Packaged: 2020-05-31 22:11:33 UTC; devin.incerti

Packaged: 2020-09-29 05:56:49 UTC; devin.incerti

Packaged: 2020-10-02 06:17:36 UTC; devin.incerti

Imports: data.table, flexsurv, MASS, msm, Rcpp (>= 0.12.16), R6, stats,
Suggests: covr, ggplot2, kableExtra, knitr, mstate, nnet, numDeriv,
Packaged: 2020-12-06 01:53:00 UTC; devin.incerti

Title: Health Economic Simulation Modeling and Decision Analysis
parameterizing, simulating, and analyzing health economic simulation
Imports: data.table, flexsurv, ggplot2, MASS, msm, Rcpp (>= 0.12.16),
Suggests: covr, kableExtra, knitr, magrittr, mstate, nnet, numDeriv,
Packaged: 2021-02-17 16:56:14 UTC; devin.incerti

Packaged: 2021-07-25 23:39:14 UTC; devin.incerti

Title: White's Test for Heteroskedasticity
Description: An implementation of White's Test for Heteroskedasticity
Packaged: 2013-02-27 21:10:14 UTC; Sebastian

Title: Heteroskedastic Gaussian Process Modeling and Design under
Author: Mickael Binois, Robert B. Gramacy
Description: Performs Gaussian process regression with heteroskedastic noise following Binois, M., Gramacy, R., Ludkovski, M. (2016) <arXiv:1611.05902>. The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.
Imports: Rcpp (>= 0.12.3), MASS, methods, DiceDesign

Suggests: knitr, monomvn, lhs, colorspace

Description: Performs Gaussian process regression with heteroskedastic noise following the model by Binois, M., Gramacy, R., Ludkovski, M. (2016) <arXiv:1611.05902>, with implementation details in Binois, M. & Gramacy, R. B. (2021) <doi:10.18637/jss.v098.i13>. The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.