Title: Data Manipulation with Parallelism and Shared Memory Matrices
Description: Provides a new form of data frame backed by shared memory matrices and a way to manipulate them. Upon creation these data frames are shared across multiple local nodes to allow for simple parallel processing.
Authors@R: c(person(given="Jim", family="Blundell", role = c("aut", "cre", "cph"), email = "james.blundell@keble.ox.ac.uk"))
Imports: bigmemory (>= 4.5.19), bigmemory.sri (>= 0.1.3), lazyeval (>=

Description: Provides a new form of data frame backed by shared memory matrices
and a way to manipulate them. Upon creation these data frames are shared across
multiple local nodes to allow for simple parallel processing.
Authors@R: c(person(given="Jim", family="Blundell", role = c("aut", "cre", "cph"), email = "jeblundell@doctors.org.uk"))
Imports: bigmemory (>= 4.5.19), bigmemory.sri (>= 0.1.3), parallel,

Description: Various utilities to manipulate multivariate polynomials
Packaged: Mon Apr 21 22:13:33 2008; rksh

Packaged: Thu May  8 22:29:03 2008; rksh

Description: Various utilities to manipulate multivariate polynomials.

Title: Calculate Multivariate Richness via UTC and sUTC
Description: Functions to calculate Unique Trait Combinations (UTC) and
scaled Unique Trait Combinations (sUTC) as measures of multivariate
richness. The package can also calculate beta-diversity for trait richness and
can partition this into nestedness-related and turnover components. The
code will also calculate several measures of overlap.

Description: Functions to calculate Unique Trait Combinations (UTC) and scaled
Unique Trait Combinations (sUTC) as measures of multivariate richness. The
package can also calculate beta-diversity for trait richness and can
partition this into nestedness-related and turnover components. The code
will also calculate several measures of overlap.

will also calculate several measures of overlap. See Keyel and Wiegand

Title: Fast Cross-Validation for Multi-Penalty Ridge Regression
Maintainer: Mark A. van de Wiel <mark.vdwiel@amsterdamumc.nl>
Depends: R (>= 3.5.0), survival, pROC, methods, mgcv, snowfall
Description: Multi-penalty linear, logistic and cox ridge regression, including estimation of the penalty parameters by efficient (repeated) cross-validation and marginal likelihood maximization. Multiple high-dimensional data types that require penalization are allowed, as well as unpenalized variables. Paired and preferential data types can be specified. See Van de Wiel et al. (2021), <arXiv:2005.09301>.