Description: Bayesian multinomial mixture model

Title: Counting Motifs in Bipartite Networks
person("Benno", "Simmons", email = "benno.simmons@gmail.com", role = c("aut", "cre")),
person("Michelle", "Sweering", email = "michelle.sweering@gmail.com", role = c("aut")),
person("Riccardo", "Di Clemente", email = "rdicle@mit.edu", role = c("aut")))
Description: Counts occurrences of motifs in bipartite networks, as well as the number of
times each node appears in each unique position within motifs. Intended for
use in ecology, but its methods are general and can be applied to any
Packaged: 2018-01-26 16:36:39 UTC; bennosimmons

Title: Motif Analyses of Bipartite Networks
person("Maybritt", "Schillinger", email = "maybritt.schillinger@gmail.com", role = c("aut")),
times each node or link appears in each unique position within motifs. Has support for both
binary and weighted motifs: can calculate the mean weight of motifs and the standard deviation
of their mean weights. Intended for use in ecology, but its methods are general and can be
Packaged: 2019-12-11 15:18:24 UTC; bennosimmons

Packaged: 2020-07-01 11:55:10 UTC; bennosimmons

applied to any bipartite network. Full details are given in Simmons et al. (2019)
Packaged: 2020-09-04 10:20:35 UTC; bennosimmons

Description: Reads Windows BMP format images. Currently limited to 8 bit
greyscale images and 24 bit RGB images. Pure R implementation without

greyscale images and 24,32 bit (A)RGB images. Pure R implementation without

Title: Bundle Methods for Regularized Risk Minimization Package
Description: An implementation of "Bundle Methods for Regularized Risk
Minimization" (BMRM) inspired by work of Teo et al., JMLR 2010.
Collate: 'bmrm.R' 'scalarLoss.R' 'cost.R' 'vectorialLoss.R'

Description: An implementation of "Bundle Methods for Regularized Risk Minimization" (BMRM) inspired by work of Teo et al., JMLR 2010. This universal data mining framework is particularly useful for structured prediction, classification and regression on big data. The package implements, in particular, linear SVM with L1 and L2 regularization, multiclass SVM, f-beta optimization, ordinal regression, quantile regression (see package examples).