Easy and rapid quantitative estimation of small terrestrial ectotherm temperature regulation effectiveness in R. ectotemp is built on classical formulas that evaluate temperature regulation by means of various indices, inaugurated by Hertz et al. (1993) <doi: 10.1086/285573>. Options for bootstrapping and permutation testing are included to test hypotheses about divergence between organisms, species or populations.

To run data analysis for enzyme-link immunosorbent assays (ELISAs). Either the five- or four-parameter logistic model will be fitted for data of single ELISA. Moreover, the batch effect correction/normalization will be carried out, when there are more than one batches of ELISAs. Feng (2018) <doi:10.1101/483800>.

This SVG elements generator can easily generate SVG elements such as rect, line, circle, ellipse, polygon, polyline, text and group. Also, it can combine and output SVG elements into a SVG file.

This package provides environment hooks that obtain errors and warnings which occur during the execution of code to automatically search for solutions.

This package provides tools to analyse human and mosquito behavioral interactions and to compute exposure to mosquito bites estimates. Using behavioral data for human individuals and biting patterns for mosquitoes, you will be able to compute hourly exposure for bed net users and non-users, and summarize (e.g. proportion indoors and outdoors, proportion per time periods, and proportion prevented by bed nets) or visualize these dynamics across a 24-hour cycle.

This package provides unsupervised selection and clustering of microarray data using mixture models. Following the methods described in McLachlan, Bean and Peel (2002) <doi:10.1093/bioinformatics/18.3.413> a subset of genes are selected based one the likelihood ratio statistic for the test of one versus two components when fitting mixtures of t-distributions to the expression data for each gene. The dimensionality of this gene subset is further reduced through the use of mixtures of factor analyzers, allowing the tissue samples to be clustered by fitting mixtures of normal distributions.

This package provides reproducible methods for experimental design and treatment allocation in biomedical, veterinary, agricultural, and clinical research. Includes simple, fixed-block, variable-block, stratified, stratified-block, cluster, matched-pair, restricted, minimization, and covariate-adaptive randomization, together with completely randomized, randomized-block, factorial, split-plot, Latin square, and crossover designs. Also provides allocation summaries, balance diagnostics, schedule export, and visualization. The methods are based on established principles of randomization and experimental design; see Rosenberger and Lachin (2015, ISBN:9781118742242) and Jones and Kenward (2014, ISBN:9781439861424).

Computes exact p-values for multinomial goodness-of-fit tests based on multiple test statistics, namely, Pearson's chi-square, the log-likelihood ratio and the probability mass statistic. Implements the algorithm detailed in Resin (2023) <doi:10.1080/10618600.2022.2102026>. Estimates based on the classical asymptotic chi-square approximation or Monte-Carlo simulation can also be computed.

It enables detailed interpretation of complex classification and regression models through Shapley analysis including data-driven characterization of subgroups of individuals. Furthermore, it facilitates multi-measure model evaluation, model fairness, and decision curve analysis. Additionally, it offers enhanced visualizations with interactive elements.

Analysis of trade in value added with international input-output tables. Includes commands for easy data extraction, matrix manipulation, decomposition of value added in gross exports and calculation of value added indicators, with full geographical and sector customization. Decomposition methods include Borin and Mancini (2023) <doi:10.1080/09535314.2022.2153221>, Miroudot and Ye (2021) <doi:10.1080/09535314.2020.1730308>, Wang et al. (2013) <https://econpapers.repec.org/paper/nbrnberwo/19677.htm> and Koopman et al. (2014) <doi:10.1257/aer.104.2.459>.

Reads European Data Format files EDF and EDF+, see <http://www.edfplus.info>, BioSemi Data Format files BDF, see <http://www.biosemi.com/faq/file_format.htm>, and BDF+ files, see <http://www.teuniz.net/edfbrowser/bdfplus%20format%20description.html>. The files are read in two steps: first the header is read and then the signals (using the header object as a parameter).

This dataset contains population estimates of all European cities with at least 10,000 inhabitants during the period 1500-1800. These data are adapted from Jan De Vries, "European Urbanization, 1500-1800" (1984).

This package implements the probability mass function of, and random draws from, the Ewens distribution, a probability distribution over partitions of integer, as described in Ewens (1972) <doi:10.1016/0040-5809(72)90035-4>.

Analyzes participation in course-session transcripts stored in the WebVTT format <https://www.w3.org/TR/webvtt1/>, including transcripts exported by Zoom and similar videoconferencing platforms. Provides tools to load and process transcripts, calculate speaker-level engagement metrics, create privacy-supporting plots and exports, and perform reviewable exact name matching against course rosters. Structured-field masking and technical privacy-review helpers support local review but do not determine legal or institutional compliance.

Extra strength glue for data-driven templates. String interpolation for Shiny apps or R Markdown and knitr'-powered Quarto documents, built on the glue and whisker packages.

This package provides functions for signal detection and identification designed for Event-Related Potentials (ERP) data in a linear model framework. The functional F-test proposed in Causeur, Sheu, Perthame, Rufini (2018, submitted) for analysis of variance issues in ERP designs is implemented for signal detection (tests for mean difference among groups of curves in One-way ANOVA designs for example). Once an experimental effect is declared significant, identification of significant intervals is achieved by the multiple testing procedures reviewed and compared in Sheu, Perthame, Lee and Causeur (2016, <DOI:10.1214/15-AOAS888>). Some of the methods gathered in the package are the classical FDR- and FWER-controlling procedures, also available using function p.adjust. The package also implements the Guthrie-Buchwald procedure (Guthrie and Buchwald, 1991 <DOI:10.1111/j.1469-8986.1991.tb00417.x>), which accounts for the auto-correlation among t-tests to control erroneous detection of short intervals. The Adaptive Factor-Adjustment method is an extension of the method described in Causeur, Chu, Hsieh and Sheu (2012, <DOI:10.3758/s13428-012-0230-0>). It assumes a factor model for the correlation among tests and combines adaptively the estimation of the signal and the updating of the dependence modelling (see Sheu et al., 2016, <DOI:10.1214/15-AOAS888> for further details).

Implementation of an Event Categorization Matrix (ECM) detonation detection model and a Bayesian variant. Functions are provided for importing and exporting data, fitting models, and applying decision criteria for categorizing new events. This package implements methods described in the paper "Bayesian Event Categorization Matrix Approach for Nuclear Detonations" Koermer, Carmichael, and Williams (2024) available on arXiv at <doi:10.48550/arXiv.2409.18227>.

This package provides a complete rewrite and reimagining of bakR (see Vock et al. (2025) <doi:10.1371/journal.pcbi.1013179>). Designed to support a wide array of analyses of nucleotide recoding RNA-seq (NR-seq) datasets of any type, including TimeLapse-seq/SLAM-seq/TUC-seq, Start-TimeLapse-seq (STL-seq), TT-TimeLapse-seq (TT-TL-seq), and subcellular NR-seq. EZbakR extends standard NR-seq standard NR-seq mutational modeling to support multi-label analyses (e.g., 4sU and 6sG dual labeling), and implements an improved hierarchical model to better account for transcript-to-transcript variance in metabolic label incorporation. EZbakR also generalized dynamical systems modeling of NR-seq data to support analyses of premature mRNA processing and flow between subcellular compartments. Finally, EZbakR implements flexible and well-powered comparative analyses of all estimated parameters via design matrix-specified generalized linear modeling.

The cointegration based support vector regression model enables researchers to use data obtained from the cointegrating vector as input in the support vector regression model.

The EpiSimR package provides an interactive shiny app based on deterministic compartmental mathematical modeling for simulating and visualizing the dynamics of epidemic and endemic disease spread. It allows users to explore various intervention strategies, including vaccination and isolation, by adjusting key epidemiological parameters. The methodology follows the approach described by Brauer (2008) <doi:10.1007/978-3-540-78911-6_2>. Thanks to shiny package.

Four ensemble-based methods (SMOTEBoost, RUSBoost, UnderBagging, and SMOTEBagging) for class imbalance problem are implemented for binary classification. Such methods adopt ensemble methods and data re-sampling techniques to improve model performance in presence of class imbalance problem. One special feature offers the possibility to choose multiple supervised learning algorithms to build weak learners within ensemble models. References: Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, and Kevin W. Bowyer (2003) <doi:10.1007/978-3-540-39804-2_12>, Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, and Amri Napolitano (2010) <doi:10.1109/TSMCA.2009.2029559>, R. Barandela, J. S. Sanchez, R. M. Valdovinos (2003) <doi:10.1007/s10044-003-0192-z>, Shuo Wang and Xin Yao (2009) <doi:10.1109/CIDM.2009.4938667>, Yoav Freund and Robert E. Schapire (1997) <doi:10.1006/jcss.1997.1504>.

Model-based clustering for paired data based on the regression of a mixture of Bayesian hierarchical models on covariates. Zhang et al. (2023) <doi:10.1186/s12859-023-05556-x>.

Exploratory principal component analysis for large-scale dataset, including sparse principal component analysis and sparse matrix approximation.

Tidied datasets of confirmed exoplanets and their host stars, retrieved from NASA's Exoplanet Archive via the REXoplanets package. Column names have been simplified for classroom use, with units baked into names where ambiguous. Intended as a companion dataset for introductory statistics teaching in the spirit of the ModernDive textbook.