Implementation of uniformly most powerful invariant equivalence tests for one- and two-sample problems (paired and unpaired) as described in Wellek (2010, ISBN:978-1-4398-0818-4). Also one-sided alternatives (non-inferiority and non-superiority tests) are supported. Basically a variant of a t-test with (relaxed) null and alternative hypotheses exchanged.

This package provides a fast, flexible tool for generating disease surveillance reports from data exported from EpiTrax', a central repository for epidemiological data used by public health officials. It provides functions to manipulate EpiTrax datasets, tailor reports to internal or public use, and export reports in CSV, Excel xlsx', or PDF formats.

Basic sensitivity analysis of the observed relative risks adjusting for unmeasured confounding and misclassification of the exposure/outcome, or both. It follows the bias analysis methods and examples from the book by Fox M.P., MacLehose R.F., and Lash T.L. "Applying Quantitative Bias Analysis to Epidemiologic Data, second ed.", ('Springer', 2021).

This package provides functions to estimate model parameters and forecast future volatilities using the Unified GARCH-Ito [Kim and Wang (2016) <doi:10.1016/j.jeconom.2016.05.003>] and Realized GARCH-Ito [Song et. al. (2020) <doi:10.1016/j.jeconom.2020.07.007>] models. Optimization is done using augmented Lagrange multiplier method.

Computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed. See Taylor Arnold and Ryan Tibshirani (2016) <doi:10.1080/10618600.2015.1008638>.

The correlations and linkage disequilibrium between tests can vary as a function of minor allele frequency thresholds used to filter variants, and also varies with different choices of test statistic for region-based tests. Appropriate genome-wide significance thresholds can be estimated empirically through permutation on only a small proportion of the whole genome.

Create plots that combine a phylogeny and frequency dynamics. Phylogenetic input can be a generic adjacency matrix or a tree of class "phylo". Inspired by similar plots in publications of the labs of RE Lenski and JE Barrick. Named for HJ Muller (who popularised such plots) and H Wickham (whose code this package exploits).

The HistData package provides a collection of small data sets that are interesting and important in the history of statistics and data visualization. The goal of the package is to make these available, both for instructional use and for historical research. Some of these present interesting challenges for graphics or analysis in R.

This package implements the Information Matrix test for regression models following Cameron, A. C., & Trivedi, P. K. (1990) <https://cameron.econ.ucdavis.edu/research/imtest_impliedalternatives_ucdwp372.pdf> Decomposes the test into components for heteroscedasticity, skewness, and kurtosis to diagnose specific forms of misspecification. Provides both overall and component-wise statistics for model assessment.

It constructs a Consensus Network which identifies the general information of all the layers and Specific Networks for each layer with the information present only in that layer and not in all the others.The method is described in Policastro et al. (2024) "INet for network integration" <doi:10.1007/s00180-024-01536-8>.

Create small multiples of several leaflet web maps with (optional) synchronised panning and zooming control. When syncing is enabled all maps respond to mouse actions on one map. This allows side-by-side comparisons of different attributes of the same geometries. Syncing can be adjusted so that any combination of maps can be synchronised.

The MIMS-unit algorithm is developed to compute Monitor Independent Movement Summary Unit, a measurement to summarize raw accelerometer data while ensuring harmonized results across different devices. It also includes scripts to reproduce results in the related publication (John, D., Tang. Q., Albinali, F. and Intille, S. (2019) <doi:10.1123/jmpb.2018-0068>).

Perform a differential analysis at pathway level based on metabolite quantifications and information on pathway metabolite composition. The method, described in Guilmineau et al (2025) <doi:10.1186/s12859-025-06118-z> is based on a Principal Component Analysis step and on a linear mixed model. Automatic query of metabolic pathways is also implemented.

This package provides a very small package for more convenient use of NaileR'. You provide a data set containing a latent variable you want to understand. It generates a description and an interpretation of this latent variable using a Large Language Model. For perceptual data, it describes the stimuli used in the experiment.

Use the paged media properties in CSS and the JavaScript library paged.js to split the content of an HTML document into discrete pages. Each page can have its page size, page numbers, margin boxes, and running headers, etc. Applications of this package include books, letters, reports, papers, business cards, resumes, and posters.

By adding dependencies to the "Suggests" field of a package's DESCRIPTION file, and then declaring that they are needed within any dependent functionality, it is often possible to significantly reduce the number of "hard" dependencies required by a package. This package provides a minimal way to declare when a suggested package is needed.

Population genetics package for designing diagnostic panels. Candidate markers, marker combinations, and different panel sizes are assessed for how well they can predict the source population of known samples. Requires a genotype file of candidate markers in STRUCTURE format. Methods for population cross-validation are described in Jombart (2008) <doi:10.1093/bioinformatics/btn129>.

This package provides utilities for generating SQL queries (particularly CREATE TABLE statements) from R model objects. The most important use case is generating SQL to score a generalized linear model or related model represented as an R object, in which case the package handles parsing formula operators and including the model's response function.

Fits Bayesian spatio-temporal models and makes predictions on stream networks using the approach by Santos-Fernandez, Edgar, et al. (2022)."Bayesian spatio-temporal models for stream networks". <arXiv:2103.03538>. In these models, spatial dependence is captured using stream distance and flow connectivity, while temporal autocorrelation is modelled using vector autoregression methods.

Calculates topic-specific diagnostics (e.g. mean token length, exclusivity) for Latent Dirichlet Allocation and Correlated Topic Models fit using the topicmodels package. For more details, see Chapter 12 in Airoldi et al. (2014, ISBN:9781466504080), pp 262-272 Mimno et al. (2011, ISBN:9781937284114), and Bischof et al. (2014) <arXiv:1206.4631v1>.

Operators and functions provided by base R sometimes lack some features found in other programming languages, such as the ability to concatenate strings using + or to repeat strings using *. This package aims at providing such functionality without breaking existing code, i.e., only statements, that would throw errors in pure base R are patched.

Implementation of zero-inflated Poisson models under Bayesian framework using data augmentation as discussed in Chapter 5 of Zhang (2020) <https://hdl.handle.net/10012/16378>. This package is constructed in accommodating four different scenarios: the general scenario, the scenario with measurement error in responses, the external validation scenario, and the internal validation scenario.

Parse GFF and GTF files using C++ classes. The package also provides utilities to read and write GFF3 files. The GFF (General Feature Format) format is a tab-delimited file format for describing genes and other features of DNA, RNA, and protein sequences. GFF files are often used to describe the features of genomes.

GDS files are widely used to represent genotyping or sequence data. The GDSArray package implements the `GDSArray` class to represent nodes in GDS files in a matrix-like representation that allows easy manipulation (e.g., subsetting, mathematical transformation) in _R_. The data remains on disk until needed, so that very large files can be processed.