Showing 25 of total 25 results (show query)
grosssbm
sbm: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>.
Maintained by Julien Chiquet. Last updated 6 months ago.
network-analysissbmstochastic-block-modelcpp
89.6 match 16 stars 8.27 score 98 scripts 2 dependents
igraph
igraph:Network Analysis and Visualization
Routines for simple graphs and network analysis. It can handle large graphs very well and provides functions for generating random and regular graphs, graph visualization, centrality methods and much more.
Maintained by Kirill Müller. Last updated 3 hours ago.
complex-networksgraph-algorithmsgraph-theorymathematicsnetwork-analysisnetwork-graphfortranlibxml2glpkopenblascpp
5.6 match 582 stars 21.11 score 31k scripts 1.9k dependents
ludkinm
SBMSplitMerge:Inference for a Generalised SBM with a Split Merge Sampler
Inference in a Bayesian framework for a generalised stochastic block model. The generalised stochastic block model (SBM) can capture group structure in network data without requiring conjugate priors on the edge-states. Two sampling methods are provided to perform inference on edge parameters and block structure: a split-merge Markov chain Monte Carlo algorithm and a Dirichlet process sampler. Green, Richardson (2001) <doi:10.1111/1467-9469.00242>; Neal (2000) <doi:10.1080/10618600.2000.10474879>; Ludkin (2019) <arXiv:1909.09421>.
Maintained by Matthew Ludkin. Last updated 5 years ago.
17.0 match 2.70 score 3 scripts
comeetie
greed:Clustering and Model Selection with the Integrated Classification Likelihood
An ensemble of algorithms that enable the clustering of networks and data matrices (such as counts, categorical or continuous) with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see <arXiv:2002:11577> for more details).
Maintained by Etienne Côme. Last updated 2 years ago.
5.0 match 14 stars 5.94 score 41 scripts
arecibo
nonparaeff:Nonparametric Methods for Measuring Efficiency and Productivity
Efficiency and productivity indices are measured using this package. This package contains functions for measuring efficiency and productivity of decision making units (DMUs) under the framework of Data Envelopment Analysis (DEA) and its variations.
Maintained by Dong-hyun Oh. Last updated 3 years ago.
4.5 match 4.47 score 18 scripts 11 dependentsgrosssbm
missSBM: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>.
Maintained by Julien Chiquet. Last updated 4 days ago.
missing-datanasnetwork-analysisnetwork-datasetstochastic-block-modelcpp
3.4 match 12 stars 5.53 score 19 scripts
roy-sr-007
GoodFitSBM:Monte Carlo goodness-of-fit tests for Stochastic Blockmodels
Performing goodness-of-fit tests for stochastic blockmodels used to fit network data. Among the three variants of SBMs discussed in <https://doi.org/10.1093/jrsssb/qkad084>, goodness-of-fit test has been performed for the Erdős-Rényi (ER) and Beta versions of SBMs.
Maintained by Soham Ghosh. Last updated 1 years ago.
6.7 match 1 stars 2.70 score
jcdterry
cassandRa:Finds Missing Links and Metric Confidence Intervals in Ecological Bipartite Networks
Provides methods to deal with under sampling in ecological bipartite networks from Terry and Lewis (2020) Ecology <doi:10.1002/ecy.3047> Includes tools to fit a variety of statistical network models and sample coverage estimators to highlight most likely missing links. Also includes simple functions to resample from observed networks to generate confidence intervals for common ecological network metrics.
Maintained by Chris Terry. Last updated 9 months ago.
3.8 match 3 stars 4.48 score 4 scripts
tgno3
DJL:Distance Measure Based Judgment and Learning
Implements various decision support tools related to the Econometrics & Technometrics. Subroutines include correlation reliability test, Mahalanobis distance measure for outlier detection, combinatorial search (all possible subset regression), non-parametric efficiency analysis measures: DDF (directional distance function), DEA (data envelopment analysis), HDF (hyperbolic distance function), SBM (slack-based measure), and SF (shortage function), benchmarking, Malmquist productivity analysis, risk analysis, technology adoption model, new product target setting, network DEA, dynamic DEA, intertemporal budgeting, etc.
Maintained by Dong-Joon Lim. Last updated 2 years ago.
8.5 match 1 stars 1.97 score 93 scripts
kdesantiago
mimiSBM:Mixture of Multilayer Integrator Stochastic Block Models
Our approach uses a mixture of multilayer stochastic block models to group co-membership matrices with similar information into components and to partition observations into different clusters. See De Santiago (2023, ISBN: 978-2-87587-088-9).
Maintained by Kylliann De Santiago. Last updated 1 years ago.
6.0 match 2.70 score
rohelab
fastRG:Sample Generalized Random Dot Product Graphs in Linear Time
Samples generalized random product graphs, a generalization of a broad class of network models. Given matrices X, S, and Y with with non-negative entries, samples a matrix with expectation X S Y^T and independent Poisson or Bernoulli entries using the fastRG algorithm of Rohe et al. (2017) <https://www.jmlr.org/papers/v19/17-128.html>. The algorithm first samples the number of edges and then puts them down one-by-one. As a result it is O(m) where m is the number of edges, a dramatic improvement over element-wise algorithms that which require O(n^2) operations to sample a random graph, where n is the number of nodes.
Maintained by Alex Hayes. Last updated 7 months ago.
adjacency-matrixgraph-samplinglatent-factors
3.3 match 5 stars 4.52 score 22 scripts
aaamini
nett:Network Analysis and Community Detection
Features tools for the network data analysis and community detection. Provides multiple methods for fitting, model selection and goodness-of-fit testing in degree-corrected stochastic blocks models. Most of the computations are fast and scalable for sparse networks, esp. for Poisson versions of the models. Implements the following: Amini, Chen, Bickel and Levina (2013) <doi:10.1214/13-AOS1138> Bickel and Sarkar (2015) <doi:10.1111/rssb.12117> Lei (2016) <doi:10.1214/15-AOS1370> Wang and Bickel (2017) <doi:10.1214/16-AOS1457> Zhang and Amini (2020) <arXiv:2012.15047> Le and Levina (2022) <doi:10.1214/21-EJS1971>.
Maintained by Arash A. Amini. Last updated 2 years ago.
2.0 match 8 stars 5.48 score 19 scripts
marcusrowcliffe
sbd:Size Biased Distributions
Fitting and plotting parametric or non-parametric size-biased non-negative distributions, with optional covariates if parametric. Rowcliffe, M. et al. (2016) <doi:10.1002/rse2.17>.
Maintained by Marcus Rowcliffe. Last updated 9 months ago.
3.3 match 3.30 score
tabea17
noisySBM:Noisy Stochastic Block Mode: Graph Inference by Multiple Testing
Variational Expectation-Maximization algorithm to fit the noisy stochastic block model to an observed dense graph and to perform a node clustering. Moreover, a graph inference procedure to recover the underlying binary graph. This procedure comes with a control of the false discovery rate. The method is described in the article "Powerful graph inference with false discovery rate control" by T. Rebafka, E. Roquain, F. Villers (2020) <arXiv:1907.10176>.
Maintained by Tabea Rebafka. Last updated 4 years ago.
5.1 match 2.00 score 2 scripts
cran
gsbm:Estimate Parameters in the Generalized SBM
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, as describes in Gaucher, Klopp and Robin (2019) <arXiv:1911.13122>.
Maintained by Solenne Gaucher. Last updated 2 years ago.
3.1 match 3.18 score 9 scriptsgrosssbm
GREMLINS:Generalized Multipartite Networks
We define generalized multipartite networks as the joint observation of several networks implying some common pre-specified groups of individuals. The aim is to fit an adapted version of the popular stochastic block model to multipartite networks, as described in Bar-hen, Barbillon and Donnet (2020) <arXiv:1807.10138>.
Maintained by Sophie Donnet. Last updated 2 years ago.
1.8 match 1 stars 5.26 score 9 scripts 4 dependents
jo-theo
shinySbm:'shiny' Application to Use the Stochastic Block Model
A 'shiny' interface for a simpler use of the 'sbm' R package. It also contains useful functions to easily explore the 'sbm' package results. With this package you should be able to use the stochastic block model without any knowledge in R, get automatic reports and nice visuals, as well as learning the basic functions of 'sbm'.
Maintained by Theodore Vanrenterghem. Last updated 1 years ago.
2.5 match 3.70 score 6 scripts
valentinkil
noisysbmGGM:Noisy Stochastic Block Model for GGM Inference
Greedy Bayesian algorithm to fit the noisy stochastic block model to an observed sparse graph. Moreover, a graph inference procedure to recover Gaussian Graphical Model (GGM) from real data. This procedure comes with a control of the false discovery rate. The method is described in the article "Enhancing the Power of Gaussian Graphical Model Inference by Modeling the Graph Structure" by Kilian, Rebafka, and Villers (2024) <arXiv:2402.19021>.
Maintained by Valentin Kilian. Last updated 1 years ago.
1.5 match 2.00 score 4 scriptscran
deaR:Conventional and Fuzzy Data Envelopment Analysis
Set of functions for Data Envelopment Analysis. It runs both classic and fuzzy DEA models. See: Banker, R.; Charnes, A.; Cooper, W.W. (1984). <doi:10.1287/mnsc.30.9.1078>, Charnes, A.; Cooper, W.W.; Rhodes, E. (1978). <doi:10.1016/0377-2217(78)90138-8> and Charnes, A.; Cooper, W.W.; Rhodes, E. (1981). <doi:10.1287/mnsc.27.6.668>.
Maintained by Vicente Bolos. Last updated 2 years ago.
1.8 match 1.41 scorecran
swash:Swash-Backwash Model for the Single Epidemic Wave
The Swash-Backwash Model for the Single Epidemic Wave was developed by Cliff and Haggett (2006) <doi:10.1007/s10109-006-0027-8> to model the velocity of spread of infectious diseases across space. This package enables the calculation of the Swash-Backwash Model for user-supplied panel data on regional infections. The package also provides additional functions for bootstrap confidence intervals, country comparison, visualization of results, and data management.
Maintained by Thomas Wieland. Last updated 20 days ago.
1.9 match 1.30 score
mariaguilleng
boostingDEA:A Boosting Approach to Data Envelopment Analysis
Includes functions to estimate production frontiers and make ideal output predictions in the Data Envelopment Analysis (DEA) context using both standard models from DEA and Free Disposal Hull (FDH) and boosting techniques. In particular, EATBoosting (Guillen et al., 2023 <doi:10.1016/j.eswa.2022.119134>) and MARSBoosting. Moreover, the package includes code for estimating several technical efficiency measures using different models such as the input and output-oriented radial measures, the input and output-oriented Russell measures, the Directional Distance Function (DDF), the Weighted Additive Measure (WAM) and the Slacks-Based Measure (SBM).
Maintained by Maria D. Guillen. Last updated 2 years ago.
0.5 match 2 stars 4.00 score 3 scriptscran
GMPro:Graph Matching with Degree Profiles
Functions for graph matching via nodes' degree profiles are provided in this package. The models we can handle include Erdos-Renyi random graphs and stochastic block models(SBM). More details are in the reference paper: Yaofang Hu, Wanjie Wang and Yi Yu (2020) <arXiv:2006.03284>.
Maintained by Yaofang Hu. Last updated 5 years ago.
0.5 match 1.00 score 1 scripts