The routine twosample_test() in this package runs the two sample test using various test statistic. The p values are found via permutation or large sample theory. The routine twosample_power() allows the calculation of the power in various cases, and plot_power() draws the corresponding power graphs. The routine run.studies allows a user to quickly study the power of a new method and how it compares to some of the standard ones.

The functions in this package compute robust estimators by minimizing a kernel-based distance known as MMD (Maximum Mean Discrepancy) between the sample and a statistical model. Recent works proved that these estimators enjoy a universal consistency property, and are extremely robust to outliers. Various optimization algorithms are implemented: stochastic gradient is available for most models, but the package also allows gradient descent in a few models for which an exact formula is available for the gradient. In terms of distribution fit, a large number of continuous and discrete distributions are available: Gaussian, exponential, uniform, gamma, Poisson, geometric, etc. In terms of regression, the models available are: linear, logistic, gamma, beta and Poisson. Alquier, P. and Gerber, M. (2024) <doi:10.1093/biomet/asad031> Cherief-Abdellatif, B.-E. and Alquier, P. (2022) <doi:10.3150/21-BEJ1338>.

Researchers commonly need to summarize scientific information, a process known as evidence synthesis'. The first stage of a synthesis process (such as a systematic review or meta-analysis) is to download a list of references from academic search engines such as Web of Knowledge or Scopus'. The traditional approach to systematic review is then to sort these data manually, first by locating and removing duplicated entries, and then screening to remove irrelevant content by viewing titles and abstracts (in that order). revtools provides interfaces for each of these tasks. An alternative approach, however, is to draw on tools from machine learning to visualise patterns in the corpus. In this case, you can use revtools to render ordinations of text drawn from article titles, keywords and abstracts, and interactively select or exclude individual references, words or topics.

This package provides a set of functions to simplify reading data from files. The main function, reader(), should read most common R datafile types without needing any parameters except the filename. Other functions provide simple ways of handling file paths and extensions, and automatically detecting file format and structure.

Automatically creates separate regression models for different spatial regions. The prediction surface is smoothed using a regional border smoothing method. If regional models are continuous, the resulting prediction surface is continuous across the spatial dimensions, even at region borders. Methodology is described in Wagstaff and Bean (2023) <doi:10.32614/RJ-2023-004>.

Portfolio optimization is achieved through a combination of regularization techniques and ensemble methods that are designed to generate stable out-of-sample return predictions, particularly in the presence of strong correlations among assets. The package includes functions for data preparation, parallel processing, and portfolio analysis using methods such as Mean-Variance, James-Stein, LASSO, Ridge Regression, and Equal Weighting. It also provides visualization tools and performance metrics, such as the Sharpe ratio, volatility, and maximum drawdown, to assess the results.

An R Commander plug-in providing an integrated solution to perform a series of text mining tasks such as importing and cleaning a corpus, and analyses like terms and documents counts, vocabulary tables, terms co-occurrences and documents similarity measures, time series analysis, correspondence analysis and hierarchical clustering. Corpora can be imported from spreadsheet-like files, directories of raw text files, as well as from Dow Jones Factiva', LexisNexis', Europresse and Alceste files.

Implementation of the affine-invariant method of Goodman & Weare (2010) <DOI:10.2140/camcos.2010.5.65>, a method of producing Monte-Carlo samples from a target distribution.

To detecting rare variants for binary traits using general pedigrees, the pedigree disequilibrium tests are proposed by collapsing rare haplotypes/variants with/without weights. To run the test, MERLIN is needed in Linux for haplotyping.

The Ryan-Holm step-down Bonferroni or Sidak procedure is to control the family-wise (experiment-wise) type I error rate in the multiple comparisons. This procedure provides the adjusting p-values and adjusting CIs. The methods used in this package are referenced from John Ludbrook (2000) <doi:10.1046/j.1440-1681.2000.03223.x>.

Utility functions to retrieve data from the UK National River Flow Archive (<https://nrfa.ceh.ac.uk/>, terms and conditions: <https://nrfa.ceh.ac.uk/help/costs-terms-and-conditions>). The package contains R wrappers to the UK NRFA data temporary-API. There are functions to retrieve stations falling in a bounding box, to generate a map and extracting time series and general information. The package is fully described in Vitolo et al (2016) "rnrfa: An R package to Retrieve, Filter and Visualize Data from the UK National River Flow Archive" <https://journal.r-project.org/archive/2016/RJ-2016-036/RJ-2016-036.pdf>.

This package provides a flexible alternative to the built-in rank() function called smartrank(). Optionally rank categorical variables by frequency (instead of in alphabetical order), and control whether ranking is based on descending/ascending order. smartrank() is suitable for both numerical and categorical data.

Bootstrap, permutation tests, and jackknife, featuring easy-to-use syntax.

This package implements Kornbrot's rank difference test as described in <doi:10.1111/j.2044-8317.1990.tb00939.x>. This method is a modified Wilcoxon signed-rank test which produces consistent and meaningful results for ordinal or monotonically-transformed data.

This package provides 3D plotting routines that facilitate the use of the rgl package and extend its functionality. For example, the routines allow the user to directly control the camera position & orientation, as well as to generate 3D movies with a moving observer.

Pointwise generation and display of attractors (prefractals) of the random iterated function system (RIFS) for various combinations of probabilistic and geometric parameters of some fixed point sets (protofractals), described by Bukhovets A.G. (2012) <doi:10.1134/S0005117912020154>.

Invoke a BUGS model in OpenBUGS or WinBUGS', a class "bugs" for BUGS results and functions to work with that class. Function write.model() allows a BUGS model file to be written. The class and auxiliary functions could be used with other MCMC programs, including JAGS'. The suggested package BRugs (only needed for function openbugs()) is only available from the CRAN archives, see <https://cran.r-project.org/package=BRugs>.

Tool for the analysis Mass Spectrometry (MS) data in the context of immunopeptidomic analysis for the identification of hybrid peptides and the predictions of binding affinity of all peptides using netMHCpan <doi:10.1093/nar/gkaa379> while providing a summary of the netMHCpan output. RHybridFinder (RHF) is destined for researchers who are looking to analyze their MS data for the purpose of identification of potential spliced peptides. This package, developed mainly in base R, is based on the workflow published by Faridi et al. in 2018 <doi:10.1126/sciimmunol.aar3947>.

Enhances the R Optimization Infrastructure ('ROI') package with a connection to the neos server. ROI optimization problems can be directly be sent to the neos server and solution obtained in the typical ROI style.

This package provides a part of precision agriculture is linked to the spectral image obtained from the cameras. With the image information of the agricultural experiment, the included functions facilitate the collection of spectral data associated with the experimental units. Some designs generated in R are linked to the images, which allows the use of the information of each pixel of the image in the experimental unit and the treatment. Tables and images are generated for the analysis of the precision agriculture experiment during the entire vegetative period of the crop.

Calculates risk differences (or prevalence differences for cross-sectional data) and Number Needed to Treat (NNT) using generalized linear models with automatic link function selection. Provides robust model fitting with fallback methods, support for stratification and adjustment variables, inverse probability of treatment weighting (IPTW) for causal inference with NNT calculations, and publication-ready output formatting. Handles model convergence issues gracefully and provides confidence intervals using multiple approaches. Methods are based on approaches described in Mark W. Donoghoe and Ian C. Marschner (2018) "logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model" <doi:10.18637/jss.v086.i09> for robust GLM fitting, Peter C. Austin (2011) "An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies" <doi:10.1080/00273171.2011.568786> for IPTW methods, and standard epidemiological methods for risk difference estimation as described in Kenneth J. Rothman, Sander Greenland and Timothy L. Lash (2008, ISBN:9780781755641) "Modern Epidemiology".

Calculate RNNI distance between and manipulate with ranked trees. RNNI stands for Ranked Nearest Neighbour Interchange and is an extension of the classical NNI space (space of trees created by the NNI moves) to ranked trees, where internal nodes are ordered according to their heights (usually assumed to be times). The RNNI distance takes the tree topology into account, as standard NNI does, but also penalizes changes in the order of internal nodes, i.e. changes in the order of times of evolutionary events. For more information about the RNNI space see: Gavryushkin et al. (2018) <doi:10.1007/s00285-017-1167-9>, Collienne & Gavryushkin (2021) <doi:10.1007/s00285-021-01567-5>, Collienne et al. (2021) <doi:10.1007/s00285-021-01685-0>, and Collienne (2021) <http://hdl.handle.net/10523/12606>.

This package provides a collection of R functions for use with Stock Synthesis, a fisheries stock assessment modeling platform written in ADMB by Dr. Richard D. Methot at the NOAA Northwest Fisheries Science Center. The functions include tools for summarizing and plotting results, manipulating files, visualizing model parameterizations, and various other common stock assessment tasks. This version of r4ss is compatible with Stock Synthesis versions 3.24 through 3.30 (specifically version 3.30.19.01, from April 2022).

This package provides tools for randomization-based inference. Current focus is on the d^2 omnibus test of differences of means following Hansen and Bowers (2008) <doi:10.1214/08-STS254> . This test is useful for assessing balance in matched observational studies or for analysis of outcomes in block-randomized experiments.