c(
person("Benno", "Simmons", email = "benno.simmons@gmail.com", role = c("aut", "cre")),
person("Michelle", "Sweering", email = "michelle.sweering@gmail.com", role = c("aut")),
person("Maybritt", "Schillinger", email = "maybritt.schillinger@gmail.com", role = c("aut")),
person("Riccardo", "Di Clemente", email = "rdicle@mit.edu", role = c("aut")))
Counts occurrences of motifs in bipartite networks, as well as the number of
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
applied to any bipartite network.
Benno Simmons [aut, cre],
Michelle Sweering [aut],
Maybritt Schillinger [aut],
Riccardo Di Clemente [aut]

Counts occurrences of motifs in bipartite networks, as well as the number of
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
applied to any bipartite network. Full details are given in Simmons et al. (2019)
<doi:10.1111/2041-210X.13149>.

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

Reads Windows BMP format images. Currently limited to 8 bit
greyscale images and 24,32 bit (A)RGB images. Pure R implementation without
external dependencies.

Bundle Methods for Regularized Risk Minimization Package
An implementation of "Bundle Methods for Regularized Risk
Minimization" (BMRM) inspired by work of Teo et al., JMLR 2010.

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).