This package implements various estimators of entropy, such as the shrinkage estimator by Hausser and Strimmer, the maximum likelihood and the Millow-Madow estimator, various Bayesian estimators, and the Chao-Shen estimator. It also offers an R interface to the NSB estimator. Furthermore, it provides functions for estimating Kullback-Leibler divergence, chi-squared, mutual information, and chi-squared statistic of independence. In addition there are functions for discretizing continuous random variables.

This package is a collection of ANSI escape code related libraries enabling ANSI colorization and stylization of console output. Included in the library are the Code module, which defines ANSI codes as constants and methods, a Mixin module for including color methods, a Logger, a ProgressBar, and a String subclass. The library also includes a Terminal module which provides information about the current output device.

This package provides example one-dimensional proton NMR spectra of murine urine samples collected before and after bariatric or sham surgery (Roux-en-Y gastric bypass). The data are adapted from Jia V Li et al. (2011), "Metabolic surgery profoundly influences gut microbial-host metabolic cross-talk", Gut, 60(9), 1214–1223. <doi:10.1136/gut.2010.234708>. This package serves as example data for metabolomics analysis and teaching purposes.

This package provides a model designed for dimensionality reduction and batch effect removal for scRNA-seq data. It is designed to be massively parallelizable using shared objects that prevent memory duplication, and it can be used with different mini-batch approaches in order to reduce time consumption. It assumes a negative binomial distribution for the data with a dispersion parameter that can be both commonwise across gene both genewise.

This package provides functions for identification and visualization of potential intramolecular triplex patterns in DNA sequence. The main functionality is to detect the positions of subsequences capable of folding into an intramolecular triplex (H-DNA) in a much larger sequence. The potential H-DNA (triplexes) should be made of as many cannonical nucleotide triplets as possible. The package includes visualization showing the exact base-pairing in 1D, 2D or 3D.

This package provides a fast integrative genetic association test for rare diseases based on a model for disease status given allele counts at rare variant sites. Probability of association, mode of inheritance and probability of pathogenicity for individual variants are all inferred in a Bayesian framework - A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases', Greene et al 2017 <doi:10.1016/j.ajhg.2017.05.015>.

This package provides functions for downloading data from the Bank for International Settlements (BIS; <https://www.bis.org/>) in Basel. Supported are only full datasets in (typically) CSV format. The package is lightweight and without dependencies; suggested packages are used only if data is to be transformed into particular data structures, for instance into zoo objects. Downloaded data can optionally be cached, to avoid repeated downloads of the same files.

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 implements a specific form of segmented linear regression with two independent variables. The visualization of that function looks like a quarter segment of a cowbell giving the package its name. The package has been specifically constructed for the case where minimum and maximum value of the dependent and two independent variables are known a prior, which is usually the case when those values are derived from Likert scales.

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>.

Estimates the time-varying reproduction number, rate of spread, and doubling time using a range of open-source tools (Abbott et al. (2020) <doi:10.12688/wellcomeopenres.16006.1>), and current best practices (Gostic et al. (2020) <doi:10.1101/2020.06.18.20134858>). It aims to help users avoid some of the limitations of naive implementations in a framework that is informed by community feedback and is actively supported.

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.

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 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>.

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 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 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))".

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 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>.