from <https://pubmed.ncbi.nlm.nih.gov/>.

Fits successive Lasso models for several blocks of (omics) data with different priorities and takes the predicted values as an offset for the next block. Also offers options to deal with block-wise missingness in multi-omics data.

Estimates (and controls for) phylogenetic signal through phylogenetic eigenvectors regression (PVR) and phylogenetic signal-representation (PSR) curve, along with some plot utilities.

Descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce models, distance-based models, and rank-ordered logit models) and visualization with multidimensional preference analysis for ranking data are provided. Current, only complete rankings are supported by this package.

Allows users to find a piecewise linear regression approximation to a given continuous univariate function within a specified error tolerance. Methods based on Warwicker and Rebennack (2025) "Efficient continuous piecewise linear regression for linearising univariate non-linear functions" <doi:10.1080/24725854.2023.2299809>.

This software has evolved from fisheries research conducted at the Pacific Biological Station (PBS) in Nanaimo', British Columbia, Canada. It extends the R language to include two-dimensional plotting features similar to those commonly available in a Geographic Information System (GIS). Embedded C code speeds algorithms from computational geometry, such as finding polygons that contain specified point events or converting between longitude-latitude and Universal Transverse Mercator (UTM) coordinates. Additionally, we include C++ code developed by Angus Johnson for the Clipper library, data for a global shoreline, and other data sets in the public domain. Under the user's R library directory .libPaths()', specifically in ./PBSmapping/doc', a complete user's guide is offered and should be consulted to use package functions effectively.

Evaluate or optimize designs for nonlinear mixed effects models using the Fisher Information matrix. Methods used in the package refer to Mentré F, Mallet A, Baccar D (1997) <doi:10.1093/biomet/84.2.429>, Retout S, Comets E, Samson A, Mentré F (2007) <doi:10.1002/sim.2910>, Bazzoli C, Retout S, Mentré F (2009) <doi:10.1002/sim.3573>, Le Nagard H, Chao L, Tenaillon O (2011) <doi:10.1186/1471-2148-11-326>, Combes FP, Retout S, Frey N, Mentré F (2013) <doi:10.1007/s11095-013-1079-3> and Seurat J, Tang Y, Mentré F, Nguyen TT (2021) <doi:10.1016/j.cmpb.2021.106126>.

Generates Proteomics (PTX) quality control (QC) reports for shotgun LC-MS data analyzed with the MaxQuant software suite (from .txt files) or mzTab files (ideally from OpenMS QualityControl tool). Reports are customizable (target thresholds, subsetting) and available in HTML or PDF format. Published in J. Proteome Res., Proteomics Quality Control: Quality Control Software for MaxQuant Results (2015) <doi:10.1021/acs.jproteome.5b00780>.

Set of functions to polish content for Microsoft Word and PowerPoint into OOXML'. Polishing is the conversion of the R object into an OOXML representation of the object that can then be added to Word or PowerPoint files.

This package provides functions for reading, and in some cases writing, foreign files containing spectral data from spectrometers and their associated software, output from daylight simulation models in common use, and some spectral data repositories. As well as functions for exchange of spectral data with other R packages. Part of the r4photobiology suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

This package provides a project infrastructure with a focus on manuscript creation. Creates a project folder with a single command, containing subdirectories for specific components, templates for manuscripts, and so on.

Automate formation and evaluation of polynomial regression models. The motivation for this package is described in Polynomial Regression As an Alternative to Neural Nets by Xi Cheng, Bohdan Khomtchouk, Norman Matloff, and Pete Mohanty (<arXiv:1806.06850>).

This package provides a friendly API for sequence iteration and set comprehension.