This tool enables the user to choose a randomization procedure based on sound scientific criteria. It comprises the generation of randomization sequences as well the assessment of randomization procedures based on carefully selected criteria. Furthermore, randomizeR provides a function for the comparison of randomization procedures.

Implementation of JQuery <https://jquery.com> and CSS styles to allow easy incorporation of various social media elements on a page. The elements include addition of share buttons or connect with us buttons or hyperlink buttons to Shiny applications or dashboards and Rmarkdown documents.Sharing capability on social media platforms including Facebook <https://www.facebook.com>, Linkedin <https://www.linkedin.com>, X/Twitter <https://x.com>, Tumblr <https://www.tumblr.com>, Pinterest <https://www.pinterest.com>, Whatsapp <https://www.whatsapp.com>, Reddit <https://www.reddit.com>, Baidu <https://www.baidu.com>, Blogger <https://www.blogger.com>, Weibo <https://www.weibo.com>, Instagram <https://www.instagram.com>, Telegram <https://www.telegram.me>, Youtube <https://www.youtube.com>.

Understanding heterogeneous causal effects based on pretreatment covariates is a crucial step in modern empirical work in data science. Building on the recent developments in Calonico et al (2025) <doi:10.48550/arXiv.2503.13696>, this package provides tools for estimation and inference of heterogeneous treatment effects in Regression Discontinuity (RD) Designs. The package includes two main commands: rdhte to conduct estimation and robust bias-corrected inference for conditional RD treatment effects (given choice of bandwidth parameter); rdbwhte', which implements automatic bandwidth selection methods; and rdhte_lincom to test linear combinations of parameters.

This package provides functions for simulating Markov chains using the Barker proposal to compute Markov chain Monte Carlo (MCMC) estimates of expectations with respect to a target distribution on a real-valued vector space. The Barker proposal, described in Livingstone and Zanella (2022) <doi:10.1111/rssb.12482>, is a gradient-based MCMC algorithm inspired by the Barker accept-reject rule. It combines the robustness of simpler MCMC schemes, such as random-walk Metropolis, with the efficiency of gradient-based methods, such as the Metropolis adjusted Langevin algorithm. The key function provided by the package is sample_chain(), which allows sampling a Markov chain with a specified target distribution as its stationary distribution. The chain is sampled by generating proposals and accepting or rejecting them using a Metropolis-Hasting acceptance rule. During an initial warm-up stage, the parameters of the proposal distribution can be adapted, with adapters available to both: tune the scale of the proposals by coercing the average acceptance rate to a target value; tune the shape of the proposals to match covariance estimates under the target distribution. As well as the default Barker proposal, the package also provides implementations of alternative proposal distributions, such as (Gaussian) random walk and Langevin proposals. Optionally, if BridgeStan's R interface <https://roualdes.us/bridgestan/latest/languages/r.html>, available on GitHub <https://github.com/roualdes/bridgestan>, is installed, then BridgeStan can be used to specify the target distribution to sample from.

This package implements the Regional Association Score (RAS) method for genome-wide association studies (GWAS). For each single nucleotide polymorphism (SNP), RAS quantifies the strength of association within its surrounding genomic region, arranges these regional scores along the chromosome into a signal profile, and applies changepoint detection to locate association regions, improving statistical power while controlling the false positive rate. The method is described in Jiang and Zhang (2025) <doi:10.1073/pnas.2419721122>.

Interface for loading data from ActiveCampaign API v3 <https://developers.activecampaign.com/reference>. Provide functions for getting data by deals, contacts, accounts, campaigns and messages.

This package provides functions to complete three-dimensional rock fabric and strain analyses following the Rf Phi, Fry, and normalized Fry methods. Also allows for plotting of results and interactive 3D visualization functionality.

Installs OpenCV for use by other packages. OpenCV <https://opencv.org/> is library of programming functions mainly aimed at real-time computer vision. This Lite version installs the stable base version of OpenCV and some of its experimental externally contributed modules. It does not provide R bindings directly.

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.

Utilities used across packages of the rtemis ecosystem. Includes the msg() messaging system and the fmt() formatting system. Provides a library of S7 properties, test_* functions that return logical values, check_* functions that throw informative errors, and clean_* functions that return validated and coerced values. This code began as part of the rtemis package (<doi:10.32614/CRAN.package.rtemis>).

Prediction of claim counts using the feature based development factors introduced in the manuscript Hiabu M., Hofman E. and Pittarello G. (2023) <doi:10.48550/arXiv.2312.14549>. Implementation of Neural Networks, Extreme Gradient Boosting, and Cox model with splines to optimise the partial log-likelihood of proportional hazard models.

Fit statistical models based on the Dawid-Skene model - Dawid and Skene (1979) <doi:10.2307/2346806> - to repeated categorical rating data. Full Bayesian inference for these models is supported through the Stan modelling language. rater also allows the user to extract and plot key parameters of these models.

This package provides a proof of concept implementation of regularized non-negative matrix factorization optimization. A non-negative matrix factorization factors non-negative matrix Y approximately as L R, for non-negative matrices L and R of reduced rank. This package supports such factorizations with weighted objective and regularization penalties. Allowable regularization penalties include L1 and L2 penalties on L and R, as well as non-orthogonality penalties. This package provides multiplicative update algorithms, which are a modification of the algorithm of Lee and Seung (2001) <http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf>, as well as an additive update derived from that multiplicative update. See also Pav (2024) <doi:10.48550/arXiv.2410.22698>.

Assists in statistical model building to find optimal and semi-optimal higher order interactions and best subsets. Uses the lm(), glm(), and other R functions to fit models generated from a feasible solution algorithm. Discussed in Subset Selection in Regression, A Miller (2002). Applied and explained for least median of squares in Hawkins (1993) <doi:10.1016/0167-9473(93)90246-P>. The feasible solution algorithm comes up with model forms of a specific type that can have fixed variables, higher order interactions and their lower order terms.

This package provides an interface for working with RStudio preference files to modify settings and addin shortcuts without using point-and-click option menus. Useful for ensuring a unified experience across devices and for enforcing best practices. Also exposes some settings not available in the Global Options dialog.

Estimates the rearranged dependence measure ('RDM') of two continuous random variables for different underlying measures. Furthermore, it provides a method to estimate the (SI)-rearrangement copula using empirical checkerboard copulas. It is based on the theoretical results presented in Strothmann et al. (2022) <arXiv:2201.03329> and Strothmann (2021) <doi:10.17877/DE290R-22733>.

This package provides an R interface to the ROCEEH Out of Africa Database (ROAD) (<https://www.roceeh.uni-tuebingen.de/roadweb/smarty_road_simple_search.php>), a comprehensive resource for archaeological, anthropological, paleoenvironmental and geographic data from Africa and Eurasia dating from 3,000,000 to 20,000 years BP. The package allows users to retrieve data from the online database at different levels of detail and customize search requests. Functions return data frame objects compatible with other R packages used in prehistoric and paleoenvironmental science, supporting reproducible workflows as an input provider.

This package contains three functions that query AuriQ Systems Essentia Database and return the results in R. essQuery takes a single Essentia command and captures the output in R, where you can save the output to a dataframe or stream it directly into additional analysis. read.essentia takes an Essentia script and captures the output csv data into R, where you can save the output to a dataframe or stream it directly into additional analysis. capture.essentia takes a file containing any number of Essentia commands and captures the output of the specified statements into R dataframes. Essentia can be downloaded for free at http://www.auriq.com/documentation/source/install/index.html.

Toolbox for remote sensing image processing and analysis such as calculating spectral indexes, principal component transformation, unsupervised and supervised classification or fractional cover analyses.

Client for various CrossRef APIs', including metadata search with their old and newer search APIs', get citations in various formats (including bibtex', citeproc-json', rdf-xml', etc.), convert DOIs to PMIDs', and vice versa', get citations for DOIs', and get links to full text of articles when available.

R implementation of SIDES-based subgroup search algorithms (Lipkovich et al. (2017) <doi:10.1002/sim.7064>).

Bayesian regression with functional data, including regression with scalar, survival, or functional outcomes. The package allows regression with scalar and functional predictors. Methods are described in Jiang et al. (2025) "Tutorial on Bayesian Functional Regression Using Stan" <doi:10.1002/sim.70265>.

This package provides a model agnostic tool for white-box model trained on features extracted from a black-box model. For more information see: Gosiewska et al. (2020) <doi:10.1016/j.dss.2021.113556>.

Tests linear regressions for significance reversal through leave-one(multiple)-out.