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r-alakazam
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public |
Provides methods for high-throughput adaptive immune receptor repertoire sequencing (AIRR-Seq; Rep-Seq) analysis. In particular, immunoglobulin (Ig) sequence lineage reconstruction, lineage topology analysis, diversity profiling, amino acid property analysis and gene usage. Citations: Gupta and Vander Heiden, et al (2017) <doi:10.1093/bioinformatics/btv359>, Stern, Yaari and Vander Heiden, et al (2014) <doi:10.1126/scitranslmed.3008879>.
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2025-09-24 |
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r-ecodist
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public |
Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community data.
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2025-09-24 |
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r-tidygraph
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public |
A graph, while not "tidy" in itself, can be thought of as two tidy data frames describing node and edge data respectively. 'tidygraph' provides an approach to manipulate these two virtual data frames using the API defined in the 'dplyr' package, as well as provides tidy interfaces to a lot of common graph algorithms.
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2025-09-24 |
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r-qgraph
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public |
Weighted network visualization and analysis, as well as Gaussian graphical model computation. See Epskamp et al. (2012) <doi:10.18637/jss.v048.i04>.
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2025-09-24 |
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r-intergraph
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public |
Functions implemented in this package allow to coerce (i.e. convert) network data between classes provided by other R packages. Currently supported classes are those defined in packages: network and igraph.
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2025-09-24 |
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r-leiden
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public |
Implements the 'Python leidenalg' module to be called in R. Enables clustering using the leiden algorithm for partition a graph into communities. See the 'Python' repository for more details: <https://github.com/vtraag/leidenalg> Traag et al (2018) From Louvain to Leiden: guaranteeing well-connected communities. <arXiv:1810.08473>.
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2025-09-24 |
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r-mixomics
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public |
Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares.
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2025-09-24 |
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r-influencer
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public |
Provides functionality to compute various node centrality measures on networks. Included are functions to compute betweenness centrality (by utilizing Madduri and Bader's SNAP library), implementations of Burt's constraint and effective network size (ENS) metrics, Borgatti's algorithm to identify key players, and Valente's bridging metric. On Unix systems, the betweenness, Key Players, and bridging implementations are parallelized with OpenMP, which may run faster on systems which have OpenMP configured.
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2025-09-24 |
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r-targets
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public |
As a pipeline toolkit for Statistics and data science in R, the 'targets' package brings together function-oriented programming and 'Make'-like declarative workflows. It analyzes the dependency relationships among the tasks of a workflow, skips steps that are already up to date, runs the necessary computation with optional parallel workers, abstracts files as R objects, and provides tangible evidence that the results match the underlying code and data. The methodology in this package borrows from GNU 'Make' (2015, ISBN:978-9881443519) and 'drake' (2018, <doi:10.21105/joss.00550>).
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2025-09-24 |
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r-gwasexacthw
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public |
This package contains a function to do exact Hardy-Weinburg testing (using Fisher's test) for SNP genotypes as typically obtained in a Genome Wide Association Study (GWAS).
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2025-09-24 |
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r-threejs
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public |
Create interactive 3D scatter plots, network plots, and globes using the 'three.js' visualization library (<https://threejs.org>).
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2025-09-24 |
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tree-sitter-sql
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public |
SQL grammar for tree-sitter
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2025-09-24 |
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cffi
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public |
Foreign Function Interface for Python calling C code.
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2025-09-24 |
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r-rgexf
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public |
Create, read and write 'GEXF' (Graph Exchange 'XML' Format) graph files (used in 'Gephi' and others). Using the 'XML' package, it allows the user to easily build/read graph files including attributes, 'GEXF' visual attributes (such as color, size, and position), network dynamics (for both edges and nodes) and edge weighting. Users can build/handle graphs element-by-element or massively through data-frames, visualize the graph on a web browser through 'gexf-js' (a 'javascript' library) and interact with the 'igraph' package.
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2025-09-24 |
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r-ruv
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public |
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. These algorithms were proposed in Gagnon-Bartsch and Speed (2012) <doi:10.1093/nar/gkz433>, Gagnon-Bartsch, Jacob and Speed (2013), and Molania, et. al. (2019) <doi:10.1093/nar/gkz433>. 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.
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2025-09-24 |
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libgz-sim-yarp-plugins
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public |
YARP plugins for Modern Gazebo (gz-sim).
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2025-09-24 |
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reboost
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public |
LEGEND Monte-Carlo simulation post-processing
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2025-09-24 |
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gz-sim-yarp-plugins
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public |
YARP plugins for Modern Gazebo (gz-sim).
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2025-09-24 |
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esmvalcore
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public |
ESMValCore: A community tool for pre-processing data from Earth system models in CMIP and running analysis scripts.
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2025-09-24 |
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django-viewflow
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public |
Reusable workflow library for Django
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2025-09-24 |
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ojph
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public |
Python bindings for OpenJPH, a JPEG 2000 codec
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2025-09-24 |
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znflow
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public |
A general purpose framework for building and running computational graphs.
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2025-09-24 |
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r-bart
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public |
Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. For more information see Sparapani, Spanbauer and McCulloch <doi:10.18637/jss.v097.i01>.
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2025-09-24 |
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r-kza
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public |
Time Series Analysis including break detection, spectral analysis, KZ Fourier Transforms.
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2025-09-24 |
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netbird
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public |
Connect your devices into a secure WireGuard®-based overlay network with SSO, MFA and granular access controls.
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2025-09-24 |
