This package provides a Shiny input widget, pasteBoxInput, that allows users to paste images directly into a Shiny application. The pasted images are captured as Base64 encoded strings and can be used within the application for various purposes, such as display or further processing. This package is particularly useful for applications that require easy and quick image uploads without the need for traditional file selection dialog boxes.

This package provides a fully legal chess move generator and game engine implemented in C++17 via Rcpp'. Provides FEN (Forsyth-Edwards Notation) parsing, PGN (Portable Game Notation) replay, position feature enrichment, and a multi-game registry backed by a bitboard representation.

Perform tasks commonly encountered when preparing and analysing demographic data. Some functions are intended for end users, and others for developers. Includes functions for working with life tables.

This package provides a probabilistic framework that integrates Data Envelopment Analysis (DEA) (Banker et al., 1984) <doi:10.1287/mnsc.30.9.1078> with machine learning classifiers (Kuhn, 2008) <doi:10.18637/jss.v028.i05> to estimate both the (in)efficiency status and the probability of efficiency for decision-making units. The approach trains predictive models on DEA-derived efficiency labels (Charnes et al., 1985) <doi:10.1016/0304-4076(85)90133-2>, enabling explainable artificial intelligence (XAI) workflows with global and local interpretability tools, including permutation importance (Molnar et al., 2018) <doi:10.21105/joss.00786>, Shapley value explanations (Strumbelj & Kononenko, 2014) <doi:10.1007/s10115-013-0679-x>, and sensitivity analysis (Cortez, 2011) <https://CRAN.R-project.org/package=rminer>. The framework also supports probability-threshold peer selection and counterfactual improvement recommendations for benchmarking and policy evaluation. The probabilistic efficiency framework is detailed in González-Moyano et al. (2025) "Probability-based Technical Efficiency Analysis through Machine Learning", in review for publication.

Computes Owen's T function or the bivariate normal integral using one of the following methods: the modified Euler's arctangent series, tetrachoric series, or Vasicek's series. For the methodology, see Komelj, J. (2023) <doi:10.4236/ajcm.2023.134026>, or the revised postprint version with corrected contents and improved typography at <doi:10.48550/arXiv.2312.00011>. Also see the alternative approach by Vasicek, O. A. (1998) <doi:10.21314/JCF.1998.015>.

In ancient Roman mythology, Pluto was the ruler of the underworld and presides over the afterlife. Pluto was frequently conflated with Plutus', the god of wealth, because mineral wealth was found underground. When plotting with R, you try once, twice, practice again and again, and finally you get a pretty figure you want. It's a plot tour', a tour about repetition and reward. Hope plutor helps you on the tour!

This package provides a unified framework for generating, submitting, and analyzing pairwise comparisons of writing quality using large language models (LLMs). The package supports live and/or batch evaluation workflows across multiple providers ('OpenAI', Anthropic', Google Gemini', Together AI', and locally-hosted Ollama models), includes bias-tested prompt templates and a flexible template registry, and offers tools for constructing forward and reversed comparison sets to analyze consistency and positional bias. The package additionally supports adaptive pairing workflows that iteratively select comparisons based on model uncertainty to improve ranking efficiency. Results can be modeled using frequentist or Bayesian Bradleyâ Terryâ Luce models (Bradley & Terry, 1952 <doi:10.2307/2334029>; see also Caron & Doucet, 2012 <doi:10.1080/10618600.2012.638220>) or Elo rating methods (see Clark et al., 2018 <doi:10.1371/journal.pone.0190393>) to derive writing quality scores. For information on pairwise comparisons and comparative judgement, see Thurstone (1927) <doi:10.1037/h0070288> and Heldsinger & Humphry (2010) <doi:10.1007/BF03216919>.

An implementation of a non-parametric statistical model using a parallelised Monte Carlo sampling scheme. The method implemented in this package allows non-parametric inference to be regularized for small sample sizes, while also being more accurate than approximations such as variational Bayes. The concentration parameter is an effective sample size parameter, determining the faith we have in the model versus the data. When the concentration is low, the samples are close to the exact Bayesian logistic regression method; when the concentration is high, the samples are close to the simplified variational Bayes logistic regression. The method is described in full in the paper Lyddon, Walker, and Holmes (2018), "Nonparametric learning from Bayesian models with randomized objective functions" <doi:10.48550/arXiv.1806.11544>.

This package provides a convenient framework for aggregating and disaggregating continuously varying parameters (for example, case fatality ratio, with age) for proper parametrization of lower-resolution compartmental models (for example, with broad age categories) and subsequent upscaling of model outputs to high resolution (for example, as needed when calculating age-sensitive measures like years-life-lost).

This package contains functions for data preparation, prediction of transition probabilities, estimating semi-parametric regression models and for implementing nonparametric estimators for other quantities. See Meira-Machado and Roca-Pardiñas (2011) <doi:10.18637/jss.v038.i03>.

This package provides tools for analyzing and optimizing PTSD (Post-Traumatic Stress Disorder) diagnostic criteria using PCL-5 (PTSD Checklist for DSM-5) and CAPS-5 (Clinician-Administered PTSD Scale for DSM-5) data. Functions identify optimal subsets of PCL-5 items that maintain diagnostic accuracy while reducing assessment burden. Includes tools for both hierarchical (cluster-based) and non-hierarchical symptom combinations, calculation of diagnostic metrics, and comparison with standard DSM-5 criteria. Redundancy analysis quantifies how many item subsets perform equivalently, through plateau sizes and bootstrap stability, and reports item selection against the chance baseline of the searched candidate space. A transport layer allows a site to evaluate subsets derived elsewhere and return only aggregate summaries, supporting multi-site validation without sharing individual-level data. Model validation is conducted using holdout and cross-validation methods to assess robustness and generalizability of the results. For more details see Weidmann et al. (2025) <doi:10.31219/osf.io/6rk72_v1>.

Examines the characteristics of a data frame and a formula to automatically choose the most suitable type of plot out of the following supported options: scatter, violin, box, bar, density, hexagon bin, spine plot, and heat map. The aim of the package is to let the user focus on what to plot, rather than on the "how" during exploratory data analysis. It also automates handling of observation weights, logarithmic axis scaling, reordering of factor levels, and overlaying smoothing curves and median lines. Plots are drawn using ggplot2'.

This package provides programmatic access to GitHub API with a focus on project management. Key functionality includes setting up issues and milestones from R objects or YAML configurations, querying outstanding or completed tasks, and generating progress updates in tables, charts, and RMarkdown reports. Useful for those using GitHub in personal, professional, or academic settings with an emphasis on streamlining the workflow of data analysis projects.

Bayesian supervised predictive classifiers, hypothesis testing, and parametric estimation under Partition Exchangeability are implemented. The two classifiers presented are the marginal classifier (that assumes test data is i.i.d.) next to a more computationally costly but accurate simultaneous classifier (that finds a labelling for the entire test dataset at once based on simultanous use of all the test data to predict each label). We also provide the Maximum Likelihood Estimation (MLE) of the only underlying parameter of the partition exchangeability generative model as well as hypothesis testing statistics for equality of this parameter with a single value, alternative, or multiple samples. We present functions to simulate the sequences from Ewens Sampling Formula as the realisation of the Poisson-Dirichlet distribution and their respective probabilities.

This package provides tools for processing, analyzing, and visualizing spectral data collected from 3D laser-based scanning systems. Supports applications in agriculture, forestry, environmental monitoring, industrial quality control, and biomedical research. Enables evaluation of plant growth, productivity, resource efficiency, disease management, and pest monitoring. Includes statistical methods for extracting insights from multispectral and hyperspectral data and generating publication-ready visualizations. See Zieschank & Junker (2023) <doi:10.3389/fpls.2023.1141554> and Saric et al. (2022) <doi:10.1016/J.TPLANTS.2021.12.003> for related work.

Defines functions to describe regression models using only pre-computed summary statistics (i.e. means, variances, and covariances) in place of individual participant data. Possible models include linear models for linear combinations, products, and logical combinations of phenotypes. Implements methods presented in Wolf et al. (2021) <doi:10.3389/fgene.2021.745901> Wolf et al. (2020) <doi:10.1142/9789811215636_0063> and Gasdaska et al. (2019) <doi:10.1142/9789813279827_0036>.

Puzzle game that can be played in the R console. Restore the pixel art by shifting rows.

This R package allows the determination of some distributions of the voters power when passing laws in weighted voting situations.

Applies cartographic projections to spatial data frames containing geographic coordinates. Projection methods are based on the D3.js ecosystem <doi:10.1109/TVCG.2011.185> and use spherical geometry rather than ellipsoidal geodesic models.

Parallel Constraint Satisfaction (PCS) models are an increasingly common class of models in Psychology, with applications to reading and word recognition (McClelland & Rumelhart, 1981; \doi10.1037/0033-295X.88.5.375), judgment and decision making (Glöckner & Betsch, 2008 \doi10.1017/S1930297500002424; Glöckner, Hilbig, & Jekel, 2014 \doi10.1016/j.cognition.2014.08.017), and several other fields. In each of these fields, they provide a quantitative model of psychological phenomena, with precise predictions regarding choice probabilities, decision times, and often the degree of confidence. This package provides the necessary functions to create and simulate basic Parallel Constraint Satisfaction networks within R.

Given k populations (can be in thousands), what is the probability that a given subset of size t contains the true top t populations? This package finds this probability and offers three tuning parameters (G, d, L) to relax the definition.

An R package for polygenic trait analysis.

Infer the genetic composition of individuals in terms of haplotype dosages for a haploblock, based on bi-allelic marker dosages, for any ploidy level. Reference: Voorrips and Tumino: PolyHaplotyper: haplotyping in polyploids based on bi-allelic marker dosage data. Submitted to BMC Bioinformatics (2021).

Estimates two-level multilevel linear model and two-level multivariate linear multilevel model with weights following Probability Weighted Iterative Generalised Least Squares approach. For details see Veiga et al.(2014) <doi:10.1111/rssc.12020>.