Compute a cyclist's Eddington number, including efficiently computing cumulative E over a vector. A cyclist's Eddington number <https://en.wikipedia.org/wiki/Arthur_Eddington#Eddington_number_for_cycling> is the maximum number satisfying the condition such that a cyclist has ridden E miles or greater on E distinct days. The algorithm in this package is an improvement over the conventional approach because both summary statistics and cumulative statistics can be computed in linear time, since it does not require initial sorting of the data. These functions may also be used for computing h-indices for authors, a metric described by Hirsch (2005) <doi:10.1073/pnas.0507655102>. Both are specific applications of computing the side length of a Durfee square <https://en.wikipedia.org/wiki/Durfee_square>. Some additional author-level metrics such as g-index and i10-index are also included in the package.

Runs a series of configurable tests against a user's compute environment. This can be used for checking that things like a specific directory or an environment variable is available before you start an analysis. Alternatively, you can use the package's situation report when filing error reports with your compute infrastructure.

This package provides a collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data.

The nonparametric trend and its derivatives in equidistant time series (TS) with long-memory errors can be estimated. The estimation is conducted via local polynomial regression using an automatically selected bandwidth obtained by a built-in iterative plug-in algorithm or a bandwidth fixed by the user. The smoothing methods of the package are described in Letmathe, S., Beran, J. and Feng, Y., (2023) <doi:10.1080/03610926.2023.2276049>.

This package provides tools for conducting epigenome-wide association studies (EWAS) and visualizing results. Users provide sample metadata and methylation matrices to run EWAS with linear models, linear mixed-effects models, or Cox models. The package supports downstream visualization, bootstrap validation, enrichment analysis, batch effect correction, and differentially methylated region (DMR) analysis with optional parallel computing. Methods are described in Wang et al. (2025) <doi:10.1093/bioadv/vbaf026>, Johnson et al. (2007) <doi:10.1093/biostatistics/kxj037>, and Peters et al. (2015) <doi:10.1186/1756-8935-8-6>.

This package provides methods for analyzing R by C ecological contingency tables using the extreme case analysis, ecological regression, and Multinomial-Dirichlet ecological inference models. Also provides tools for manipulating higher-dimension data objects.

Recently many new p-value based multiple test procedures have been proposed, and these new methods are more powerful than the widely used Hochberg procedure. These procedures strongly control the familywise error rate (FWER). This is a comprehensive collection of p-value based FWER-control stepwise multiple test procedures, including six procedure families and thirty multiple test procedures. In this collection, the conservative Hochberg procedure, linear time Hommel procedures, asymptotic Rom procedure, Gou-Tamhane-Xi-Rom procedures, and Quick procedures are all developed in recent five years since 2014. The package name "elitism" is an acronym of "e"quipment for "l"ogarithmic and l"i"near "ti"me "s"tepwise "m"ultiple hypothesis testing. See Gou, J. (2022), "Quick multiple test procedures and p-value adjustments", Statistics in Biopharmaceutical Research 14(4), 636-650.

This package provides a collection of functions for microbial ecology and other applications of genomics and metagenomics. Companion package for the Enveomics Collection (Rodriguez-R, L.M. and Konstantinidis, K.T., 2016 <DOI:10.7287/peerj.preprints.1900v1>).

This package provides utility functions for standardizing economic entity (economy, aggregate, institution, etc.) name and id in economic datasets such as those published by the International Monetary Fund and World Bank. Aims to facilitate consistent data analysis, reporting, and joining across datasets. Used as a foundational building block in the EconDataverse family of packages (<https://www.econdataverse.org>).

Returns the noncentrality parameter of the noncentral F distribution if probability of type I and type II error, degrees of freedom of the numerator and the denominator are given. It may be useful for computing minimal detectable differences for general ANOVA models. This program is documented in the paper of A. Baharev, S. Kemeny, On the computation of the noncentral F and noncentral beta distribution; Statistics and Computing, 2008, 18 (3), 333-340.

This package provides functions for selecting attributes from a given dataset. Attribute subset selection is the process of identifying and removing as much of the irrelevant and redundant information as possible.

Project scaffolding and workflow tools for reproducible data science. Manages packages, tracks data integrity, handles database connections, generates notebooks, and publishes to S3-compatible storage. More information at <https://framework.table1.org>.

All data sets required for the examples and exercises in the book "Forecasting: principles and practice" by Rob J Hyndman and George Athanasopoulos <https://OTexts.com/fpp3/>. All packages required to run the examples are also loaded. Additional data sets not used in the book are also included.

This package provides a full set of fast data manipulation tools with a tidy front-end and a fast back-end using collapse and cheapr'.

This package provides a selection of 3 different inference rules (including additionally the clamped types of the referred inference rules) and 4 threshold functions in order to obtain the inference of the FCM (Fuzzy Cognitive Map). Moreover, the fcm package returns a data frame of the concepts values of each state after the inference procedure. Fuzzy cognitive maps were introduced by Kosko (1986) <doi:10.1002/int.4550010405> providing ideal causal cognition tools for modeling and simulating dynamic systems.

Proposes non-parametric estimates of the Fisher information measure and the Shannon entropy power. More theoretical and implementation details can be found in Guignard et al. <doi:10.3389/feart.2020.00255>. A python version of this work is available on github and PyPi ('FiShPy').

This package provides tools, helpers and data structures for developing models and time series functions for fable and extension packages. These tools support a consistent and tidy interface for time series modelling and analysis.

Interactive forest plot for clinical trial safety analysis using metalite', reactable', plotly', and Analysis Data Model (ADaM) datasets. Includes functionality for adverse event filtering, incidence-based group filtering, hover-over reveals, and search and sort operations. The workflow allows for metadata construction, data preparation, output formatting, and interactive plot generation.

This package provides methods for performing fMRI quality assurance (QA) measurements of test objects. Heavily based on the fBIRN procedures detailed by Friedman and Glover (2006) <doi:10.1002/jmri.20583>.

Application of the filtered monotonic polynomial (FMP) item response model to flexibly fit item response models. The package includes tools that allow the item response model to be build on any monotonic transformation of the latent trait metric, as described by Feuerstahler (2019) <doi:10.1007/s11336-018-9642-9>.

Computes Fourier integrals of functions of one and two variables using the Fast Fourier transform. The Fourier transforms must be evaluated on a regular grid for fast evaluation.

Growth models and forest production require existing data manipulation and the creation of new data, structured from basic forest inventory data. The purpose of this package is provide functions to support these activities.

This package provides doubly robust one-step and targeted maximum likelihood (TMLE) estimators for average causal effects in acyclic directed mixed graphs (ADMGs) with unmeasured variables. Automatically determines whether the treatment effect is identified via backdoor adjustment or the extended front-door functional, and dispatches to the appropriate estimator. Supports incorporation of machine learning algorithms via SuperLearner and cross-fitting for nuisance estimation. Methods are described in Guo and Nabi (2024) <doi:10.48550/arXiv.2409.03962>.

Streamlines the process of updating changelogs (NEWS.md) and versioning R packages developed in git repositories.