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r-icellr
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A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.05.05.078550> and Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.03.31.019109> for more details.
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2026-03-14 |
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r-goat
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Perform gene set enrichment analyses using the Gene set Ordinal Association Test (GOAT) algorithm and visualize your results. Koopmans, F. (2024) <doi:10.1038/s42003-024-06454-5>.
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2026-03-14 |
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r-abdiv
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public |
A collection of measures for measuring ecological diversity. Ecological diversity
comes in two flavors: alpha diversity measures the diversity within a single site
or sample, and beta diversity measures the diversity across two sites or samples.
This package overlaps considerably with other R packages such as ''vegan'', ''gUniFrac'',
''betapart'', and ''fossil''. We also include a wide range of functions that are
implemented in software outside the R ecosystem, such as ''scipy'', ''Mothur'',
and ''scikit-bio''. The implementations here are designed to be basic and clear
to the reader
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2026-03-14 |
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r-clevr
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public |
Tools for evaluating link prediction and clustering algorithms with respect to ground truth. Includes efficient implementations of common performance measures such as pairwise precision/recall, cluster homogeneity/completeness, variation of information, Rand index etc.
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2026-03-14 |
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swmmtoolbox
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public |
Command line script and Python library to read Storm Water Management Model binary output.
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2026-03-14 |
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opensees
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public |
Simulate the performance of structural and geotechnical systems subjected to earthquakes
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2026-03-14 |
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r-recommenderlab
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Provides a research infrastructure to develop and evaluate collaborative filtering recommender algorithms. This includes a sparse representation for user-item matrices, many popular algorithms, top-N recommendations, and cross-validation. Hahsler (2022) <doi:10.48550/arXiv.2205.12371>.
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2026-03-14 |
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r-weightsvm
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Functions for subject/instance weighted support vector machines (SVM). It uses a modified version of 'libsvm' and is compatible with package 'e1071'. It also allows user defined kernel matrix.
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2026-03-14 |
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r-bst
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Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems. See Wang (2011) <doi:10.2202/1557-4679.1304>, Wang (2012) <doi:10.3414/ME11-02-0020>, Wang (2018) <doi:10.1080/10618600.2018.1424635>, Wang (2018) <doi:10.1214/18-EJS1404>.
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2026-03-14 |
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logmuse
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public |
Logging setup tool
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2026-03-14 |
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r-monopoly
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public |
Functions for fitting monotone polynomials to data. Detailed discussion of the methodologies used can be found in Murray, Mueller and Turlach (2013) <doi:10.1007/s00180-012-0390-5> and Murray, Mueller and Turlach (2016) <doi:10.1080/00949655.2016.1139582>.
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2026-03-14 |
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r-fclust
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Algorithms for fuzzy clustering, cluster validity indices and plots for cluster validity and visualizing fuzzy clustering results.
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2026-03-14 |
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r-segclust2d
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public |
Provides two methods for segmentation and joint segmentation/clustering of bivariate time-series. Originally intended for ecological segmentation (home-range and behavioural modes) but easily applied on other series, the package also provides tools for analysing outputs from R packages 'moveHMM' and 'marcher'. The segmentation method is a bivariate extension of Lavielle's method available in 'adehabitatLT' (Lavielle, 1999 <doi:10.1016/S0304-4149(99)00023-X> and 2005 <doi:10.1016/j.sigpro.2005.01.012>). This method rely on dynamic programming for efficient segmentation. The segmentation/clustering method alternates steps of dynamic programming with an Expectation-Maximization algorithm. This is an extension of Picard et al (2007) <doi:10.1111/j.1541-0420.2006.00729.x> method (formerly available in 'cghseg' package) to the bivariate case. The method is fully described in Patin et al (2018) <doi:10.1101/444794>.
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2026-03-14 |
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r-codingmatrices
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A collection of coding functions as alternatives to the standard functions in the stats package, which have names starting with 'contr.'. Their main advantage is that they provide a consistent method for defining marginal effects in factorial models. In a simple one-way ANOVA model the intercept term is always the simple average of the class means.
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2026-03-14 |
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r-inflection
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Implementation of methods Extremum Surface Estimator (ESE) and Extremum Distance Estimator (EDE) to identify the inflection point of a curve . Christopoulos, DT (2014) <doi:10.48550/arXiv.1206.5478> . Christopoulos, DT (2016) <https://demovtu.veltech.edu.in/wp-content/uploads/2016/04/Paper-04-2016.pdf> . Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076> .
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2026-03-14 |
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r-knnmi
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This is a 'C++' mutual information (MI) library based on the k-nearest neighbor (KNN) algorithm. There are three functions provided for computing MI for continuous values, mixed continuous and discrete values, and conditional MI for continuous values. They are based on algorithms by A. Kraskov, et. al. (2004) <doi:10.1103/PhysRevE.69.066138>, BC Ross (2014)<doi:10.1371/journal.pone.0087357>, and A. Tsimpiris (2012) <doi:10.1016/j.eswa.2012.05.014>, respectively.
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2026-03-14 |
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clang-tools
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Development headers and libraries for Clang
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2026-03-14 |
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r-mtlr
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An implementation of Multi-Task Logistic Regression (MTLR) for R. This package is based on the method proposed by Yu et al. (2011) which utilized MTLR for generating individual survival curves by learning feature weights which vary across time. This model was further extended to account for left and interval censored data.
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2026-03-14 |
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libclang
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public |
Development headers and libraries for Clang
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2026-03-14 |
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clang-18
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public |
Development headers and libraries for Clang
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2026-03-14 |
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clang
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public |
Development headers and libraries for Clang
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2026-03-14 |
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libclang13
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public |
Development headers and libraries for Clang
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2026-03-14 |
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libclang-cpp
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public |
Development headers and libraries for Clang
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2026-03-14 |
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clang-format
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public |
Development headers and libraries for Clang
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2026-03-14 |
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clangxx
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public |
Development headers and libraries for Clang
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2026-03-14 |
