Depends: DiceKriging, pbivnorm, rgenoud, randtoolbox
Description: Criteria and algorithms for sequentially estimating level sets of a multivariate numerical function, possibly observed with noise.
Packaged: 2014-12-22 09:55:13 UTC; chevalier

Packaged: 2012-08-10 08:32:23 UTC; chevalier

Title: Kriging-Based Inversion for Deterministic and Noisy Computer
Author: Clement Chevalier, Victor Picheny, David Ginsbourger and Dario Azzimonti
Imports: randtoolbox, rgenoud, pbivnorm, anMC, mvtnorm

Packaged: 2022-09-09 13:37:24 UTC; dario.azzimonti
Dario Azzimonti [aut, cre] (<https://orcid.org/0000-0001-5080-3061>),

Title: Check the power of a statistical model.
Depends: e1071, caret, nnet, MASS, caTools, mda, glmnet, randomForest,
Description: Check the power of a statistical model moving sample size
License: GNU General Public License Version 2

Title: Check the power of seveal statistical models.

Title: Performs Kurtosis-Based Statistical Analyses
Authors@R: c(person("Cinzia", "Franceschini", role = c("aut", "cre"),
person("Nicola", "Loperfido", role = "aut",
Description: Computes measures of multivariate kurtosis, matrices of fourth-order moments and cumulants, kurtosis-based projection pursuit.
Franceschini, C. and Loperfido, N. (2018, ISBN:978-3-319-73905-2). "An Algorithm for Finding Projections with Extreme Kurtosis".
Loperfido, N. (2017,ISSN:0024-3795). "A New Kurtosis Matrix, with Statistical Applications".
Depends: labstatR (>= 1.0.9), matrixcalc (>= 1.0.3), polynom (>=

Depends: labstatR (>= 1.0.11), matrixcalc (>= 1.0.5), polynom (>=

Title: Fast Algorithms for Best Subset Selection
Description: Highly optimized toolkit for (approximately) solving L0-regularized learning problems.
The algorithms are based on coordinate descent and local combinatorial search.
For more details, check the paper by Hazimeh and Mazumder (2018) <arXiv:1803.01454>;
the link is provided in the URL field below.
Imports: Rcpp (>= 0.12.13), Matrix, methods, ggplot2, reshape2

Description: Highly optimized toolkit for approximately solving L0-regularized learning problems (aka best subset selection).