This package provides alternative statistical methods for meta-analysis, including:

1. bivariate generalized linear mixed models for synthesizing odds ratios, relative risks, and risk differences

2. heterogeneity tests and measures that are robust to outliers;

3. measures, tests, and visualization tools for publication bias or small-study effects;

4. meta-analysis of diagnostic tests for synthesizing sensitivities, specificities, etc.;

5. meta-analysis methods for synthesizing proportions;

6. models for multivariate meta-analysis.

This package provides a comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e. mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g. due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g. due to phylogenetic relatedness) can also be conducted.

This package provides functions related to L-moments: computation of L-moments and trimmed L-moments of distributions and data samples; parameter estimation; L-moment ratio diagram; plot vs. quantiles of an extreme-value distribution.

This package contains methods described by Dennis Helsel in his book Nondetects and Data Analysis: Statistics for Censored Environmental Data.

iheatmapr is an R package for building complex, interactive heatmaps using modular building blocks. "Complex" heatmaps are heatmaps in which subplots along the rows or columns of the main heatmap add more information about each row or column. For example, a one column additional heatmap may indicate what group a particular row or column belongs to. Complex heatmaps may also include multiple side by side heatmaps which show different types of data for the same conditions. Interactivity can improve complex heatmaps by providing tooltips with information about each cell and enabling zooming into interesting features. iheatmapr uses the plotly library for interactivity.

Recipes is an extensible framework to create and preprocess design matrices. Recipes consist of one or more data manipulation and analysis "steps". Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting design matrices can then be used as inputs into statistical or machine learning models.

Data exploration and modelling is a process in which a lot of data artifacts are produced. Artifacts like: subsets, data aggregates, plots, statistical models, different versions of data sets and different versions of results. Archivist helps to store and manage artifacts created in R. It allows you to store selected artifacts as binary files together with their metadata and relations. Archivist allows sharing artifacts with others. It can look for already created artifacts by using its class, name, date of the creation or other properties. It also makes it easy to restore such artifacts.

Render R Markdown to Markdown (without using knitr), and Markdown to lightweight HTML or LaTeX documents with the commonmark package (instead of Pandoc). Some missing Markdown features in commonmark are also supported, such as raw HTML or LaTeX blocks, LaTeX math, superscripts, subscripts, footnotes, element attributes, and appendices, but not all Pandoc Markdown features are (or will be) supported. With additional JavaScript and CSS, you can also create HTML slides and articles. This package can be viewed as a trimmed-down version of R Markdown and knitr. It does not aim at rich Markdown features or a large variety of output formats (the primary formats are HTML and LaTeX). Book and website projects of multiple input documents are also supported.

This package provides convenience functions for analyzing factorial experiments using ANOVA or mixed models.

This package provides template functions to assist in building friendly R packages that praise their users.

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 the URL checking tools available in R 4.1+ as a package for earlier versions of R. It also uses concurrent requests so can be much faster than the serial versions.

ICGE is a package that helps to estimate the number of real clusters in data as well as to identify atypical units. The underlying methods are based on distances rather than on unit x variables.

This package lets you expand factors, characters and other eligible classes into dummy/indicator variables.

This package provides routines for the analysis of indirectly measured haplotypes. The statistical methods assume that all subjects are unrelated and that haplotypes are ambiguous (due to unknown linkage phase of the genetic markers). The main functions are: haplo.em(), haplo.glm(), haplo.score(), and haplo.power(); all of which have detailed examples in the vignette.

This package exposes R bindings to jsTree, a JavaScript library that supports interactive trees, to enable rich, editable trees in Shiny.

This package lets you construct paths to your project's files. Use the here function as a drop-in replacement for file.path, it will always locate the files relative to your project root.

This package provides extended data frames, with a special data frame column which contains two indexes, with potentially a nesting structure.

This package provides interfaces to audio devices (mainly sample-based) from R to allow recording and playback of audio.

This package implements functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the spatstat family of packages. Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.

This package provides a collection of clean R Markdown HTML document templates using classless CSS styles. These documents use a minimal set of dependencies but still look great, making them suitable for use a package vignettes or for sharing results via email.

This package provides an infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). It also provides C implementations of the association mining algorithms Apriori and Eclat.

The revgeo procedure allows you to use the Photon geocoder for OpenStreetMap, Google Maps, and Bing to reverse geocode coordinate pairs with minimal hassle.

This package provides convenience functions for advanced linear algebra with tensors and computation with datasets of tensors on a higher level abstraction. It includes Einstein and Riemann summing conventions, dragging, co- and contravariate indices, and parallel computations on sequences of tensors.