This package provides various features to streamline and enhance the styling of interactive reactable tables with easy-to-use and highly-customizable functions and themes. Apply conditional formatting to cells with data bars, color scales, color tiles, and icon sets. Utilize custom table themes inspired by popular websites such and bootstrap themes. Apply sparkline line & bar charts (note this feature requires the dataui package which can be downloaded from <https://github.com/timelyportfolio/dataui>). Increase the portability and reproducibility of reactable tables by embedding images from the web directly into cells. Save the final table output as a static image or interactive file.

The Linear Programming via Regularized Least Squares (LPPinv) is a two-stage estimation method that reformulates linear programs as structured least-squares problems. Based on the Convex Least Squares Programming (CLSP) framework, LPPinv solves linear inequality, equality, and bound constraints by (1) constructing a canonical constraint system and computing a pseudoinverse projection, followed by (2) a convex-programming correction stage to refine the solution under additional regularization (e.g., Lasso, Ridge, or Elastic Net). LPPinv is intended for underdetermined and ill-posed linear problems, for which standard solvers fail.

This package performs aggregation of ordered lists based on the ranks using several different algorithms: Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a brute force algorithm (for small problems).

Access the Refuge API, a web-application for locating trans and intersex-friendly restrooms, including unisex and accessible restrooms. Includes data on the location of restrooms, along with directions, comments, user ratings and amenities. Coverage is global, but data is most comprehensive in the United States. See <https://www.refugerestrooms.org/api/docs/> for full API documentation.

This package provides a thin wrapper around the tiktoken-rs crate, allowing to encode text into Byte-Pair-Encoding (BPE) tokens and decode tokens back to text. This is useful to understand how Large Language Models (LLMs) perceive text.

This package provides an R interface to the JuliaBUGS.jl package (<https://github.com/TuringLang/JuliaBUGS.jl>) for Bayesian inference using the BUGS modeling language. Allows R users to run models in Julia and return results as familiar R objects. Visualization and posterior analysis are supported via the bayesplot and posterior packages.

Interactively build and explore public transit routes with r5r package via a graphical user interface in a shiny app. The underlying routing methods are described in Pereira et al. (2021) <doi:10.32866/001c.21262>.

This package provides functions for dissimilarity analysis and memory-based learning (MBL, a.k.a local modeling) in complex spectral data sets. Most of these functions are based on the methods presented in Ramirez-Lopez et al. (2013) <doi:10.1016/j.geoderma.2012.12.014>.

Encrypt R objects to a raw vector or file using modern cryptographic techniques. Password-based key derivation is with Argon2 (<https://en.wikipedia.org/wiki/Argon2>). Objects are serialized and then encrypted using XChaCha20-Poly1305 (<https://en.wikipedia.org/wiki/ChaCha20-Poly1305>) which follows RFC 8439 for authenticated encryption (<https://en.wikipedia.org/wiki/Authenticated_encryption>). Cryptographic functions are provided by the included monocypher C library (<https://monocypher.org>).

Queries data from WHOIS servers.

Computes confidence intervals for binomial or Poisson rates and their differences or ratios. Including the rate (or risk) difference ('RD') or rate ratio (or relative risk, RR') for binomial proportions or Poisson rates, and odds ratio ('OR', binomial only). Also confidence intervals for RD, RR or OR for paired binomial data, and estimation of a proportion from clustered binomial data. Includes skewness-corrected asymptotic score ('SCAS') methods, which have been developed in Laud (2017) <doi:10.1002/pst.1813> from Miettinen and Nurminen (1985) <doi:10.1002/sim.4780040211> and Gart and Nam (1988) <doi:10.2307/2531848>, and in Laud (2025, under review) for paired proportions. The same score produces hypothesis tests that are improved versions of the non-inferiority test for binomial RD and RR by Farrington and Manning (1990) <doi:10.1002/sim.4780091208>, or a generalisation of the McNemar test for paired data. The package also includes MOVER methods (Method Of Variance Estimates Recovery) for all contrasts, derived from the Newcombe method but with options to use equal-tailed intervals in place of the Wilson score method, and generalised for Bayesian applications incorporating prior information. So-called exact methods for strictly conservative coverage are approximated using continuity adjustments, and the amount of adjustment can be selected to avoid over-conservative coverage. Also includes methods for stratified calculations (e.g. meta-analysis), either with fixed effect assumption (matching the CMH test) or incorporating stratum heterogeneity.

Creation, manipulation, simulation of linear Gaussian Bayesian networks from text files and more...

Robust parameter estimation and prediction of Gaussian stochastic process emulators. It allows for robust parameter estimation and prediction using Gaussian stochastic process emulator. It also implements the parallel partial Gaussian stochastic process emulator for computer model with massive outputs See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics; Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics.

Simulation of several fractional and multifractional processes. Includes Brownian and fractional Brownian motions, bridges and Gaussian Haar-based multifractional processes (GHBMP). Implements the methods from Ayache, Olenko and Samarakoon (2025) <doi:10.48550/arXiv.2503.07286> for simulation of GHBMP. Estimation of Hurst functions and local fractal dimension. Clustering realisations based on the Hurst functions. Several functions to estimate and plot geometric statistics of the processes and time series. Provides a shiny application for interactive use of the functions from the package.

Utilities for accessing RePEc (Research Papers in Economics) through a RESTful API. You can request a code and get detailed information at the following page: <https://ideas.repec.org/api.html>.

KEEL is a popular Java software for a large number of different knowledge data discovery tasks. Furthermore, RKEEL is a package with a R code layer between R and KEEL', for using KEEL in R code. This package includes the datasets from KEEL in .dat format for its use in RKEEL package. For more information about KEEL', see <http://www.keel.es/>.

Mediation analysis for multiple mediators by penalized structural equation models with different types of penalties depending on whether there are multiple mediators and only one exposure and one outcome variable (using sparse group lasso) or multiple exposures, multiple mediators, and multiple outcome variables (using lasso, L1, penalties).

Estimates the rank intraclass correlation coefficient (ICC) for clustered continuous and ordinal data. See Tu et al. (2023) <DOI:10.1002/sim.9864> for details.

Allows users to import data files containing heartbeat positions in the most broadly used formats, to remove outliers or points with unacceptable physiological values present in the time series, to plot HRV data, and to perform time domain, frequency domain and nonlinear HRV analysis. See Garcia et al. (2017) <DOI:10.1007/978-3-319-65355-6>.

ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.

Used for generating randomized community matrices under strict range cohesion. The package can handle data where species occurrence are recorded across sites ordered along gradients such as elevation and latitude, as well as species occurrences recorded on spatial grids with known geographic coordinates.

The minimum covariance determinant estimator is used to perform robust quadratic discriminant analysis, including cross-validation. References: Friedman J., Hastie T. and Tibshirani R. (2009). "The elements of statistical learning", 2nd edition. Springer, Berlin. <doi:10.1007/978-0-387-84858-7>.

This package provides a series of functions to aid in repeated tasks for Rmd documents. All details are to my personal preference, though I am happy to add flexibility if there are use cases I am missing. I will continue updating with new functions as I add utility functions for myself.

Translation of the MATLAB program Carb (Nathan and Mauz 2008 <DOI:10.1016/j.radmeas.2007.12.012>; Mauz and Hoffmann 2014) for dose rate modelling for carbonate-rich samples in the context of trapped charged dating (e.g., luminescence dating) applications.