PLNmodels - Poisson Lognormal Models
The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 2021 <doi:10.3389/fevo.2021.588292>) can be used for a variety of multivariate problems when count data are at play, including principal component analysis for count data, discriminant analysis, model-based clustering and network inference. Implements variational algorithms to fit such models accompanied with a set of functions for visualization and diagnostic.
Last updated 2 months ago
count-datamultivariate-analysisnetwork-inferencepcapoisson-lognormal-model
9.68 score 54 stars 224 scripts 379 downloadssbm - Stochastic Blockmodels
A collection of tools and functions to adjust a variety of stochastic blockmodels (SBM). Supports at the moment Simple, Bipartite, 'Multipartite' and Multiplex SBM (undirected or directed with Bernoulli, Poisson or Gaussian emission laws on the edges, and possibly covariate for Simple and Bipartite SBM). See Léger (2016) <doi:10.48550/arXiv.1602.07587>, 'Barbillon et al.' (2020) <doi:10.1111/rssa.12193> and 'Bar-Hen et al.' (2020) <doi:10.48550/arXiv.1807.10138>.
Last updated 2 months ago
network-analysissbmstochastic-block-model
8.67 score 16 stars 2 packages 97 scripts 457 downloadsaricode - Efficient Computations of Standard Clustering Comparison Measures
Implements an efficient O(n) algorithm based on bucket-sorting for fast computation of standard clustering comparison measures. Available measures include adjusted Rand index (ARI), normalized information distance (NID), normalized mutual information (NMI), adjusted mutual information (AMI), normalized variation information (NVI) and entropy, as described in Vinh et al (2009) <doi:10.1145/1553374.1553511>. Include AMI (Adjusted Mutual Information) since version 0.1.2, a modified version of ARI (MARI), as described in Sundqvist et al. <doi:10.1007/s00180-022-01230-7> and simple Chi-square distance since version 1.0.0.
Last updated 9 months ago
bucket-sortclusteringclustering-comparison-measures
8.06 score 25 stars 14 packages 460 scripts 1.6k downloadsmissSBM - Handling Missing Data in Stochastic Block Models
When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0 due to missing information between node pairs), it is possible to account for the underlying process that generates those NAs. 'missSBM', presented in 'Barbillon, Chiquet and Tabouy' (2022) <doi:10.18637/jss.v101.i12>, adjusts the popular stochastic block model from network data sampled under various missing data conditions, as described in 'Tabouy, Barbillon and Chiquet' (2019) <doi:10.1080/01621459.2018.1562934>.
Last updated 1 years ago
missing-datanasnetwork-analysisnetwork-datasetstochastic-block-model
5.53 score 12 stars 19 scripts 267 downloadsquadrupen - Sparsity by Worst-Case Quadratic Penalties
Fits classical sparse regression models with efficient active set algorithms by solving quadratic problems as described by Grandvalet, Chiquet and Ambroise (2017) <doi:10.48550/arXiv.1210.2077>. Also provides a few methods for model selection purpose (cross-validation, stability selection).
Last updated 5 months ago
3.48 score 30 scripts 679 downloadsspinyReg - Sparse Generative Model and Its EM Algorithm
Implements a generative model that uses a spike-and-slab like prior distribution obtained by multiplying a deterministic binary vector. Such a model allows an EM algorithm, optimizing a type-II log-likelihood.
Last updated 9 years ago
1.00 score 1 scripts 86 downloads