This is a package for binomial and Poisson regression for clustered data, fixed and random effects with bootstrapping.

This package provides utilities based on libpoppler for extracting text, fonts, attachments and metadata from a PDF file. It also supports high quality rendering of PDF documents into PNG, JPEG, TIFF format, or into raw bitmap vectors for further processing in R.

This package provides meta-analysis methods that correct for publication bias and outcome reporting bias. Four methods and a visual tool are currently included in the package.

1. The p-uniform method as described in van Assen, van Aert, and Wicherts (2015) doi:10.1037/met0000025 can be used for estimating the average effect size, testing the null hypothesis of no effect, and testing for publication bias using only the statistically significant effect sizes of primary studies.

2. The p-uniform* method as described in van Aert and van Assen (2019) doi:10.31222/osf.io/zqjr9. This method is an extension of the p-uniform method that allows for estimation of the average effect size and the between-study variance in a meta-analysis, and uses both the statistically significant and nonsignificant effect sizes.

3. The hybrid method as described in van Aert and van Assen (2017) doi:10.3758/s13428-017-0967-6. The hybrid method is a meta-analysis method for combining an original study and replication and while taking into account statistical significance of the original study. The p-uniform and hybrid method are based on the statistical theory that the distribution of p-values is uniform conditional on the population effect size.

4. The fourth method in the package is the Snapshot Bayesian Hybrid Meta-Analysis Method as described in van Aert and van Assen (2018) doi:10.1371/journal.pone.0175302. This method computes posterior probabilities for four true effect sizes (no, small, medium, and large) based on an original study and replication while taking into account publication bias in the original study. The method can also be used for computing the required sample size of the replication akin to power analysis in null hypothesis significance testing.

The meta-plot is a visual tool for meta-analysis that provides information on the primary studies in the meta-analysis, the results of the meta-analysis, and characteristics of the research on the effect under study (van Assen and others, 2020).

Helper functions to apply the Correcting for Outcome Reporting Bias (CORB) method to correct for outcome reporting bias in a meta-analysis (van Aert & Wicherts, 2020).

This package provides efficient tools to compute the proximity between rows or columns of large matrices. Functions are optimised for large sparse matrices using the Armadillo and Intel TBB libraries. Among several built-in similarity/distance measures, computation of correlation, cosine similarity and Euclidean distance is particularly fast.

This package lets you construct Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Results Data objects. These objects are used and re-used to construct summary tables, visualizations, and written reports. The package also exports utilities for working with these objects and creating new Analysis Results Data objects.

Writing interfaces to command line software is cumbersome. The cmdfun package provides a framework for building function calls to seamlessly interface with shell commands by allowing lazy evaluation of command line arguments. It also provides methods for handling user-specific paths to tool installs or secrets like API keys. Its focus is to equally serve package builders who wish to wrap command line software, and to help analysts stay inside R when they might usually leave to execute non-R software.

This package fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models.

Enrich your ggplots with group-wise comparisons. This package provides an easy way to indicate if two groups are significantly different. Commonly this is shown by a bracket on top connecting the groups of interest which itself is annotated with the level of significance. The package provides a single layer that takes the groups for comparison and the test as arguments and adds the annotation to the plot.

This package provides tools for defensive programming. It is inspired by purrr mappers and based on rlang. Attempt extends and facilitates defensive programming by providing a consistent grammar, and a set of functions for common tests and conditions. Attempt only depends on rlang, and focuses on speed, so it can be integrated with other functions and used in the data analysis.

This package provides tools that allow you to recreate the parsing, evaluation and display of R code, with enough information that you can accurately recreate what happens at the command line. The tools can easily be adapted for other output formats, such as HTML or LaTeX.

This package represents an implementation of functions to optimize ordering of nodes in a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can be sorted based on the average distance of subtrees, or based on the smallest distance value. These sorting methods improve readability and interpretability of tree structure, especially for tasks such as comparison of different distance measures or linkage types and identification of tight clusters and outliers. As a result, it also introduces more meaningful reordering for a coupled heatmap visualization.

This r-acceptancesampling provides functionality for creating and evaluating acceptance sampling plans. Acceptance sampling is a methodology commonly used in quality control and improvement. International standards of acceptance sampling provide sampling plans for specific circumstances. The aim of this package is to provide an easy-to-use interface to visualize single, double or multiple sampling plans. In addition, methods have been provided to enable the user to assess sampling plans against pre-specified levels of performance, as measured by the probability of acceptance for a given level of quality in the lot.

This package provides alternative implementations of some base R functions, including sort, order, and match. The functions are simplified but can be faster or have other advantages.

The zlog package offers functions to transform laboratory measurements into standardised z or z(log)-values. Therefore the lower and upper reference limits are needed. If these are not known they could be estimated from a given sample.

This package provides tools to perform analyses and combine results from multiple-imputation datasets.

This package provides a set of R functions for identifying and correcting HGNC human gene symbols. In addition, you can identify MGI mouse gene symbols, which have been converted to date format by Excel, withdrawn, or aliased. It also contains functions for reversibly converting between HGNC symbols and valid R names.

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

This package provides color palettes. They are checked for colorblind accessibility from hue, saturation, and lightness value scaling using the Chroma.js Color Palette Helper. See https://gka.github.io/palettes.

This package lets you compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and machine learning models in R. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference. Details can be found in Arel-Bundock, Greifer, and Heiss (2024) <doi:10.18637/jss.v111.i09>.

This package provides various R programming tools for model fitting.

This package implements various measures of information theory based on several entropy estimators.

This R package downloads labeled single-cell RNA-seq data from PanglaoDB. It merges the data into a Seurat object for streamlined analysis.

Apache Arrow is a cross-language development platform for in-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. This package provides an R interface to the Arrow C++ library.

This package provides methods for manipulating regression models and for describing these in a style adapted for medical journals. It contains functions for generating an HTML table with crude and adjusted estimates, plotting hazard ratio, plotting model estimates and confidence intervals using forest plots, extending this to comparing multiple models in a single forest plots. In addition to the descriptive methods, there are functions for the robust covariance matrix provided by the sandwich package, a function for adding non-linearities to a model, and a wrapper around the Epi package's Lexis() functions for time-splitting a dataset when modeling non-proportional hazards in Cox regressions.