Extracts information from text using lookup tables of regular expressions. Each text entry is compared against all patterns, and all matching patterns and their corresponding substrings are returned. If a text entry matches multiple patterns, multiple rows are generated to capture each match. This approach enables comprehensive pattern coverage when processing large or complex text datasets.

The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The rdpower package provides tools to perform power, sample size, and minimum detectable effect calculations in RD designs: rdpower() calculates the power of an RD design, rdsampsi() calculates the required sample size to achieve a desired power, and rdmde() calculates minimum detectable effects. See Cattaneo, Titiunik and Vazquez-Bare (2019) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2019_Stata.pdf> for further methodological details.

Adds subtotal rows / sections (a la the SAS Proc Tabulate All option) to a Group By output by running a series of Group By functions with partial sets of the same variables and combining the results with the original. Can be used to add comprehensive information to a data report or to quickly aggregate Group By outputs used to gain a greater understanding of data.

This package provides functions to safely map from a vector of keys to a vector of values, determine properties of a given relation, or ensure a relation conforms to a given type, such as many-to-many, one-to-many, injective, surjective, or bijective. Permits default return values for use similar to a vectorised switch statement, as well as safely handling large vectors, NAs, and duplicate mappings.

This package provides a unified framework for designing, simulating, and analyzing implementation rollout trials, including stepped wedge, sequential rollout, head-to-head, multi-condition, and rollout implementation optimization designs. The package enables users to flexibly specify rollout schedules, incorporate site-level and nested data structures, generate outcomes under rich hierarchical models, and evaluate analytic strategies through simulation-based power analysis. By separating data generation from model fitting, the tools support assessment of bias, Type I error, and robustness to model misspecification. The workflow integrates with standard mixed-effects modeling approaches and the tidyverse ecosystem, offering transparent and reproducible tools for implementation scientists and applied statisticians.

Client for Rserve, allowing to connect to Rserve instances and issue commands.

Implementation of the race/ethnicity prediction method, described in "rethnicity: An R package for predicting ethnicity from names" by Fangzhou Xie (2022) <doi:10.1016/j.softx.2021.100965> and "Rethnicity: Predicting Ethnicity from Names" by Fangzhou Xie (2021) <doi:10.48550/arXiv.2109.09228>.

Providing wrapper functions to implement Bayesian analysis in JAGS. Some major features include monitoring convergence of a MCMC model using Rubin and Gelman Rhat statistics, automatically running a MCMC model till it converges, and implementing parallel processing of a MCMC model for multiple chains.

This is a wrapper function for image(), which makes reasonable raster plots with nice axis and other useful features.

Sundry discrete probability distributions and helper functions.

This package provides a supportive collection of functions for gathering and plotting treatment ranking metrics after network meta-analysis.

Simulate random matrices and ensembles and compute their eigenvalue spectra and dispersions.

Add-in to the RJDemetra package on seasonal adjustments. It allows to produce dashboards to summarise models and quickly check the quality of the seasonal adjustment.

This package provides R-squared values and standardized regression coefficients for linear models applied to multiply imputed datasets as obtained by mice'. Confidence intervals, zero-order correlations, and alternative adjusted R-squared estimates are also available. The methods are described in Van Ginkel and Karch (2024) <doi:10.1111/bmsp.12344> and in Van Ginkel (2020) <doi:10.1007/s11336-020-09696-4>.

Adaptation of the Matlab tsEVA toolbox developed by Lorenzo Mentaschi available here: <https://github.com/menta78/tsEva>. It contains an implementation of the Transformed-Stationary (TS) methodology for non-stationary extreme value Analysis (EVA) as described in Mentaschi et al. (2016) <doi:10.5194/hess-20-3527-2016>. In synthesis this approach consists in: (i) transforming a non-stationary time series into a stationary one to which the stationary extreme value theory can be applied; and (ii) reverse-transforming the result into a non-stationary extreme value distribution. RtsEva offers several options for trend estimation (mean, extremes, seasonal) and contains multiple plotting functions displaying different aspects of the non-stationarity of extremes.

Listings are often part of the submission of clinical trial data in regulatory settings. We provide a framework for the specific formatting features often used when displaying large datasets in that context.

Helper function to install packages for R using an external requirements.txt or a string containing diverse packages from several resources like Github or CRAN.

This package provides a Tidy implementation of grouping sets', rollup and cube - extensions of the group_by clause that allow for computing multiple group_by clauses in a single statement. For more detailed information on these functions, please refer to "Enhanced Aggregation, Cube, Grouping and Rollup" <https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C+Grouping+and+Rollup>.

This package provides a collection of tools to import and structure the (currently) single-season event, game-log, roster, and schedule data available from <https://www.retrosheet.org>. In particular, the event (a.k.a. play-by-play) files can be especially difficult to parse. This package does the parsing on those files, returning the requested data in the most practical R structure to use for sabermetric or other analyses.

This package provides a wrapper for Jagger, a morphological analyzer proposed in Yoshinaga (2023) <arXiv:2305.19045>. Jagger uses patterns derived from morphological dictionaries and training data sets and applies them from the beginning of the input. This simultaneous and deterministic process enables it to effectively perform tokenization, POS tagging, and lemmatization.

Univariate and multivariate versions of risk-based control charts. Univariate versions of control charts, such as the risk-based version of X-bar, Moving Average (MA), Exponentially Weighted Moving Average Control Charts (EWMA), and Cumulative Sum Control Charts (CUSUM) charts. The risk-based version of the multivariate T2 control chart. Plot and summary functions. Kosztyan et. al. (2016) <doi:10.1016/j.eswa.2016.06.019>.

This package creates the radar-boxplot, a plot that was created by the author during his Ph.D. in forest resources. The radar-boxplot is a visualization feature suited for multivariate classification/clustering. It provides an intuitive deep understanding of the data.

This package implements various tests, visualizations, and metrics for diagnosing convergence of MCMC chains in phylogenetics. It implements and automates many of the functions of the AWTY package in the R environment, as well as a host of other functions. Warren, Geneva, and Lanfear (2017), <doi:10.1093/molbev/msw279>.

Bundles the datasets and functions featured in Philip H. Pollock and Barry C. Edwards<https://edge.sagepub.com/pollock>, "An R Companion to Political Analysis, 3rd Edition," Thousand Oaks, CA: Sage Publications.