Description: Functions for performing least-squares bilinear clustering of
three-way data. The method uses the bilinear decomposition (or biadditive
model) to model two-way matrix slices while clustering over the third way.
Up to four different types of clusters are included, one for each term of the
bilinear decomposition. In this way, matrices are clustered simultaneously on
(a subset of) their overall means, row margins, column margins and row-column
interactions. The orthogonality of the bilinear model results in separability of
the joint clustering problem into four separate ones. Three of these subproblems
are specific k-means problems, while a special algorithm is implemented for the
interactions. Plotting methods are provided, including biplots for the low-rank
Depends: R (>= 3.2.2), stats, parallel, ggplot2

three-way data. The method uses the bilinear decomposition (or bi-additive
the joint clustering problem into four separate ones. Three of these sub-problems
Imports: plyr, clue, grid, gridExtra, reshape2, Rcpp, mvtnorm,
graphics, methods, doParallel, foreach, parallel

Description: Functions for performing least-squares bilinear clustering of three-way data. The method uses the bilinear decomposition (or biadditive model) to model two-way matrix slices while clustering over the third way. Up to four different types of clusters are included, one for each term of the bilinear decomposition. In this way, matrices are clustered simultaneously on (a subset of) their overall means, row margins, column margins and row-column interactions. The orthogonality of the bilinear model results in separability of the joint clustering problem into four separate ones. Three of these subproblems are specific k-means problems, while a special algorithm is implemented for the interactions. Plotting methods are provided, including biplots for the low-rank approximations of the interactions.

Title: Bandwidth Selection for Level Sets and HDR Estimation
Authors@R: person("Guangwei", "Weng", email = "wengx076@umn.edu", role = c("aut", "cre"))
Description: Bandwidth selection for kernel density estimators of 2-d
level sets and highest density regions. It applies a plug-in
strategy to estimate the asymptotic risk function and minimize to
get the optimal bandwidth matrix. See Doss and Weng (2018)