This package provides a flexible tool for calculating carbon-equivalent emissions. Mostly using data from the UK Government's Greenhouse Gas Conversion Factors report <https://www.gov.uk/government/publications/greenhouse-gas-reporting-conversion-factors-2024>, it facilitates transparent emissions calculations for various sectors, including travel, accommodation, and clinical activities. The package is designed for easy integration into R workflows, with additional support for shiny applications and community-driven extensions.

This package provides functions for discordant kinship modeling (and other sibling-based quasi-experimental designs). Contains data restructuring functions and functions for generating biometrically informed data for kin pairs. See [Garrison and Rodgers, 2016 <doi:10.1016/j.intell.2016.08.008>], [Sims, Trattner, and Garrison, 2024 <doi:10.3389/fpsyg.2024.1430978>] for empirical examples, and [Garrison and colleagues for theoretical work <doi:10.1101/2025.08.25.25334395>].

The US EPA ECOTOX database is a freely available database with a treasure of aquatic and terrestrial ecotoxicological data. As the online search interface doesn't come with an API, this package provides the means to easily access and search the database in R. To this end, all raw tables are downloaded from the EPA website and stored in a local SQLite database <doi:10.1016/j.chemosphere.2024.143078>.

Homomorphic encryption (Brakerski and Vaikuntanathan (2014) <doi:10.1137/120868669>) using Ring Learning with Errors (Lyubashevsky et al. (2012) <https://eprint.iacr.org/2012/230>) is a form of Learning with Errors (Regev (2005) <doi:10.1145/1060590.1060603>) using polynomial rings over finite fields. Functions to generate the required polynomials (using polynom'), with various distributions of coefficients are provided. Additionally, functions to generate and take coefficient modulo are provided.

This package provides a comprehensive collection of linkage methods for agglomerative hierarchical clustering on a matrix of proximity data (distances or similarities), returning a multifurcated dendrogram or multidendrogram. Multidendrograms can group more than two clusters when ties in proximity data occur, and therefore they do not depend on the order of the input data. Descriptive measures to analyze the resulting dendrogram are additionally provided. <doi:10.18637/jss.v114.i02>.

Inference of Multiscale graphical models with neighborhood selection approach. The method is based on solving a convex optimization problem combining a Lasso and fused-group Lasso penalties. This allows to infer simultaneously a conditional independence graph and a clustering partition. The optimization is based on the Continuation with Nesterov smoothing in a Shrinkage-Thresholding Algorithm solver (Hadj-Selem et al. 2018) <doi:10.1109/TMI.2018.2829802> implemented in python.

Function ModEstM() is the only one of this package, it estimates the modes of an empirical univariate distribution. It relies on the stats::density() function, even for input control. Due to very good performance of the density estimation, computation time is not an issue. The multiple modes are handled using dplyr::group_by(). For conditions and rates of convergences, see Eddy (1980) <doi:10.1214/aos/1176345080>.

This package provides a Software Development Kit for working with Nixtla''s TimeGPT', a foundation model for time series forecasting. API is an acronym for application programming interface'; this package allows users to interact with TimeGPT via the API'. You can set and validate API keys and generate forecasts via API calls. It is compatible with tsibble and base R. For more details visit <https://docs.nixtla.io/>.

The openMSE package is designed for building operating models, doing simulation modelling and management strategy evaluation for fisheries. openMSE is an umbrella package for the MSEtool (Management Strategy Evaluation toolkit), DLMtool (Data-Limited Methods toolkit), and SAMtool (Stock Assessment Methods toolkit) packages. By loading and installing openMSE', users have access to the full functionality contained within these packages. Learn more about openMSE at <https://openmse.com/>.

This package implements a range of facilities for post-hoc analysis and summarizing linear models, generalized linear models and generalized linear mixed models, including grouping and clustering via pairwise comparisons using graph representations and efficient algorithms for finding maximal cliques of a graph. Includes also non-parametric toos for post-hoc analysis. It has S3 methods for printing summarizing, and producing plots, line and barplots suitable for post-hoc analyses.

Presents an explanatory animation of normal quantile-quantile plots based on a water-filling analogy. The animation presents a normal QQ plot as the parametric plot of the water levels in vases defined by two distributions. The distributions decorate the axes in the normal QQ plot and are optionally shown as vases adjacent to the plot. The package draws QQ plots for several distributions, either as samples or continuous functions.

An R implementation of quality controlâ based robust LOESS(local polynomial regression fitting) signal correction for metabolomics data analysis, described in Dunn, W., Broadhurst, D., Begley, P. et al. (2011) <doi:10.1038/nprot.2011.335>. The optimisation of LOESS's span parameter using generalized cross-validation (GCV) is provided as an option. In addition to signal correction, qcrlscR includes some utility functions like batch shifting and data filtering.

This tiny package contains one function smirnov() which calculates two scaled taxonomic coefficients, Txy (coefficient of similarity) and Txx (coefficient of originality). These two characteristics may be used for the analysis of similarities between any number of taxonomic groups, and also for assessing uniqueness of giving taxon. It is possible to use smirnov() output as a distance measure: convert it to distance by "as.dist(1 - smirnov(x))".

This data-driven phylogenetic comparative method fits stabilizing selection models to continuous trait data, building on the ouch methodology of Butler and King (2004) <doi:10.1086/426002>. The main functions fit a series of Hansen models using stepwise AIC, then identify cases of convergent evolution where multiple lineages have shifted to the same adaptive peak. For more information see Ingram and Mahler (2013) <doi:10.1111/2041-210X.12034>.

This package provides a collection of commonly used tools for animal movement and other tracking data. Variously distance, angle, bearing, distance-to, bearing-to and speed are provided for geographic data that can be used directly or within tidyverse syntax. Distances and bearings are calculated using modern geodesic methods as provided by Charles F. F. Karney (2013) <doi:10.1007/s00190-012-0578-z> via the geodist and geosphere packages.

This package produces weighted cross-tabulation tables for one or more outcome variables across one or more breakdown variables, and exports them directly to Excel'. For each outcome-by-breakdown combination, the package creates a weighted percentage table and a corresponding unweighted count table, with transparent handling of missing values and light, readable formatting. Designed to support social survey analysis workflows that require large sets of consistent, publication-ready tables.

This package implements marginal structural models combined with a latent class growth analysis framework for assessing the causal effect of treatment trajectories. Based on the approach described in "Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories" Diop, A., Sirois, C., Guertin, J.R., Schnitzer, M.E., Candas, B., Cossette, B., Poirier, P., Brophy, J., Mésidor, M., Blais, C. and Hamel, D., (2023) <doi:10.1177/09622802231202384>.

Estimates heterogeneous treatment effects using tidy semantics on experimental or observational data. Methods are based on the doubly-robust learner of Kennedy (2023) <doi:10.1214/23-EJS2157>. You provide a simple recipe for what machine learning algorithms to use in estimating the nuisance functions and tidyhte will take care of cross-validation, estimation, model selection, diagnostics and construction of relevant quantities of interest about the variability of treatment effects.

This package provides tools to analyze sex differences in omics data for complex diseases. It includes functions for differential expression analysis using the limma method <doi:10.1093/nar/gkv007>, interaction testing between sex and disease, pathway enrichment with clusterProfiler <doi:10.1089/omi.2011.0118>, and gene regulatory network (GRN) construction and analysis using igraph'. The package enables a reproducible workflow from raw data processing to biological interpretation.

There are two new network metrics, RWC (random walk centrality) and CBET (counting betweenness). Also available are the normalized versions of those metrics. These measures of centrality and betweenness are particularly useful for the analysis of very dense weighted networks which include loops. Traditional measures do not work as well for those network characteristics. The main reference is DePaolis at al (2022) <doi:10.1007/s41109-022-00519-2>.

This package provides functions and command-line user interface to generate allocation sequence by response-adaptive randomization for clinical trials. The package currently supports two families of frequentist response-adaptive randomization procedures, Doubly Adaptive Biased Coin Design ('DBCD') and Sequential Estimation-adjusted Urn Model ('SEU'), for binary and normal endpoints. One-sided proportion (or mean) difference and Chi-square (or ANOVA') hypothesis testing methods are also available in the package to facilitate the inference for treatment effect. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria. For example, plots for relationship among assumed treatment effects, sample size, and power are provided. Five allocation functions for DBCD and six addition rule functions for SEU are implemented to target allocations such as Neyman', Rosenberger Rosenberger et al. (2001) <doi:10.1111/j.0006-341X.2001.00909.x> and Urn allocations.

This package provides the cumulative distribution function (CDF), quantile, and statistical power calculator for a collection of thresholding Fisher's p-value combination methods, including Fisher's p-value combination method, truncated product method and, in particular, soft-thresholding Fisher's p-value combination method which is proven to be optimal in some context of signal detection. The p-value calculator for the omnibus version of these tests are also included.

This package provides a Bayesian method for quantifying the liklihood that a given plasma mutation arises from clonal hematopoesis or the underlying tumor. It requires sequencing data of the mutation in plasma and white blood cells with the number of distinct and mutant reads in both tissues. We implement a Monte Carlo importance sampling method to assess the likelihood that a mutation arises from the tumor relative to non-tumor origin.

STADyUM is a package with functionality for analyzing nascent RNA read counts to infer transcription rates. This includes utilities for processing experimental nascent RNA read counts as well as for simulating PRO-seq data. Rates such as initiation, pause release and landing pad occupancy are estimated from either synthetic or experimental data. There are also options for varying pause sites and including steric hindrance of initiation in the model.