Title: General ANOVA Design (GAD): Analysis of variance from general
Author: Leonardo Sandrini-Neto & Mauricio G. Camargo
Depends: R (>= 2.7.0), stats, matrixStats, R.methodsS3
Description: This package analyses complex ANOVA models with any
combination of orthogonal/nested and fixed/random factors, as
described by Underwood (1997). There are two restrictions: (i)
data must be balanced; (ii) fixed nested factors are not
allowed. Homogeneity of variances is checked using Cochran's C
test and "a posteriori" comparisons of means are done using

Title: GAD: Analysis of variance from general principles
test and 'a posteriori' comparisons of means are done using

Package: GAD
Type: Package
Title: GAD: Analysis of variance from general principles
Version: 1.1
Date: 2011-02-08
Author: Leonardo Sandrini-Neto & Mauricio G. Camargo
Maintainer: Leonardo Sandrini-Neto <leonardosandrini@gmail.com>
Depends: R (>= 2.7.0), stats, matrixStats, R.methodsS3
Description: This package analyses complex ANOVA models with any
combination of orthogonal/nested and fixed/random factors, as
described by Underwood (1997). There are two restrictions: (i)
data must be balanced; (ii) fixed nested factors are not
allowed. Homogeneity of variances is checked using Cochran's C
test and "a posteriori" comparisons of means are done using
Student-Newman-Keuls (SNK) procedure.
License: GPL (>= 2.0)
Packaged: 2011-02-08 14:37:56 UTC; leonardosandrini
Repository: CRAN
Date/Publication: 2011-02-08 15:49:58

Title: A Genetic Algorithm for Learning Directed Acyclic Graphs
Author: Magali Champion, Victor Picheny and Matthieu Vignes
Description: Sparse large Directed Acyclic Graphs learning with a combination of a convex program and a tailored genetic algorithm (see Champion et al. (2017) <https://hal.archives-ouvertes.fr/hal-01172745v2/document>).

Title: Gaussian Process Approximations for Designing Experiments
Description: Computes near-optimal Bayesian experimental designs with Gaussian processes optimization following the algorithm presented by  B. Weaver, et al. (2016) <doi:10.1214/15-BA945> for either physical or sequential computer experiments.
Imports: DiceKriging, DiceOptim, lhs, pbapply, utils, graphics, stats
Packaged: 2020-01-16 18:50:39 UTC; isaacmichaud