Title: Read, Plot and Analyse Output from the DEPONS Model
Description: Methods for analyzing population dynamics and movement tracks simulated using  the DEPONS model <https://www.depons.eu>, and for manipulating input raster files.
Imports: raster, methods, sp, utils, grDevices, xml2

Description: Methods for analyzing population dynamics and movement tracks simulated using the DEPONS model <https://www.depons.eu>, and for manipulating input raster files and shipping routes.
Imports: raster, methods, sp, utils, grDevices, xml2, rgdal, rgeos,

Author: Jacob Nabe-Nielsen and Caitlin K. Frankish

Description: Discrete event simulation (DES) involves modeling of systems
having discrete, i.e. abrupt, state changes. For instance, when a job
arrives to a queue, the queue length abruptly increases by 1.  This
package is an R implementation of the event-oriented approach to DES;

Title: Estimation of Diagonal Elements of Sparse Precision-Matrices
Authors@R: c(person("Arnak", "Dalalyan", role = c("aut"),
person("Samuel", "Balmand", role = c("aut","cre"),
Author: Arnak Dalalyan [aut], Samuel Balmand [aut, cre]
Description: Several estimators of the diagonal elements of a sparse precision
(inverse covariance) matrix from a sample of Gaussian vectors for a
given matrix of estimated marginal regression coefficients.
To install package 'gurobi', instructions at
Imports: MASS, Matrix, graph, RBGL, SparseM

To install gurobi, follow the instructions at

given matrix of estimated marginal regression coefficients. Moreover, a robust estimator
Imports: MASS, Matrix, graph, RBGL, SparseM, stats, methods

Imports: MASS, Matrix, graph, RBGL, SparseM, stats

Title: Bagging with Distance-based Regression for Differential Gene
Imports: clue, combinat, MASS, parallel, cluster
Description: This package implements a statistical data mining method to
compare whole genome gene expression profiles, with respect to the presence
of a recurrent genetic disturbance event, to identify the affected target

Title: Representation of DET Curve with Confidence Intervals
Builds both ROC (Receiver Operating Characteristic) and DET (Detection Error Tradeoff) curves from a set of predictors, which are the
results of a binary classification system. The curves give a general vision of the performance of the classifier,
and are useful for comparing performance of different systems.
Author: García-Ródenas, Álvaro [aut, cre],