Smooth Backfitting for additive models using
Nadaraya-Watson estimator

Computing the Singular BIC for Multiple Models
c(person("Luca", "Weihs", role=c("aut","cre"), email="lucaw@uw.edu"),
person("Martyn", "Plummer", role="ctb"))
Computes the sBIC for various singular model collections including:
binomial mixtures, factor analysis models, Gaussian mixtures,
latent forests, latent class analyses, and reduced rank regressions.
poLCA, R.oo (>= 1.20.0), R.methodsS3, mclust, igraph (>=
1.0.1), Rcpp (>= 0.12.3), combinat, flexmix, hash
'ModelPoset.R' 'MixtureModels.R' 'BinomialMixtures.R'
'FactorAnalyses.R' 'GaussianMixtures.R' 'LCAs.R'
'LatentForests-Helpers.R' 'LatentForests.R' 'RcppExports.R'
'ReducedRankRegressions.R' 'sBIC-Function.R' 'sbic.R'
Luca Weihs [aut, cre],
Martyn Plummer [ctb]

R Toolbox for Unsupervised Spectral Clustering
c(person(given="Emilie", family="Poisson-Caillault", role = c("aut","cre","cph"),
email = "emilie.caillault@univ-littoral.fr" ),
person(given="Alain",family="Lefebvre",role=c("ctb"),
email = "Alain.Lefebvre@ifremer.fr"),
person(given="Erwan", family="Vincent", role = c("aut")),
person(given="Pierre-Alexandre", family="Hebert", role = c("ctb")))
Emilie Poisson-Caillault [aut, cre, cph],
Alain Lefebvre [ctb],
Erwan Vincent [aut],
Pierre-Alexandre Hebert [ctb]
Toolbox containing a variety of spectral clustering tools functions. Among the tools available are the hierarchical spectral clustering algorithm, the Shi and Malik clustering algorithm, the Perona and Freeman algorithm, the non-normalized clustering, the Von Luxburg algorithm, the Partition Around Medoids clustering algorithm, a multi-level clustering algorithm, recursive clustering and the fast method for all clustering algorithm. As well as other tools needed to run these algorithms or useful for unsupervised spectral clustering. This toolbox aims to gather the main tools for unsupervised spectral classification. See <http://mawenzi.univ-littoral.fr/> for more information and documentation.

Maximum Likelihood Estimation and Likelihood Ratio Test
Functions for Separable Variance-Covariance Structures
c(
person("Ameur", "Manceur", email = "ameur.manceur@mail.mcgill.ca", role = "aut"),
person("Timothy", "Schwinghamer", email = "schwinghamer@hotmail.com", role = "cre"),
person("Pierre", "Dutilleul", email = "pierre.dutilleul@mcgill.ca", role = "aut")
)
It combines maximum likelihood estimation of the parameters
of matrix and 3rd-order tensor normal distributions with unstructured
factor variance covariance matrices, two procedures, and unbiased
modified likelihood ratio testing of simple and double separability
for variance-covariance structures, two procedures.
2016-06-07 18:13:37 UTC; Timothy Schwinghamer
Ameur Manceur [aut],
Timothy Schwinghamer [cre],
Pierre Dutilleul [aut]