This package provides functions to support data cleaning, evaluation, and description, developed for integration with Maelstrom Research software tools. madshapR provides functions primarily to evaluate and manipulate datasets and data dictionaries in preparation for data harmonization with the package Rmonize and to facilitate integration and transfer between RStudio servers and secure Opal environments. madshapR functions can be used independently but are optimized in conjunction with â Rmonizeâ functions for streamlined and coherent harmonization processing.

Facilitate frequentist and Bayesian meta-analysis of diagnosis and prognosis research studies. It includes functions to summarize multiple estimates of prediction model discrimination and calibration performance (Debray et al., 2019) <doi:10.1177/0962280218785504>. It also includes functions to evaluate funnel plot asymmetry (Debray et al., 2018) <doi:10.1002/jrsm.1266>. Finally, the package provides functions for developing multivariable prediction models from datasets with clustering (de Jong et al., 2021) <doi:10.1002/sim.8981>.

Makes it possible to create an internally consistent repository consisting of selected packages from CRAN-like repositories. The user specifies a set of desired packages, and miniCRAN recursively reads the dependency tree for these packages, then downloads only this subset. The user can then install packages from this repository directly, rather than from CRAN. This is useful in production settings, e.g. server behind a firewall, or remote locations with slow (or zero) Internet access.

Speeds up the process of loading raw data from MBA (Multiplex Bead Assay) examinations, performs quality control checks, and automatically normalises the data, preparing it for more advanced, downstream tasks. The main objective of the package is to create a simple environment for a user, who does not necessarily have experience with R language. The package is developed within the project of the same name - PvSTATEM', which is an international project aiming for malaria elimination.

Lightweight wrappers around R CMD INSTALL', R CMD check', R CMD build', win-builder uploads, and CRAN submission. Provides functions for installing, loading, checking, building, and submitting R packages with minimal dependencies (only curl for uploads). Background on R package development is in Wickham and Bryan (2023, ISBN:9781098134945), "Writing R Extensions" <https://cran.r-project.org/doc/manuals/R-exts.html>, and the CRAN Repository Policy <https://cran.r-project.org/web/packages/policies.html>.

Index data frames and document collections and run fast full-text search entirely on your machine. tantivyr wraps the Tantivy Rust search engine (a Lucene'-inspired library) to provide BM25 ranking, structured filters, snippet highlighting and incremental updates over an on-disk or in-memory index. First-class support is provided for stemming and stop words in Portuguese and English, making it well suited to public documents, news clippings, extracted PDF text, transcripts and legal acts.

Using matrix layout to visualize the unique, common, or individual contribution of each predictor (or matrix of predictors) towards explained variation on different models. These contributions were derived from variation partitioning (VP) and hierarchical partitioning (HP), applying the algorithm of "Lai et al. (2022) Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.Methods in Ecology and Evolution, 13: 782-788 <doi:10.1111/2041-210X.13800>".

Fits transition-specific cause-specific random survival forests on a clock-reset duration scale for acyclic, non-recurrent multistate processes. Entry-conditioned state-occupation probabilities are assembled from predicted cumulative hazards by semi-Markov entry-mass and sojourn convolution on a validated regular grid. The one-row-per-subject interface supports one common initial state, one recorded entry per state, baseline time-fixed covariates, competing exits, and independent right censoring. Left truncation, recurrent visits, directed cycles, time-dependent covariates, and ongoing-sojourn dynamic prediction are not supported. The package also provides calendar-time Aalen-Johansen point estimates as a covariate-free descriptive baseline, transition-specific permutation importance, genuine ranger edge OOB concordance, and patient-level cross-validated IPCW state-probability scoring. Methods are described in Ishwaran et al. (2008) <doi:10.1214/08-AOAS169> for random survival forests, Putter et al. (2007) <doi:10.1002/sim.2712> for multistate competing risks decomposition, and Aalen and Johansen (1978) <https://www.jstor.org/stable/4615704> for the nonparametric estimator.

PCA done by eigenvalue decomposition of a data correlation matrix, here it automatically determines the number of factors by eigenvalue greater than 1 and it gives the uncorrelated variables based on the rotated component scores, Such that in each principal component variable which has the high variance are selected. It will be useful for non-statisticians in selection of variables. For more information, see the <http://www.ijcem.org/papers032013/ijcem_032013_06.pdf> web page.

An integrated set of functions for building, analyzing, and visualizing Analytic Hierarchy Process (AHP) models, designed to support structured decision-making in consultancy, policy analysis, and research (Bose 2022 <doi:10.1002/mcda.1784>; Bose 2023 <doi:10.1002/mcda.1821>). In addition to tools for assessing and improving the consistency of pairwise comparison matrices (PCMs), the package supports full-hierarchy weight computation, intuitive tree-based visualization, sensitivity analysis, along with convenient PCM generation from user preferences.

Biostatistical and clinical data analysis, including descriptive statistics, exploratory data analysis, sample size and power calculations, statistical inference, and data visualization. Normality tests are implemented following Mishra et al. (2019) <doi:10.4103/aca.ACA_157_18>, omnibus test procedures are based on Blanca et al. (2017) <doi:10.3758/s13428-017-0918-2> and Field et al. (2012, ISBN:9781446200469), while sample size and power calculation methods follow Chow et al. (2017) <doi:10.1201/9781315183084>.

This package provides Bayesian age estimation for bioarchaeological skeletal data using ordinal probit regression models implemented in JAGS and NIMBLE'. The package is designed to handle multiple ordinal traits of adult individuals and incorporates a Gompertz prior on age to reflect population-level mortality. It accounts for estimation uncertainties and supports full customization of model parameters and Markov Chain Monte Carlo settings. For more details see Müller-Scheeà el et al. (2026) <doi:10.1002/ajpa.70289>.

This package provides a collection of utility functions for biostatistics, agricultural trial planning, and experimental design. Key features include generating experimental designs (like Latin Square, Alpha-Lattice by Patterson and Williams (1976) <doi:10.2307/2335087>, and Factorial), fieldbook creation, layout sketching, QR code-based label generation, and descriptive statistical tools to easily handle most common descriptive statistics for quantitative variables as described by Field, A., Miles, J., & Field, Z. (2012, ISBN:978-1-4462-0045-2).

Color palettes for all people, including those with color vision deficiency. Popular color palette series have been organized by type and have been scored on several properties such as color-blind-friendliness and fairness (i.e. do colors stand out equally?). Own palettes can also be loaded and analysed. Besides the common palette types (categorical, sequential, and diverging) it also includes cyclic and bivariate color palettes. Furthermore, a color for missing values is assigned to each palette.

This package provides an interactive, virtualized data explorer widget for R'. Built on React (via reactR') and htmlwidgets', it offers column-type detection, multi-value checkbox filtering, sorting, column visibility toggling, virtual scrolling for large datasets, and a full-viewport modal. Includes dtsmartr_launch() with an interactive, zero-code file upload wizard using datamods'. Widgets can be embedded in R Markdown / Quarto documents, Shiny applications, or exported as standalone HTML files via save_dtsmartr()'.

Use spectrophotometry measurements performed on insects as a way to infer pathogens virulence. Insect movements cause fluctuations in fluorescence signal, and functions are provided to estimate when the insect has died as the moment when variance in autofluorescence signal drops to zero. The package provides functions to obtain this estimate together with functions to import spectrophotometry data from a Biotek microplate reader. Details of the method are given in Parthuisot et al. (2018) <doi:10.1101/297929>.

This package provides functions to have visualization and clean-up of enriched gene ontologies (GO) terms, protein complexes and pathways (obtained from multiple databases) using ConsensusPathDB from gene set over-expression analysis. Performs clustering of pathway based on similarity of over-expressed gene sets and visualizations similar to Ingenuity Pathway Analysis (IPA) when up and down regulated genes are known. The methods are described in a paper currently submitted by Orecchioni et al, 2020 in Nanoscale.

This package implements a variant of the Self-Organizing Map (SOM) algorithm designed for mixed-attribute datasets. Similarity between observations is computed using the Gower distance, and categorical prototypes are updated via heuristic strategies (weighted mode and multinomial sampling). Provides functions for model fitting, mapping, visualization (U-Matrix and component planes), and evaluation, making SOM applicable to heterogeneous real-world data. For methodological details see Sáez and Salas (2026) <doi:10.1007/s41060-025-00941-6>.

Implementation of a parametric joint model for modelling recurrent and competing event processes using generalised survival models as described in Entrop et al., (2025) <doi:10.1002/bimj.70038>. The joint model can subsequently be used to predict the mean number of events in the presence of competing risks at different time points. Comparisons of the mean number of event functions, e.g. the differences in mean number of events between two exposure groups, are also available.

This package implements an Integer Programming-based method for optimising genetic gain in polyclonal selection, where the goal is to select a group of genotypes that jointly meet multi-trait selection criteria. The method uses predictors of genotypic effects obtained from the fitting of mixed models. Its application is demonstrated with grapevine data, but is applicable to other species and breeding contexts. For more details see Surgy et al. (2025) <doi:10.1007/s00122-025-04885-0>.

It aggregates protein panel data and metadata for protein quantitative trait locus (pQTL) analysis using pQTLtools (<https://jinghuazhao.github.io/pQTLtools/>). The package includes data from affinity-based panels such as Olink (<https://olink.com/>) and SomaScan (<https://somalogic.com/>), as well as mass spectrometry-based panels from CellCarta (<https://cellcarta.com/>), Seer (<https://seer.bio/>) and SWATH-MS (<doi:10.15252/msb.20178126>). The metadata encompasses updated annotations and publication details.

Build your own universe of packages similar to the tidyverse package <https://tidyverse.org/> with this meta-package creator. Create a package-verse, or meta package, by supplying a custom name for the collection of packages and the vector of desired package names to includeâ and optionally supply a destination directory, an indicator of whether to keep the created package directory, and/or a vector of verbs implement via the usethis <http://usethis.r-lib.org/> package.

Visualizes sulcal morphometry data derived from BrainVisa <https://brainvisa.info/> including width, depth, surface area, and length. The package enables mapping of statistical group results or subject-level values onto cortical surface maps, with options to focus on all sulci or only selected regions of interest. Users can display all four measures simultaneously or restrict plots to chosen measures, creating composite, publication-quality brain visualizations in R to support the analysis and interpretation of sulcal morphology.

Efficient implementation of sparse group lasso with optional bound constraints on the coefficients; see <doi:10.18637/jss.v110.i06>. It supports the use of a sparse design matrix as well as returning coefficient estimates in a sparse matrix. Furthermore, it correctly calculates the degrees of freedom to allow for information criteria rather than cross-validation with very large data. Finally, the interface to compiled code avoids unnecessary copies and allows for the use of long integers.