Classification of Spatial Patterns from Environmental Data
Through GRadient RECognition
c(person("Wencheng", "Lau-Medrano", role = c("aut", "cre"), email = "luis.laum@gmail.com"))
Provides algorithms for detection of spatial patterns from oceanographic data using image processing methods based on Gradient Recognition.
2017-07-09 17:52:26 UTC; NvTomus InDemonic

2017-07-29 04:30:43 UTC; NvTomus InDemonic

GRadient-Based RECognition of Spatial Patterns in Environmental
Data
2017-08-13 21:27:45 UTC; NvTomus InDemonic

Gradient-Based Recognition of Spatial Patterns in Environmental
Data

Clustering and Model Selection with the Integrated
Classification Likelihood
c(person(given = "Etienne",
family = "Côme",
role = c("aut", "cre"),
email = "etienne.come@univ-eiffel.fr"),
person(given = "Nicolas",
family = "Jouvin",
role = c("aut")))
An ensemble of algorithms that enable the clustering of networks and data matrix such as document/term matrix 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).
Rcpp (>= 1.0.0), Matrix, future, listenv, ggplot2, graphics,
methods, stats,RSpectra,ggpubr, MASS,cba
testthat, knitr, rmarkdown, igraph, dplyr, tibble, tidyr,
spelling
'RcppExports.R' 'misc.R' 'models_classes.R' 'fit_classes.R'
'cleanpath.R' 'genetic_alg.R' 'hybrid_alg.R' 'alg_classes.R'
'dcsbm.R' 'coclust_dcsbm.R' 'data.R' 'generator.R' 'mvmreg.R'
'gmm.R' 'greed.R' 'sbm.R' 'misssbm.R' 'mm.R' 'multistart_alg.R'
'multsbm.R' 'plot.R'
Etienne Côme [aut, cre],
Nicolas Jouvin [aut]

An ensemble of algorithms that enable the clustering of networks and data matrix such as counts matrix 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).
Rcpp (>= 1.0.0), Matrix, future, listenv, ggplot2, graphics,
methods, stats,RSpectra,ggpubr,GGally,cba
testthat, MASS, mclust, knitr, rmarkdown, igraph, dplyr,
tibble, tidyr, spelling
'RcppExports.R' 'misc.R' 'models_classes.R' 'fit_classes.R'
'cleanpath.R' 'genetic_alg.R' 'hybrid_alg.R' 'alg_classes.R'
'dcsbm.R' 'coclust_dcsbm.R' 'data.R' 'diaggmm.R' 'generator.R'
'mvmreg.R' 'gmm.R' 'greed.R' 'sbm.R' 'misssbm.R' 'mm.R'
'multistart_alg.R' 'multsbm.R' 'plot.R'