person("Maëlle", "Salmon", role = c("ctb", "rev")),
person("Mathida", "Chuk", role = c("ctb")))

Title: Convert Gene EXPression Data to FBA FLUXes
Author: Daniel Osorio[aut,cre], Kelly Botero[aut,cre], Janneth Gonzalez[aut],
Description: For a given metabolic model with well formed Gene-Protein-Reaction (GPR) associations and an expressionSet with their associated gene expression values, this package converts gene expression values to the FBA boundaries for each reaction based in the boolean rules described in its associated GPR.

Description: A SQLite database is designed to store all information
of experiment-based data including metadata, experiment design,
managements, phenotypic values and climate records. The dataset can be
Imports: DBI, RSQLite, utils, readxl, png, grid, tibble, lubridate,
dplyr, methods, weaana (>= 0.1.1), tidyr, tidyverse, magrittr,

Title: An Exponential Stochastic Block Model for Interaction Lengths
Authors@R: c(person("Riccardo", "Rastelli", role = c("aut", "cre"),
Description: Given a continuous-time dynamic network, this package allows one to fit a stochastic blockmodel where nodes belonging to the same group create interactions and non-interactions of similar lengths. This package implements the methodology described by R. Rastelli and M. Fop (2019) <arXiv:1901.09828>.
Imports: Rcpp (>= 0.12.10), mclust, gtools
Author: Riccardo Rastelli [aut, cre] (<https://orcid.org/0000-0003-0982-2935>),

Imports: Rcpp (>= 0.12.10), mclust, gtools, blockmodels

Authors@R: person("Scott", "Miller", email = "sam3CRAN@gmail.com",
Imports: utils, stats, graphics, plyr, orthopolynom, polynom, glmnet
Description: Generates feature matrix outputs from R object inputs
using a variety of expansion functions.  The generated
feature matrices have applications as inputs
for a variety of machine learning algorithms.
The expansion functions are based on coercing the input
to a matrix, treating the columns as features and
converting individual columns or combinations into blocks of
Currently these include expansion of columns by
efficient sparse embedding by vectors of lags,
quadratic expansion into squares and unique products,
vectors of orthogonal polynomials functions,
and block random affine projection transformations (RAPTs).
magrittr- and cbind-friendly, and can be used in a
building block fashion.  For instance, taking the cos() of
the output of the RAPT transformation generates a
stationary kernel expansion via Bochner's theorem, and this
expansion can then be cbind-ed with other features.
Additionally, there are utilities for replacing features,
creating matrix samples of a given distribution,
matrix size-preserving discrete difference by row,