These utilities facilitate the programmatic manipulations of formulas, expressions, calls, assignments and other R language objects. These objects all share the same structure: a left-hand side, operator and right-hand side. This package provides methods for accessing and modifying this structures as well as extracting and replacing names and symbols from these objects.

This package computes standardized mean differences and confidence intervals for multiple data types based on Yang, D., & Dalton, J. E. (2012) <https://support.sas.com/resources/papers/proceedings12/335-2012.pdf>.

This package provides .C64(), an enhanced version of .C() and .Fortran() from the R foreign function interface. .C64() supports long vectors, arguments of type 64-bit integer, and provides a mechanism to avoid unnecessary copies of read-only and write-only arguments. This makes it a convenient and fast interface to C/C++ and Fortran code.

This package provides functionality to dynamically define R functions and S4 methods with inlined C, C++ or Fortran code supporting .C and .Call calling conventions.

This package provides various methods for MRI tissue classification.

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 R bindings to the uchardet encoding detector library from Mozilla. It takes a sequence of bytes in an unknown character encoding without any additional information, and attempts to get the encoding of the text. All return names of the encodings are iconv-compatible.

This package simplifies regression tests by comparing objects produced by test code with earlier versions of those same objects. If objects are unchanged the tests pass, otherwise execution stops with error details. If in interactive mode, tests can be reviewed through the provided interactive environment.

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 performs prediction of a response function from simulated response values, allowing black-box optimization of functions estimated with some error. It includes a simple user interface for such applications, as well as more specialized functions designed to be called by the Migraine software (Rousset and Leblois, 2012 <doi:10.1093/molbev/MSR262>; Leblois et al., 2014 <doi:10.1093/molbev/msu212>; and see URL). The latter functions are used for prediction of likelihood surfaces and implied likelihood ratio confidence intervals, and for exploration of predictor space of the surface. Prediction of the response is based on ordinary Kriging (with residual error) of the input. Estimation of smoothing parameters is performed by generalized cross-validation.

Meta-analysis is widely used to summarize estimated effects sizes across multiple statistical tests. Standard fixed and random effect meta-analysis methods assume that the estimated of the effect sizes are statistically independent. Here we relax this assumption and enable meta-analysis when the correlation matrix between effect size estimates is known.

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.

RestRserve is an R web API framework for building high-performance AND robust microservices and app backends. With Rserve backend on UNIX-like systems it is parallel by design. It will handle incoming requests in parallel - each request in a separate fork.

This package provides an R interface to the dygraphs JavaScript charting library (a copy of which is included in the package). It provides rich facilities for charting time-series data in R, including highly configurable series- and axis-display and interactive features like zoom/pan and series/point highlighting.

This package provides some helpful extensions and modifications to the ggplot2 package to combine multiple ggplot2 plots into one and label them with letters, as is often required for scientific publications.

This package provides a collection of fast (utility) functions for data analysis. Column- and row- wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions.

This package is a port of the gWidgets2 API for the tcltk package.

This package provides tools to fit and predict with the high-dimensional principal fitted components model. This model is described by Cook, Forzani, and Rothman (2012) doi:10.1214/11-AOS962.

This package provides mosaic plots for the ggplot2 framework. Mosaic plot functionality is provided in a single ggplot2 layer by calling the geom mosaic.

This package implements fast hierarchical, agglomerative clustering routines. Part of the functionality is designed as drop-in replacement for existing routines: linkage() in the SciPy package scipy.cluster.hierarchy, hclust() in R's stats package, and the flashClust package. It provides the same functionality with the benefit of a much faster implementation. Moreover, there are memory-saving routines for clustering of vector data, which go beyond what the existing packages provide.

This package enhances the ROI with the lp_solve solver.

Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). This package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided.

This package provides tools for data frame summaries, cross-tabulations, weight-enabled frequency tables and common univariate statistics in concise tables available in a variety of formats (plain ASCII, Markdown and HTML). A good point-of-entry for exploring data, both for experienced and new R users.

This package converts between a number of bibliography formats, including BibTeX, BibLaTeX and Bibentry. It includes a port of the bibutils utilities and supports all bibliography formats and character encodings implemented in bibutils.