This package provides tools for applying Sklar's Omega (Hughes, 2022) <doi:10.1007/s11222-022-10105-2> methodology to nominal scores, ordinal scores, percentages, counts, amounts (i.e., non-negative real numbers), and balances (i.e., any real number). The framework can accommodate any number of units, any number of coders, and missingness; and can be used to measure agreement with a gold standard, intra-coder agreement, and/or inter-coder agreement. Frequentist inference is supported for all levels of measurement. Bayesian inference is supported for continuous scores only.

Built on graph theory and the high-performance data.table framework, this package provides a comprehensive suite of tools for tidying, pruning, and visualizing animal pedigrees. By modeling pedigrees as directed acyclic graphs using igraph', it ensures robust loop detection, efficient generation assignment, and sophisticated hierarchical layouts. Key features include standardizing pedigree formats, flexible ancestry tracing, and generating legible vector-based PDF graphs. A unique compaction algorithm enables the visualization of massive pedigrees (e.g., in aquaculture selective breeding population) by grouping full-sib families, maintaining structural clarity without overcrowding.

Calculates marginal effects based on logistic model objects such as glm or speedglm at the average (default) or at given values using finite differences. It also returns confidence intervals for said marginal effects and the p-values, which can easily be used as input in stargazer. The function only returns the essentials and is therefore much faster but not as detailed as other functions available to calculate marginal effects. As a result, it is highly suitable for large datasets for which other packages may require too much time or calculating power.

Implementation of two sample comparison procedures based on median-based statistical tests for functional data, introduced in Smida et al (2022) <doi:10.1080/10485252.2022.2064997>. Other competitive state-of-the-art approaches proposed by Chakraborty and Chaudhuri (2015) <doi:10.1093/biomet/asu072>, Horvath et al (2013) <doi:10.1111/j.1467-9868.2012.01032.x> or Cuevas et al (2004) <doi:10.1016/j.csda.2003.10.021> are also included in the package, as well as procedures to run test result comparisons and power analysis using simulations.

This package provides functions connecting to the Salesforce Platform APIs (REST, SOAP, Bulk 1.0, Bulk 2.0, Metadata, Reports and Dashboards) <https://trailhead.salesforce.com/content/learn/modules/api_basics/api_basics_overview>. "API" is an acronym for "application programming interface". Most all calls from these APIs are supported as they use CSV, XML or JSON data that can be parsed into R data structures. For more details please see the Salesforce API documentation and this package's website <https://stevenmmortimer.github.io/salesforcer/> for more information, documentation, and examples.

Selection of spatially balanced samples. In particular, the implemented sampling designs allow to select probability samples well spread over the population of interest, in any dimension and using any distance function (e.g. Euclidean distance, Manhattan distance). For more details, Pantalone F, Benedetti R, and Piersimoni F (2022) <doi:10.18637/jss.v103.c02>, Benedetti R and Piersimoni F (2017) <doi:10.1002/bimj.201600194>, and Benedetti R and Piersimoni F (2017) <arXiv:1710.09116>. The implementation has been done in C++ through the use of Rcpp and RcppArmadillo'.

This package provides a suite of helper functions to support Bayesian Kernel Machine Regression (BKMR) analyses in environmental health research. It enables the simulation of realistic multivariate exposure data using Multivariate Skewed Gamma distributions, estimation of distributional parameters by subgroup, and application of adaptive, data-driven thresholds for feature selection via Posterior Inclusion Probabilities (PIPs). It is especially suited for handling skewed exposure data and enhancing the interpretability of BKMR results through principled variable selection. The methodology is shown in Hasan et. al. (2025) <doi:10.1101/2025.04.14.25325822>.

Affords researchers the ability to draw stratified samples from the U.S. Department of Veteran's Affairs/Department of Defense Identity Repository (VADIR) database according to a variety of population characteristics. The VADIR database contains information for all veterans who were separated from the military after 1980. The central utility of the present package is to integrate data cleaning and formatting for the VADIR database with the stratification methods described by Mahto (2019) <https://CRAN.R-project.org/package=splitstackshape>. Data from VADIR are not provided as part of this package.

This package provides a pilot matching design to automatically stratify and match large datasets. The manual_stratify() function allows users to manually stratify a dataset based on categorical variables of interest, while the auto_stratify() function does automatically by allocating a held-aside (pilot) data set, fitting a prognostic score (see Hansen (2008) <doi:10.1093/biomet/asn004>) on the pilot set, and stratifying the data set based on prognostic score quantiles. The strata_match() function then does optimal matching of the data set in parallel within strata.

This package provides a tool for computing network representations of attitudes, extracted from tabular data such as sociological surveys. Development of surveygraph software and training materials was initially funded by the European Union under the ERC Proof-of-concept programme (ERC, Attitude-Maps-4-All, project number: 101069264). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

The SALTSampler package facilitates Monte Carlo Markov Chain (MCMC) sampling of random variables on a simplex. A Self-Adjusting Logit Transform (SALT) proposal is used so that sampling is still efficient even in difficult cases, such as those in high dimensions or with parameters that differ by orders of magnitude. Special care is also taken to maintain accuracy even when some coordinates approach 0 or 1 numerically. Diagnostic and graphic functions are included in the package, enabling easy assessment of the convergence and mixing of the chain within the constrained space.

Takes user-provided baseline data from groups of randomised controlled data and assesses whether the observed distribution of baseline p-values, numbers of participants in each group, or categorical variables are consistent with the expected distribution, as an aid to the assessment of integrity concerns in published randomised controlled trials. References (citations in PubMed format in details of each function): Bolland MJ, Avenell A, Gamble GD, Grey A. (2016) <doi:10.1212/WNL.0000000000003387>. Bolland MJ, Gamble GD, Avenell A, Grey A, Lumley T. (2019) <doi:10.1016/j.jclinepi.2019.05.006>. Bolland MJ, Gamble GD, Avenell A, Grey A. (2019) <doi:10.1016/j.jclinepi.2019.03.001>. Bolland MJ, Gamble GD, Grey A, Avenell A. (2020) <doi:10.1111/anae.15165>. Bolland MJ, Gamble GD, Avenell A, Cooper DJ, Grey A. (2021) <doi:10.1016/j.jclinepi.2020.11.012>. Bolland MJ, Gamble GD, Avenell A, Grey A. (2021) <doi:10.1016/j.jclinepi.2021.05.002>. Bolland MJ, Gamble GD, Avenell A, Cooper DJ, Grey A. (2023) <doi:10.1016/j.jclinepi.2022.12.018>. Carlisle JB, Loadsman JA. (2017) <doi:10.1111/anae.13650>. Carlisle JB. (2017) <doi:10.1111/anae.13938>.

This R package provides tools for building and running automated end-to-end analysis workflows for a wide range of next generation sequence (NGS) applications such as RNA-Seq, ChIP-Seq, VAR-Seq and Ribo-Seq. Important features include a uniform workflow interface across different NGS applications, automated report generation, and support for running both R and command-line software, such as NGS aligners or peak/variant callers, on local computers or compute clusters. Efficient handling of complex sample sets and experimental designs is facilitated by a consistently implemented sample annotation infrastructure.

This package provides tools for testing, monitoring and dating structural changes in (linear) regression models. It features tests/methods from the generalized fluctuation test framework as well as from the F test (Chow test) framework. This includes methods to fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM, recursive/moving estimates) and F statistics, respectively. It is possible to monitor incoming data online using fluctuation processes. Finally, the breakpoints in regression models with structural changes can be estimated together with confidence intervals. Emphasis is always given to methods for visualizing the data.

This package provides a set of functions to select the optimal block-length for a dependent bootstrap (block-bootstrap). Includes the Hall, Horowitz, and Jing (1995) <doi:10.1093/biomet/82.3.561> subsampling-based cross-validation method, the Politis and White (2004) <doi:10.1081/ETC-120028836> Spectral Density Plug-in method, including the Patton, Politis, and White (2009) <doi:10.1080/07474930802459016> correction, and the Lahiri, Furukawa, and Lee (2007) <doi:10.1016/j.stamet.2006.08.002> nonparametric plug-in method, with a corresponding set of S3 plot methods.

This package provides functions for the estimation of conditional copulas models, various estimators of conditional Kendall's tau (proposed in Derumigny and Fermanian (2019a, 2019b, 2020) <doi:10.1515/demo-2019-0016>, <doi:10.1016/j.csda.2019.01.013>, <doi:10.1016/j.jmva.2020.104610>), test procedures for the simplifying assumption (proposed in Derumigny and Fermanian (2017) <doi:10.1515/demo-2017-0011> and Derumigny, Fermanian and Min (2022) <doi:10.1002/cjs.11742>), and measures of non-simplifyingness (proposed in Derumigny (2025) <doi:10.48550/arXiv.2504.07704>).

This package creates a HTML widget which displays the results of searching for a pattern in files in a given folder. The results can be viewed in the RStudio viewer pane, included in a R Markdown document or in a Shiny application. Also provides a Shiny application allowing to run this widget and to navigate in the files found by the search. Instead of creating a HTML widget, it is also possible to get the results of the search in a tibble'. The search is performed by the grep command-line utility.

Fits look-up tables by filling entries with the mean or median values of observations fall in partitions of the feature space. Partitions can be determined by user of the package using input argument feature.boundaries, and dimensions of the feature space can be any combination of continuous and categorical features provided by the data set. A Predict function directly fetches corresponding entry value, and a default value is defined as the mean or median of all available observations. The table and other components are represented using the S4 class lookupTable.

This package provides tools to generate random landscape graphs, evaluate species occurrence in dynamic landscapes, simulate future landscape occupation and evaluate range expansion when new empty patches are available (e.g. as a result of climate change). References: Mestre, F., Canovas, F., Pita, R., Mira, A., Beja, P. (2016) <doi:10.1016/j.envsoft.2016.03.007>; Mestre, F., Risk, B., Mira, A., Beja, P., Pita, R. (2017) <doi:10.1016/j.ecolmodel.2017.06.013>; Mestre, F., Pita, R., Mira, A., Beja, P. (2020) <doi:10.1186/s12898-019-0273-5>.

An implementation of the Likelihood ratio Test (LRT) for testing that, in a (non)linear mixed effects model, the variances of a subset of the random effects are equal to zero. There is no restriction on the subset of variances that can be tested: for example, it is possible to test that all the variances are equal to zero. Note that the implemented test is asymptotic. This package should be used on model fits from packages nlme', lmer', and saemix'. Charlotte Baey and Estelle Kuhn (2019) <doi:10.18637/jss.v107.i06>.

Animalcules is an R package for utilizing up-to-date data analytics, visualization methods, and machine learning models to provide users an easy-to-use interactive microbiome analysis framework. It can be used as a standalone software package or users can explore their data with the accompanying interactive R Shiny application. Traditional microbiome analysis such as alpha/beta diversity and differential abundance analysis are enhanced, while new methods like biomarker identification are introduced by animalcules. Powerful interactive and dynamic figures generated by animalcules enable users to understand their data better and discover new insights.

This variant of the Racket BC (``before Chez'' or ``bytecode'') implementation is not recommended for general use. It uses CGC (a ``Conservative Garbage Collector''), which was succeeded as default in PLT Scheme version 370 (which translates to 3.7 in the current versioning scheme) by the 3M variant, which in turn was succeeded in version 8.0 by the Racket CS implementation.

Racket CGC is primarily used for bootstrapping Racket BC [3M]. It may also be used for embedding applications without the annotations needed in C code to use the 3M garbage collector.

MotifPeeker is used to compare and analyse datasets from epigenomic profiling methods with motif enrichment as the key benchmark. The package outputs an HTML report consisting of three sections: (1. General Metrics) Overview of peaks-related general metrics for the datasets (FRiP scores, peak widths and motif-summit distances). (2. Known Motif Enrichment Analysis) Statistics for the frequency of user-provided motifs enriched in the datasets. (3. De-Novo Motif Enrichment Analysis) Statistics for the frequency of de-novo discovered motifs enriched in the datasets and compared with known motifs.

This package implements the algorithm described in Trapnell,C. et al. (2010) <doi: 10.1038/nbt.1621>. This function takes read counts matrix of RNA-Seq data, feature lengths which can be retrieved using biomaRt package, and the mean fragment lengths which can be calculated using the CollectInsertSizeMetrics(Picard) tool. It then returns a matrix of FPKM normalised data by library size and feature effective length. It also provides the user with a quick and reliable function to generate FPKM heatmap plot of the highly variable features in RNA-Seq dataset.