R implementation of SIDES-based subgroup search algorithms (Lipkovich et al. (2017) <doi:10.1002/sim.7064>).

The GenDataSample() and GenDataPopulation() functions create, respectively, a sample or population of multivariate nonnormal data using methods described in Ruscio and Kaczetow (2008). Both of these functions call a FactorAnalysis() function to reproduce a correlation matrix. The EFACompData() function allows users to determine how many factors to retain in an exploratory factor analysis of an empirical data set using a method described in Ruscio and Roche (2012). The latter function uses populations of comparison data created by calling the GenDataPopulation() function. <DOI: 10.1080/00273170802285693>. <DOI: 10.1037/a0025697>.

Convert REDCap exports into tidy tables for easy handling of REDCap repeat instruments and event arms.

Foundational package in the R4SUB (R for Regulatory Submission) ecosystem. Defines the core evidence table schema, parsers, indicator abstractions, and scoring primitives needed to quantify clinical submission readiness. Provides a standardized contract for ingesting heterogeneous sources (validation outputs, metadata, traceability) into a single evidence framework.

Visualization platform for T cell receptor repertoire analysis output results. It includes comparison of sequence frequency among samples, network of similar sequences and convergent recombination source between species. Currently repertoire analysis is in early stage of development and requires new approaches for repertoire data examination and assessment as we intend to develop. No publication is available yet (will be available in the near future), Efroni (2021) <https:>.

This provides a robust estimator for stochastic frontier models, employing the Minimum Density Power Divergence Estimator (MDPDE) for enhanced robustness against outliers. Additionally, it includes a function to recommend the optimal tuning parameter, alpha, which controls the robustness of the MDPDE. The methods implemented in this package are based on Song et al. (2017) <doi:10.1016/j.csda.2016.08.005>.

Fast computing an ensemble of rank-based trees via boosting or random forest on binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. Decision rules can be extracted from trees.

Data exploration and prediction with focus on high dimensional data and chemometrics. The package was initially designed about partial least squares regression and discrimination models and variants, in particular locally weighted PLS models (LWPLS). Then, it has been expanded to many other methods for analyzing high dimensional data. The name rchemo comes from the fact that the package is orientated to chemometrics, but most of the provided methods are fully generic to other domains. Functions such as transform(), predict(), coef() and summary() are available. Tuning the predictive models is facilitated by generic functions gridscore() (validation dataset) and gridcv() (cross-validation). Faster versions are also available for models based on latent variables (LVs) (gridscorelv() and gridcvlv()) and ridge regularization (gridscorelb() and gridcvlb()).

Download and open manifest files provided by the Copernicus Global Land Service data <https://land.copernicus.eu/global/>. The manifest files are available at: <https://land.copernicus.vgt.vito.be/manifest/>. Also see: <https://land.copernicus.eu/global/access/>. Before you can download the data, you will first need to register to create a username and password.

Enhances the R Optimization Infrastructure ('ROI') package by registering the free GLPK solver. It allows for solving mixed integer linear programming ('MILP') problems as well as all variants/combinations of LP', IP'.

Geostatistical analysis of continuous and count data. Implements stationary Gaussian processes with Matérn correlation for spatial prediction, as described in Diggle and Giorgi (2019, ISBN: 978-1-138-06102-7).

Terrestrial laser scanning (TLS) data processing and post-hurricane damage severity classification at the individual tree level using deep Learning. Further details were published in Klauberg et al. (2023) <doi:10.3390/rs15041165>.

This package provides a collection of functions for numerical construction of optimal discriminating designs. At the current moment T-optimal designs (which maximize the lower bound for the power of F-test for regression model discrimination), KL-optimal designs (for lognormal errors) and their robust analogues can be calculated with the package.

Allows you to interact with the API of the "Todoist" platform. Todoist <https://www.todoist.com/> provides an online task manager service for teams.

High-performance C++ implementation (via Rcpp') of the robust location and scale M-estimators described in Rousseeuw & Verboven (2002) <doi:10.1016/S0167-9473(02)00078-6> for very small samples. Provides numerically identical results to the revss package with significantly improved performance through sorting networks and compiled iteration loops.

Access to Boost Date_Time functionality for dates, durations (both for days and date time objects), time zones, and posix time ('ptime') is provided by using Rcpp modules'. The posix time implementation can support high-resolution of up to nano-second precision by using 96 bits (instead of 64 with R) to present a ptime object (but this needs recompilation with a #define set).

Using the novel Relative Distance to cluster datasets. Implementation of a clustering approach based on the k-means algorithm that can be used with any distance. In addition, implementation of the Hartigan and Wong method to accommodate alternative distance metrics. Both methods can operate with any distance measure, provided a suitable method is available to compute cluster centers under the chosen metric. Additionally, the k-medoids algorithm is implemented, offering a robust alternative for clustering without the need of computing cluster centers under the chosen metric. All three methods are designed to support Relative distances, Euclidean distances, and any user-defined distance functions. The Hartigan and Wong method is described in Hartigan and Wong (1979) <doi:10.2307/2346830> and an explanation of the k-medoids algorithm can be found in Reynolds et al (2006) <doi:10.1007/s10852-005-9022-1>.

The ability to plot raster graphics in PDF files can be useful when one needs multi-page documents, but the plots contain so many individual elements that (the usual) use of vector graphics results in inconveniently large file sizes. Internally, the package plots each individual page as a PNG, and then combines them in one PDF file.

Fit (exponential or diffusion) response-time extended multinomial processing tree (RT-MPT) models by Klauer and Kellen (2018) <doi:10.1016/j.jmp.2017.12.003> and Klauer, Hartmann, and Meyer-Grant (submitted). The RT-MPT class not only incorporate frequencies like traditional multinomial processing tree (MPT) models, but also latencies. This enables it to estimate process completion times and encoding plus motor execution times next to the process probabilities of traditional MPTs. rtmpt is a hierarchical Bayesian framework and posterior samples are sampled using a Metropolis-within-Gibbs sampler (for exponential RT-MPTs) or Hamiltonian-within-Gibbs sampler (for diffusion RT-MPTs).

This package provides tools for creating data validation pipelines and tidy reports. This package offers a framework for exploring and validating data frame like objects using dplyr grammar of data manipulation.

This package provides an interface to the Vamp audio analysis plugin system <https://www.vamp-plugins.org/> developed by Queen Mary University of London's Centre for Digital Music. Enables loading and running Vamp plugins for various audio analysis tasks including tempo detection, onset detection, spectral analysis, and audio feature extraction. Supports mono and stereo audio with automatic channel adaptation and domain conversion.

Generate SpatRaster objects, as defined by the terra package, from digital images, using a specified spatial object as a geographical reference.

Wraps the Ollama <https://ollama.com> API, which can be used to communicate with generative large language models locally.

The Echo nest <http://the.echonest.com> is the industry's leading music intelligence company, providing developer with deepest understanding of music content and music fans. This package can be used to access artist's data including songs, blogs, news, reviews etc. Song's data including audio summary, style, danceability, tempo etc can also be accessed.