aricode - 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), 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.
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bucket-sortclusteringclustering-comparison-measurescpp
9.66 score 28 stars 21 dependents 748 scripts 2.9k downloads
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.
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count-datamultivariate-analysisnetwork-inferencepcapoisson-lognormal-modelopenblascpp
9.63 score 59 stars 258 scripts 383 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>.
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network-analysissbmstochastic-block-modelcpp
8.03 score 17 stars 2 dependents 105 scripts 434 downloadsquadrupen - Sparse and Group Sparse Linear Models
Fits the solution paths of classical sparse regression models with efficient active set algorithms by solving small sub-problems. Include LASSO, SCAD, MCP, (Sparse) Group-LASSO, Cooperative-LASSO, (Group) LAVA, (Generalized) Fused-Lasso and (Generalized) Elastic-Net. Also provides methods for model selection purpose (information criteria, cross-validation, stability selection).
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openblascppopenmp
6.46 score 1 stars 76 scripts 230 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>.
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missing-datanasnetwork-analysisnetwork-datasetstochastic-block-modelcpp
5.55 score 13 stars 18 scripts 270 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.
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1.00 score 1 scripts 132 downloads