This package implements methods that are useful in designing research studies and analyzing data, with particular emphasis on methods that are developed for or used within the behavioral, educational, and social sciences (broadly defined). That being said, many of the methods implemented within MBESS are applicable to a wide variety of disciplines. MBESS has a suite of functions for a variety of related topics, such as effect sizes, confidence intervals for effect sizes (including standardized effect sizes and noncentral effect sizes), sample size planning (from the accuracy in parameter estimation (AIPE), power analytic, equivalence, and minimum-risk point estimation perspectives), mediation analysis, various properties of distributions, and a variety of utility functions.

When testing multiple hypotheses simultaneously, this package provides functionality to calculate a lower bound for the number of correct rejections (as a function of the number of rejected hypotheses), which holds simultaneously -with high probability- for all possible number of rejections. As a special case, a lower bound for the total number of false null hypotheses can be inferred. Dependent test statistics can be handled for multiple tests of associations. For independent test statistics, it is sufficient to provide a list of p-values.

Functions and examples are provided for transmission/disequilibrium tests for extended marker haplotypes, as in Clayton, D. and Jones, H. (1999) "Transmission/disequilibrium tests for extended marker haplotypes".

This package provides methods for fast access to large ASCII files. Currently the following file formats are supported: comma separated format (CSV) and fixed width format. It is assumed that the files are too large to fit into memory, although the package can also be used to efficiently access files that do fit into memory. Methods are provided to access and process files blockwise. Furthermore, an opened file can be accessed as one would an ordinary data.frame. The LaF vignette gives an overview of the functionality provided.

Zero-variance control variates (ZV-CV) is a post-processing method to reduce the variance of Monte Carlo estimators of expectations using the derivatives of the log target. Once the derivatives are available, the only additional computational effort is in solving a linear regression problem. This method has been extended to higher dimensions using regularisation. This package can be used to easily perform ZV-CV or regularised ZV-CV when a set of samples, derivatives and function evaluations are available. Additional functions for applying ZV-CV to two estimators for the normalising constant of the posterior distribution in Bayesian statistics are also supplied.

The mlr3 package family is a set of packages for machine-learning purposes built in a modular fashion. This wrapper package is aimed to simplify the installation and loading of the core mlr3 packages.

This package contains methods for the detection of clusters in hierarchical clustering dendrograms.

This package provides an interface to Amazon Web Services management and governance services, including CloudWatch application and infrastructure monitoring, Auto Scaling for automatically scaling resources, and more.

The biglm package lets you create a linear model object that uses only codep^2 memory for p variables. It can be updated with more data using update. This allows linear regression on data sets larger than memory.

This package implements many algorithms for statistical learning on sparse matrices: matrix factorizations, matrix completion, elastic net regressions, factorization machines. The rsparse package also enhances the Matrix package by providing methods for multithreaded <sparse, dense> matrix products and native slicing of the sparse matrices in Compressed Sparse Row (CSR) format.

This package provides a shared resource interface for the bigmemory and synchronicity packages.

This package provides an implementation of maximum likelihood estimators for a variety of heavy tailed distributions, including both the discrete and continuous power law distributions. Additionally, a goodness-of-fit based approach is used to estimate the lower cut-off for the scaling region.

This package provides miscellaneous functions for SciViews or general use, including tools to manage a temporary environment attached to the search path for temporary variables you do not want to save() or load(); test the current platform; showing progress bars, etc.

This package is designed to be used with Rscript to write shebang scripts that accept short and long options. Many users will prefer to use the packages optparse or argparse which add extra features like automatically generated help options and usage texts, support for default values, positional argument support, etc.

Read large text files by splitting them in smaller files. This package also provides some convenient wrappers around fread() and fwrite() from package data.table.

This package provides tools to create a lightweight Shiny wrapper for the css-loaders created by Luke Hass https://github.com/lukehaas/css-loaders. Wrapping a Shiny output will automatically show a loader when the output is (re)calculating.

This package contains genomic data for the plant pathogen Phytophthora infestans. It includes a variant file, a sequence file and an annotation file. This package is intended to be used as example data for packages that work with genomic data.

This package provides different high-level graphics functions for displaying large datasets, displaying circular data in a very flexible way, finding local maxima, brewing color ramps, drawing nice arrows, zooming 2D-plots, creating figures with differently colored margin and plot region. In addition, the package contains auxiliary functions for data manipulation like omitting observations with irregular values or selecting data by logical vectors, which include NAs. Other functions are especially useful in spectroscopy and analyses of environmental data: robust baseline fitting, finding peaks in spectra, converting humidity measures.

This package implements a DBI-compliant interface to MariaDB and MySQL databases.

This package provides functions and datasets for bootstrapping from the book "Bootstrap Methods and Their Application" by A.C. Davison and D.V. Hinkley (1997, CUP), originally written by Angelo Canty for S.

This package provides an implementation of robust location and scatter estimation and robust multivariate analysis with high breakdown point.

This package provides an R-based solution for symbolic differentiation. It admits user-defined functions as well as function substitution in arguments of functions to be differentiated. Some symbolic simplification is part of the work.

This package provides tidy tools for quantifying how well a model fits to a data set such as confusion matrices, class probability curve summaries, and regression metrics (e.g., RMSE).

This package provides access to phyloinformatic data in NeXML format. The package should add new functionality to R such as the possibility to manipulate NeXML objects in more various and refined way and compatibility with ape objects.