Here is a list of some of the software we have recently developed, and links to it:
binMixtC
Description: Maximum composite likelihood estimation of bin marginal Gaussian data.
Website: https://github.com/Fili75/binMixtC
DPABST
Description: This package allows one to post-process any scikit-learn classification algorithm to incorporate a demographic parity fairness constraint and user-specified per-group abstention rates.
Website: https://github.com/evgchz/dpabst
gsbm
Description: Given an adjacency matrix drawn from a Generalized Stochastic Block Model with missing observations, this package robustly estimates the probabilities of connection between nodes and detects outliers nodes
Website: https://cran.r-project.org/web/packages/gsbm/index.html
HiDimStat
Description: The HiDimStat package provides statistical inference methods to solve the problem of support recovery in the context of high-dimensional and spatially structured data.
Website: https://github.com/ja-che/hidimstat
pysarpu
Description: In the framework of positive and unlabelled (PU) learning under the SAR assumption with unknown propensity, this software implements the general SAR-EM algorithm.
Website: https://github.com/ocoudray/pysarpu
