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r-dlookr
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public |
A collection of tools that support data diagnosis, exploration, and transformation. Data diagnostics provides information and visualization of missing values and outliers and unique and negative values to help you understand the distribution and quality of your data. Data exploration provides information and visualization of the descriptive statistics of univariate variables, normality tests and outliers, correlation of two variables, and relationship between target variable and predictor. Data transformation supports binning for categorizing continuous variables, imputates missing values and outliers, resolving skewness. And it creates automated reports that support these three tasks.
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2025-09-24 |
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streamlit
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public |
A faster way to build and share data apps
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2025-09-24 |
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r-fassets
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public |
A collection of functions to manage, to investigate and to analyze data sets of financial assets from different points of view.
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2025-09-24 |
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r-matrixtests
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public |
Functions to perform fast statistical hypothesis tests on rows/columns of matrices. The main goals are: 1) speed via vectorization, 2) output that is detailed and easy to use, 3) compatibility with tests implemented in R (like those available in the 'stats' package).
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2025-09-24 |
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pyinstaller-hooks-contrib
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public |
Community maintained hooks for PyInstaller
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2025-09-24 |
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r-cytometree
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public |
Given the hypothesis of a bi-modal distribution of cells for each marker, the algorithm constructs a binary tree, the nodes of which are subpopulations of cells. At each node, observed cells and markers are modeled by both a family of normal distributions and a family of bi-modal normal mixture distributions. Splitting is done according to a normalized difference of AIC between the two families. Method is detailed in: Commenges, Alkhassim, Gottardo, Hejblum & Thiebaut (2018) <doi: 10.1002/cyto.a.23601>.
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2025-09-24 |
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r-leidenbase
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public |
An R to C/C++ interface that runs the Leiden community detection algorithm to find a basic partition (). It runs the equivalent of the 'leidenalg' find_partition() function, which is given in the 'leidenalg' distribution file 'leiden/src/functions.py'. This package includes the required source code files from the official 'leidenalg' distribution and functions from the R 'igraph' package. The 'leidenalg' distribution is available from <https://github.com/vtraag/leidenalg/> and the R 'igraph' package is available from <https://igraph.org/r/>. The Leiden algorithm is described in the article by Traag et al. (2019) <doi:10.1038/s41598-019-41695-z>. Leidenbase includes code from the packages: igraph version 0.9.8 with license GPL (>= 2), leidenalg version 0.8.10 with license GPL 3.
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2025-09-24 |
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pybids
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public |
bids: interface with datasets conforming to BIDS
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2025-09-24 |
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sasview
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public |
SasView is a Small Angle Scattering (SAS) analysis package
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2025-09-24 |
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dlt
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public |
dlt is an open-source python-first scalable data loading library that does not require any backend to run.
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2025-09-24 |
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jupyterlite-core
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public |
Tools for building JupyterLite sites
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2025-09-24 |
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jupyterlite-core-with-translation
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public |
Tools for building JupyterLite sites (with translation)
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2025-09-24 |
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jupyterlite-core-with-libarchive
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public |
Tools for building JupyterLite sites (with libarchive)
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2025-09-24 |
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jupyterlite-core-with-check
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public |
Tools for building JupyterLite sites (with check)
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2025-09-24 |
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jupyterlite-core-with-contents
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public |
Tools for building JupyterLite sites (with contents)
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2025-09-24 |
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jupyterlite-core-with-serve
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public |
Tools for building JupyterLite sites (with serve)
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2025-09-24 |
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jupyterlite-core-with-all
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public |
Tools for building JupyterLite sites (with all)
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2025-09-24 |
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jupyterlite-core-with-lab
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public |
Tools for building JupyterLite sites (with jupyterlab)
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2025-09-24 |
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r-diffusr
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public |
Implementation of network diffusion algorithms such as heat diffusion or Markov random walks. Network diffusion algorithms generally spread information in the form of node weights along the edges of a graph to other nodes. These weights can for example be interpreted as temperature, an initial amount of water, the activation of neurons in the brain, or the location of a random surfer in the internet. The information (node weights) is iteratively propagated to other nodes until a equilibrium state or stop criterion occurs.
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2025-09-24 |
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r-clustassess
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public |
A set of tools for evaluating clustering robustness using proportion of ambiguously clustered pairs (Senbabaoglu et al. (2014) <doi:10.1038/srep06207>), as well as similarity across methods and method stability using element-centric clustering comparison (Gates et al. (2019) <doi:10.1038/s41598-019-44892-y>). Additionally, this package enables stability-based parameter assessment for graph-based clustering pipelines typical in single-cell data analysis.
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2025-09-24 |
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r-fourpno
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public |
Estimate Barton & Lord's (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x> four parameter IRT model with lower and upper asymptotes using Bayesian formulation described by Culpepper (2016) <doi:10.1007/s11336-015-9477-6>.
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2025-09-24 |
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adios-db
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public |
Package for working with data in the NOAA ADIOS Oil Database
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2025-09-24 |
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adios_db
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public |
Package for working with data in the NOAA ADIOS Oil Database
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2025-09-24 |
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r-treeman
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public |
S4 class and methods for intuitive and efficient phylogenetic tree manipulation.
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2025-09-24 |
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r-netmeta
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public |
A comprehensive set of functions providing frequentist methods for network meta-analysis and supporting Schwarzer et al. (2015) <DOI:10.1007/978-3-319-21416-0>, Chapter 8 "Network Meta-Analysis": - frequentist network meta-analysis following Rücker (2012) <DOI:10.1002/jrsm.1058>; - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) <DOI:10.1186/1471-2288-13-35>; - measures characterizing the flow of evidence between two treatments by König et al. (2013) <DOI:10.1002/sim.6001>; - ranking of treatments (frequentist analogue of SUCRA) according to Rücker & Schwarzer (2015) <DOI:10.1186/s12874-015-0060-8>; - partial order of treatment rankings ('poset') and Hasse diagram for 'poset' (Carlsen & Bruggemann, 2014) <DOI:10.1002/cem.2569>; (Rücker & Schwarzer, 2017) <DOI:10.1002/jrsm.1270>; - split direct and indirect evidence to check consistency (Dias et al., 2010) <DOI:10.1002/sim.3767>, (Efthimiou et al., 2019) <DOI:10.1002/sim.8158>; - league table with network meta-analysis results; - additive network meta-analysis for combinations of treatments (Rücker et al., 2020) <DOI:10.1002/bimj.201800167>; - network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) <DOI:10.1002/sim.8158>; - 'comparison-adjusted' funnel plot (Chaimani & Salanti, 2012) <DOI:10.1002/jrsm.57>; - automated drawing of network graphs described in Rücker & Schwarzer (2016) <DOI:10.1002/jrsm.1143>; - rankograms and ranking by SUCRA; - contribution matrix as described in Papakonstantinou et al. (2018) <DOI:10.12688/f1000research.14770.3> and Davies et al. (2021) <arXiv:2107.02886>.
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2025-09-24 |
