For outlier detection in small and normally distributed samples the ratio test of Dixon (Q-test) can be used. Density, distribution function, quantile function and random generation for Dixon's ratio statistics are provided as wrapper functions. The core applies McBane's Fortran functions that use Gaussian quadrature for a numerical solution.

This package loads electrophysiology data from ABF2 files, as created by Axon Instruments/Molecular Devices software. Only files recorded in gap-free mode are currently supported.

Perform common useful JavaScript operations in Shiny apps that will greatly improve your apps without having to know any JavaScript. Examples include: hiding an element, disabling an input, resetting an input back to its original value, delaying code execution by a few seconds, and many more useful functions for both the end user and the developer. Shinyjs can also be used to easily call your own custom JavaScript functions from R.

This package provides tools to query the U.S. National Library of Medicine's Clinical Trials database. Functions are provided for a variety of techniques for searching the data using range queries, categorical filtering, and by searching for full-text keywords. Minimal graphical tools are also provided for interactively exploring the constructed data.

This package can automatically extract statistical null-hypothesis significant testing (NHST) results from articles and recompute the p-values based on the reported test statistic and degrees of freedom to detect possible inconsistencies.

This package provides various themes, palettes, and other functions that are used to customise ggplots to look like they were made in GraphPad Prism. The Prism-look is achieved with theme_prism() and scale_fill|colour_prism(), axes can be changed with custom guides like guide_prism_minor(), and significance indicators added with add_pvalue().

This package provides a small wrapper on regexpr to extract the matches and captured groups from the match of a regular expression to a character vector.

This package provides classes and methods for dense and sparse matrices and operations on them using LAPACK and SuiteSparse.

This package provides tools to calculate the Earth Mover's Distance (EMD).

This package contains functions useful for correlation theory, meta-analysis (validity-generalization), reliability, item analysis, inter-rater reliability, and classical utility.

This package provides functions for working with magnetic resonance images. It supports reading and writing of popular file formats (DICOM, Analyze, NIfTI-1, NIfTI-2, MGH); interactive and non-interactive visualization; flexible image manipulation; metadata and sparse image handling.

This package provides an interface to figshare, a scientific repository to archive and assign DOIs to data, software, figures, and more.

This package analyzes data with robust methods such as regression methodology including model selections and multivariate statistics.

This package implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging algorithm using classification trees as individual classifiers. Once these classifiers have been trained, they can be used to predict on new data. Also, cross validation estimation of the error can be done.

This package provides tools for data importation, recoding, and inspection. There are functions to create new project folders, R code templates, create uniquely named output directories, and to quickly obtain a visual summary for each variable in a data frame. The main feature here is the systematic implementation of the "variable key" framework for data importation and recoding.

This package provides maximally selected rank statistics with several p-value approximations.

This package contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included.

This package provides a toolkit for all URL-handling needs, including encoding and decoding, parsing, parameter extraction and modification. All functions are designed to be both fast and entirely vectorized. It is intended to be useful for people dealing with web-related datasets, such as server-side logs, although may be useful for other situations involving large sets of URLs.

This package implements a general framework for finite mixtures of regression models using the EM algorithm. FlexMix provides the E-step and all data handling, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering.

Create tree structures from hierarchical data, and traverse the tree in various orders. Aggregate, cumulate, print, plot, convert to and from data.frame and more. This is useful for decision trees, machine learning, finance, conversion from and to JSON, and many other applications.

This package provides procedures to answer the following questions: How much ram do you need to store a 100,000 by 100,000 matrix? How much ram is your current R session using? How much ram do you even have?

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>.

The gdtools package provides functionalities to get font metrics and to generate base64 encoded string from raster matrix.

Framework for visualising tables of counts, proportions and probabilities. The framework is called product plots, alluding to the computation of area as a product of height and width, and the statistical concept of generating a joint distribution from the product of conditional and marginal distributions. The framework, with extensions, is sufficient to encompass over 20 visualisations previously described in fields of statistical graphics and infovis, including bar charts, mosaic plots, treemaps, equal area plots and fluctuation diagrams.