Which day a week starts depends heavily on the either the local or professional context. This package is designed to be a lightweight solution to easily switching between week-based date definitions.

Easily processes batches of univariate or multivariate regression models. Returns results in a tidy format and generates visualization plots for straightforward interpretation (Wang, Shixiang, et al. (2025) <DOI:10.1002/mdr2.70028>).

Allows Brownian motion, fractional Brownian motion, and integrated Ornstein-Uhlenbeck process components to be added to linear and non-linear mixed effects models using the structures and methods of the nlme package.

Implementation of the Cluster Estimated Standard Errors (CESE) proposed in Jackson (2020) <DOI:10.1017/pan.2019.38> to compute clustered standard errors of linear coefficients in regression models with grouped data.

This package contains an implementation of the d-variable Hilbert Schmidt independence criterion and several hypothesis tests based on it, as described in Pfister et al. (2017) <doi:10.1111/rssb.12235>.

This package provides a high level API to interface over sources storing distance, dissimilarity, similarity matrices with matrix style extraction, replacement and other utilities. Currently, in-memory dist object backend is supported.

Improves the concept of multivariate range boxes, which is highly susceptible for outliers and does not consider the distribution of the data. The package uses dynamic range boxes to overcome these problems.

This package provides functions for creating, analyzing, and visualizing event study models using fixed-effects regression. Supports staggered adoption, multiple confidence intervals, flexible clustering, and panel/time transformations in a simple workflow.

Emulates a Forth programming environment with added features to interface between R and Forth'. Implements most of the functionality described in the original "Starting Forth" textbook <https://www.forth.com/starting-forth/>.

Scans all directories and subdirectories of a path for code snippets, R scripts, R Markdown, PDF or text files containing a specific pattern. Files found can be copied to a new folder.

Light procedures for learning Global Vector Autoregression model (GVAR) of Pesaran, Schuermann and Weiner (2004) <DOI:10.1198/073500104000000019> and Dees, di Mauro, Pesaran and Smith (2007) <DOI:10.1002/jae.932>.

This package provides a collection of GIS (Geographic Information System) functions in R, created for use in Statistics Norway. The functions are primarily related to network analysis on the Norwegian road network.

This package provides access to Uber's H3 library for geospatial indexing via its JavaScript transpile h3-js <https://github.com/uber/h3-js> and V8 <https://github.com/jeroen/v8>.

This package provides tools that extend the functionality of the RODBC package to work with Microsoft SQL Server databases. Makes it easier to browse the database and examine individual tables and views.

Fits the MESSI, hard constraint, and unconstrained models in Boss et al. (2023) <doi:10.48550/arXiv.2306.17347> for mediation analyses with external summary-level information on the total effect.

Create variable width bar charts i.e. "bar mekko" charts to include important quantitative context. Closely related to mosaic, spine (or spinogram), matrix, submarine, olympic, Mondrian or product plots and tree maps.

Estimation, inference and forecasting using the Bayesian approach for multivariate threshold autoregressive (TAR) models in which the distribution used to describe the noise process belongs to the class of Gaussian variance mixtures.

Estimates out-of-sample R² through bootstrap or cross-validation as a measure of predictive performance. In addition, a standard error for this point estimate is provided, and confidence intervals are constructed.

Enables the removal of training data from fitted R models while retaining predict functionality. The purged models are more portable as their memory footprints do not scale with the training sample size.

This package provides functions and datasets from Jones, O.D., R. Maillardet, and A.P. Robinson. 2014. An Introduction to Scientific Programming and Simulation, Using R. 2nd Ed. Chapman And Hall/CRC.

This package provides functionality for simulating data generation processes across various spatial regression models, conceptually aligned with the dgp module of the Python library spreg <https://pysal.org/spreg/api.html#dgp>.

Calculate the Standardized Precipitation Index (SPI) for monitoring drought, using Artificial Intelligence techniques (SPIGA) and traditional numerical technique Maximum Likelihood (SPIML). For more information see: http://drought.unl.edu/monitoringtools/downloadablespiprogram.aspx.

This package provides a suite of plots for displaying variable importance and two-way variable interaction jointly. Can also display partial dependence plots laid out in a pairs plot or zenplots style.

This package provides a toolkit to set up an R data package in a consistent structure. Automates tasks like tidy data export, data dictionary documentation, README and website creation, and citation management.