This package provides a supervised technique able to identify differentially expressed genes, based on the construction of \emphFuzzy Patterns (FPs). The Fuzzy Patterns are built by means of applying 3 Membership Functions to discretized gene expression values.

Formatter functions in the apa package take the return value of a statistical test function, e.g. a call to chisq.test() and return a string formatted according to the guidelines of the APA (American Psychological Association).

Continuous and discrete (count or categorical) estimation of density, probability mass function (p.m.f.) and regression functions are performed using associated kernels. The cross-validation technique and the local Bayesian procedure are also implemented for bandwidth selection.

This package provides a simple, fast algorithm to find the neighbors and similarities of users in user-based filtering systems, to break free from the complex computation of existing similarity formulas and the ability to solve big data.

Quite extensive package for maximum likelihood estimation and weighted least squares estimation of categorical marginal models (CMMs; e.g., Bergsma and Rudas, 2002, <http://www.jstor.org/stable/2700006?; Bergsma, Croon and Hagenaars, 2009, <DOI:10.1007/b12532>.

DataSHIELD is an infrastructure and series of R packages that enables the remote and non-disclosive analysis of sensitive research data. This package defines the API that is to be implemented by DataSHIELD compliant data repositories.

Add a "Did You Mean" feature to the R interactive. With this package, error messages for misspelled input of variable names or package names suggest what you really want to do in addition to notification of the mistake.

An implementation of a computational framework for performing robust structured regression with the L2 criterion from Chi and Chi (2021+). Improvements using the majorization-minimization (MM) principle from Liu, Chi, and Lange (2022+) added in Version 2.0.

We provide inference for personalized medicine models. Namely, we answer the questions: (1) how much better does a purported personalized recommendation engine for treatments do over a business-as-usual approach and (2) is that difference statistically significant?

Read SubRip <https://sourceforge.net/projects/subrip/> subtitle files as data frames for easy text analysis or manipulation. Easily shift numeric timings and export subtitles back into valid SubRip timestamp format to sync subtitles and audio.

Calculates total survey error (TSE) for one or more surveys, using common scale-dependent and/or scale-independent metrics. On TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>.

Radiomics image analysis toolbox for 2D and 3D radiological images. RIA supports DICOM, NIfTI, nrrd and npy (numpy array) file formats. RIA calculates first-order, gray level co-occurrence matrix, gray level run length matrix and geometry-based statistics. Almost all calculations are done using vectorized formulas to optimize run speeds. Calculation of several thousands of parameters only takes minutes on a single core of a conventional PC. Detailed methodology has been published: Kolossvary et al. Circ: Cardiovascular Imaging. 2017;10(12):e006843 <doi: 10.1161/CIRCIMAGING.117.006843>.

Simplifies the creation of reproducible data science environments using the Nix package manager, as described in Dolstra (2006) <ISBN 90-393-4130-3>. The included `rix()` function generates a complete description of the environment as a `default.nix` file, which can then be built using Nix'. This results in project specific software environments with pinned versions of R, packages, linked system dependencies, and other tools or programming languages such as Python or Julia. Additional helpers make it easy to run R code in Nix software environments for testing and production.

This package implements the RUV (Remove Unwanted Variation) algorithms. These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data. The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well. The algorithms require the user to specify a set of negative control variables, as described in the references. The algorithms included in this package are RUV-2, RUV-4, RUV-inv, RUV-rinv, RUV-I, and RUV-III, along with various supporting algorithms.

This package provides a straightforward, well-documented, and broad boosting routine for classification, ideally suited for small to moderate-sized data sets. It performs discrete, real, and gentle boost under both exponential and logistic loss on a given data set.

This package provides an R client for jq, a JSON processor. jq allows the following with JSON data: index into, parse, do calculations, cut up and filter, change key names and values, perform conditionals and comparisons, and more.

This package provides a collection of R-functions implementing adaptive smoothing procedures in 1D, 2D and 3D. This includes the Propagation-Separation approach to adaptive smoothing, the Intersecting Confidence Intervals (ICI), variational approaches, and a non-local means filter.

R-tgb provides Bayesian nonstationary regression and treed Gaussian processes. In addition, it provides visualization functions, tree drawing, sensitivity analysis, multi-resolution models, and sequential experimental design tools, including ALM, ALC, and expected improvement for optimizing noisy black-box functions.

Directory reads and summaries are provided for one or more of the subdirectories of the <https://cran.r-project.org/incoming/> directory, and a compact summary object is returned. The package name is a contraption of CRAN Incoming Watcher'.

CPP is a multiple criteria decision method to evaluate alternatives on complex decision making problems, by a probabilistic approach. The CPP was created and expanded by Sant'Anna, Annibal P. (2015) <doi:10.1007/978-3-319-11277-0>.

Compute inbreeding coefficients using the method of Meuwissen and Luo (1992) <doi:10.1186/1297-9686-24-4-305>, and numerator relationship coefficients between individuals using the method of Van Vleck (2007) <https://pubmed.ncbi.nlm.nih.gov/18050089/>.

Compute standard and generalized Nash Equilibria of non-cooperative games. Optimization methods available are nonsmooth reformulation, fixed-point formulation, minimization problem and constrained-equation reformulation. See e.g. Kanzow and Facchinei (2010), <doi:10.1007/s10479-009-0653-x>.

Insert tables created by the gt R package into Microsoft Word documents. This gives users the ability to add to their existing word documents the tables made in gt using the familiar officer package and syntax from the officeverse'.

Generate plots based on the Item Pool Visualization concept for latent constructs. Item Pool Visualizations are used to display the conceptual structure of a set of items (self-report or psychometric). Dantlgraber, Stieger, & Reips (2019) <doi:10.1177/2059799119884283>.