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. Motif Discovery Enrichment Analysis) Statistics for the frequency of ab-initio discovered motifs enriched in the datasets and compared with known motifs.

Quick and straightforward visualization of read signal over genomic intervals is key for generating hypotheses from sequencing data sets (e.g. ChIP-seq, ATAC-seq, bisulfite/methyl-seq). Many tools both inside and outside of R and Bioconductor are available to explore these types of data, and they typically start with a bigWig or BAM file and end with some representation of the signal (e.g. heatmap). profileplyr leverages many Bioconductor tools to allow for both flexibility and additional functionality in workflows that end with visualization of the read signal.

This package provides tools for the automatic stratification of survey populations using clustering and optimization techniques. The package assists researchers and survey practitioners in constructing homogeneous strata to improve the efficiency and precision of survey estimates. Functions are provided for generating strata, evaluating stratification quality, summarizing stratified populations, and visualizing stratification results. These tools support the design and implementation of efficient survey sampling strategies. The package utilizes standard statistical methods from survey sampling, clustering, and optimization for automatic stratification. Methods are described in Cochran (1977, ISBN:9780471162405) and Lohr (2021, ISBN:9780367354556).

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.

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>.

This package provides tools for constructing, auditing, and visualizing temporal social interaction networks from event-log data. Supports graph construction from raw user-to-user interaction logs, longitudinal tracking of network structure, community dynamics, user role trajectories, and concentration of engagement over time. Designed for computational social science, platform analytics, and digital community health monitoring. Includes four longitudinal audit indices: the Network Drift Index ('NDI'), Community Fragmentation Index ('CFI'), Visibility Concentration Index ('VCI'), and Role Mobility Index ('RMI'). NDI', CFI', VCI', and RMI are purpose-built composite scores for longitudinal platform auditing.

This package provides comprehensive methods for sample size determination for epidemiological studies, clinical trials, diagnostic accuracy studies, and diagnostic agreement studies. The package supports prevalence surveys, cluster prevalence studies, unmatched case-control studies, cohort studies, superiority, non-inferiority, and equivalence clinical trials, diagnostic sensitivity, diagnostic specificity, receiver operating characteristic (ROC) area under the curve (AUC), and diagnostic agreement studies. Functions include optional adjustments for finite population correction, design effect, unequal allocation, anticipated response rate, and dropout. Results are returned as standardized SampleSizeR objects with print, summary, plot, and data frame methods.

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.

NIMBLE is a general purpose modeling framework that facilitates the analysis of Markov Chain Monte Carlo (MCMC) models. However, it constructs a complex and variable data structure, which is difficult to use in a general way. The nimbleExtra package provides a set of functions to insulate users from the complexity and variability of NIMBLE data structures. It also complements NIMBLE and other MCMC analysis packages by providing missing functionality to encourage generic programming. Together these features ease the burden of working with MCMC data and encourage more systematic approaches to MCMC analysis.

From output files obtained from the software ModestR', the relative contribution of factors to explain species distribution is depicted using several plots. A global geographic raster file for each environmental variable may be also obtained with the mean relative contribution, considering all species present in each raster cell, of the factor to explain species distribution. Finally, for each variable it is also possible to compare the frequencies of any variable obtained in the cells where the species is present with the frequencies of the same variable in the cells of the extent.

Supports simulating choice experiment data for given designs. It helps to quickly test different designs against each other and compare the performance of new models. The goal of simulateDCE is to make it easy to simulate choice experiment datasets using designs from NGENE', idefix or spdesign'. You have to store the design file(s) in a sub-directory and need to specify certain parameters and the utility functions for the data generating process. For more details on choice experiments see Mariel et al. (2021) <doi:10.1007/978-3-030-62669-3>.

Specifying lavaan models manually can be time consuming when multiple similar models are required. The semFromKeys package streamlines the process of running lavaan models by generating model code from simple keys lists and running entire collections of models at once. The package was inspired by the process used in the code for Bainbridge, T. F., Ludeke, S. G., & Smillie, L. D. (2022) <doi:10.1037/pspp0000395>. The package also optionally checks that identical models have not been run on the same data, which saves time when code needs to be run again.

Interactive adverse event (AE) volcano plot for monitoring clinical trial safety. This tool allows users to view the overall distribution of AEs in a clinical trial using standard (e.g. MedDRA preferred term) or custom (e.g. Gender) categories using a volcano plot similar to proposal by Zink et al. (2013) <doi:10.1177/1740774513485311>. This tool provides a stand-along shiny application and flexible shiny modules allowing this tool to be used as a part of more robust safety monitoring framework like the Shiny app from the safetyGraphics R package.

This package implements quantile smoothing. It contains a dataset used to produce human chromosomal ideograms for plotting purposes and a collection of arrays that contains data of chromosome 14 of 3 colorectal tumors. The package provides functions for painting chromosomal icons, chromosome or chromosomal idiogram and other types of plots. Quantsmooth offers options like converting chromosomal ids to their numeric form, retrieving the human chromosomal length from NCBI data, retrieving regions of interest in a vector of intensities using quantile smoothing, determining cytoband position based on the location of the probe, and other useful tools.

Battlefield is a Swiss-army toolkit originally developed to define and extract spatial spots from specific tissue regions—such as front regions, niche borders, invasive margins, and cluster interfaces—using spatial transcriptomics data or clustered tissue maps. It has since been extended to support trajectory selection and layer inspection, and now provides a collection of low-level utilities for spatial transcriptomics analysis. These utilities are primarily intended to be reused within higher-level analytical packages. It is designed to work with sequencing-based platforms such as Visium at several resolutions and Visium HD(binned).

Includes functions to estimate production frontiers and make ideal output predictions in the Data Envelopment Analysis (DEA) context using both standard models from DEA and Free Disposal Hull (FDH) and boosting techniques. In particular, EATBoosting (Guillen et al., 2023 <doi:10.1016/j.eswa.2022.119134>) and MARSBoosting. Moreover, the package includes code for estimating several technical efficiency measures using different models such as the input and output-oriented radial measures, the input and output-oriented Russell measures, the Directional Distance Function (DDF), the Weighted Additive Measure (WAM) and the Slacks-Based Measure (SBM).

This package provides a wrapper on top of the Domino Data Python SDK library. It lets you query and access Domino Data Sources directly from your R environment. Under the hood, Domino Data R SDK leverages the API provided by the Domino Data Python SDK', which must be installed as a prerequisite. Domino is a platform that makes it easy to run your code on scalable hardware, with integrated version control and collaboration features designed for analytical workflows. See <https://docs.dominodatalab.com/en/latest/api_guide/140b48/domino-data-api> for more information.

Several multivariate techniques from a biplot perspective. It is the translation (with many improvements) into R of the previous package developed in Matlab'. The package contains some of the main developments of my team during the last 30 years together with some more standard techniques. Package includes: Classical Biplots, HJ-Biplot, Canonical Biplots, MANOVA Biplots, Correspondence Analysis, Canonical Correspondence Analysis, Canonical STATIS-ACT, Logistic Biplots for binary and ordinal data, Multidimensional Unfolding, External Biplots for Principal Coordinates Analysis or Multidimensional Scaling, among many others. References can be found in the help of each procedure.

The primary function makeCPMSampler() generates a sampler function which performs the correlated pseudo-marginal method of Deligiannidis, Doucet and Pitt (2017) <arXiv:1511.04992>. If the rho= argument of makeCPMSampler() is set to 0, then the generated sampler function performs the original pseudo-marginal method of Andrieu and Roberts (2009) <DOI:10.1214/07-AOS574>. The sampler function is constructed with the user's choice of prior, parameter proposal distribution, and the likelihood approximation scheme. Note that this algorithm is not automatically tuned--each one of these arguments must be carefully chosen.

This package provides tools for evaluating the incremental economic consequences of a proposed farm-management change using partial-budget logic. Functions organize added returns, reduced costs, added costs, and reduced returns; compare baseline and alternative budgets; calculate net changes and marginal rates of return; conduct one- and two-way sensitivity, scenario, break-even, dominance, marginal, and Monte Carlo uncertainty analyses; and convert capital changes to annual equivalents. The framework follows the approach described by the International Maize and Wheat Improvement Center (1988, ISBN: 968-6127-19-4) for farm-management, extension, and on-farm research.

This package provides a clustering algorithm similar to K-Means is implemented, it has two main advantages, namely (a) The estimator is resistant to outliers, that means that results of estimator are still correct when there are atypical values in the sample and (b) The estimator is efficient, roughly speaking, if there are no outliers in the sample, results will be similar to those obtained by a classic algorithm (K-Means). Clustering procedure is carried out by minimizing the overall robust scale so-called tau scale. (see Gonzalez, Yohai and Zamar (2019) <arxiv:1906.08198>).