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reportgenerator
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public |
ReportGenerator converts coverage reports generated by coverlet, OpenCover, dotCover, Visual Studio, NCover, Cobertura, JaCoCo, Clover, gcov or lcov into human readable reports in various formats.
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2025-09-24 |
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jupyterlite-pyodide-kernel
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public |
Python kernel for JupyterLite powered by Pyodide
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2025-09-24 |
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r-tesseract
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public |
Bindings to 'Tesseract' (https://opensource.google.com/projects/tesseract): a powerful optical character recognition (OCR) engine that supports over 100 languages. The engine is highly configurable in order to tune the detection algorithms and obtain the best possible results.
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2025-09-24 |
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r-oro.nifti
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public |
Functions for the input/output and visualization of medical imaging data that follow
either the 'ANALYZE', 'NIfTI' or 'AFNI' formats. This package is part of the Rigorous
Analytics bundle.
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2025-09-24 |
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r-tractor.base
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public |
Functions for working with magnetic resonance images. Reading and writing of popular file formats (DICOM, Analyze, NIfTI-1, NIfTI-2, MGH); interactive and non-interactive visualisation; flexible image manipulation; metadata and sparse image handling.
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2025-09-24 |
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r-distrex
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public |
Extends package 'distr' by functionals, distances, and conditional distributions.
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2025-09-24 |
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r-noisefiltersr
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public |
An extensive implementation of state-of-the-art and classical algorithms to preprocess label noise in classification problems.
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2025-09-24 |
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r-specr
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public |
Provides utilities for conducting specification curve analyses (Simonsohn, Simmons & Nelson (2020, <doi: 10.1038/s41562-020-0912-z>) or multiverse analyses (Steegen, Tuerlinckx, Gelman & Vanpaemel, 2016, <doi: 10.1177/1745691616658637>) including functions to setup, run, evaluate, and plot all specifications.
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2025-09-24 |
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r-randomforestsrc
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public |
Fast OpenMP parallel computing of Breiman's random forests for survival, competing risks, regression and classification based on Ishwaran and Kogalur's popular random survival forests (RSF) package. Handles missing data and now includes multivariate, unsupervised forests, quantile regression and solutions for class imbalanced data. New fast interface using subsampling and confidence regions for variable importance.
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2025-09-24 |
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r-treemap
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public |
A treemap is a space-filling visualization of hierarchical structures. This package offers great flexibility to draw treemaps.
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2025-09-24 |
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r-forestfloor
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public |
Form visualizations of high dimensional mapping structures of random forests and feature contributions.
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2025-09-24 |
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r-mqtl
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public |
mQTL provides a complete QTL analysis pipeline for metabolomic data. Distinctive features include normalisation using PQN approach, peak alignment using RSPA approach, dimensionality reduction using SRV approach and finally QTL mapping using R/qtl package.
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2025-09-24 |
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r-fake
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public |
This R package can be used to generate artificial data conditionally on pre-specified (simulated or user-defined) relationships between the variables and/or observations. Each observation is drawn from a multivariate Normal distribution where the mean vector and covariance matrix reflect the desired relationships. Outputs can be used to evaluate the performances of variable selection, graphical modelling, or clustering approaches by comparing the true and estimated structures (B Bodinier et al (2021) <arXiv:2106.02521>).
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2025-09-24 |
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r-scholar
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public |
Provides functions to extract citation data from Google Scholar. Convenience functions are also provided for comparing multiple scholars and predicting future h-index values.
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2025-09-24 |
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r-collapsibletree
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public |
Interactive Reingold-Tilford tree diagrams created using 'D3.js', where every node can be expanded and collapsed by clicking on it. Tooltips and color gradients can be mapped to nodes using a numeric column in the source data frame. See 'collapsibleTree' website for more information and examples.
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2025-09-24 |
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r-vtree
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public |
A tool for calculating and drawing "variable trees". Variable trees display information about nested subsets of a data frame.
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2025-09-24 |
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r-tidyseurat
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public |
It creates an invisible layer that allow to see the 'Seurat' object as tibble and interact seamlessly with the tidyverse.
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2025-09-24 |
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r-webchem
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public |
Chemical information from around the web. This package interacts with a suite of web services for chemical information. Sources include: Alan Wood's Compendium of Pesticide Common Names, Chemical Identifier Resolver, ChEBI, Chemical Translation Service, ChemIDplus, ChemSpider, ETOX, Flavornet, NIST Chemistry WebBook, OPSIN, PAN Pesticide Database, PubChem, SRS, Wikidata.
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2025-09-24 |
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r-dot
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public |
Renders DOT diagram markup language in R and also provides the possibility to export the graphs in PostScript and SVG (Scalable Vector Graphics) formats. In addition, it supports literate programming packages such as 'knitr' and 'rmarkdown'.
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2025-09-24 |
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r-intrinsic
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public |
Provides functions to estimate the intrinsic dimension of a dataset via likelihood-based approaches. Specifically, the package implements the 'TWO-NN' and 'Gride' estimators and the 'Hidalgo' Bayesian mixture model. In addition, the first reference contains an extended vignette on the usage of the 'TWO-NN' and 'Hidalgo' models. References: Denti (2022+, <arXiv:2102.11425>); Allegra et al. (2020, <doi:10.1038/s41598-020-72222-0>); Denti et al. (2022, <doi:10.1038/s41598-022-20991-1>); Facco et al. (2017, <doi:10.1038/s41598-017-11873-y>); Santos-Fernandez et al. (2021, <doi:10.1038/s41598-022-20991-1>).
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2025-09-24 |
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r-networkd3
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public |
Creates 'D3' 'JavaScript' network, tree, dendrogram, and Sankey graphs from 'R'.
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2025-09-24 |
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great-expectations
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public |
Always know what to expect from your data.
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2025-09-24 |
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r-nimble
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public |
A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom- generated C++. 'NIMBLE' includes default methods for MCMC, particle filtering, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at <https://r-nimble.org>.
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2025-09-24 |
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databricks-cli
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public |
A command line interface for Databricks
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2025-09-24 |
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apsg
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public |
APSG - python package for structural geologists
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2025-09-24 |
