Title: Parallel Versions of the Gradient-Based optim() Methods
Description: Provides parallel versions of the gradient-based optim() methods. The main function of the package is optimParallel(), which has the same usage and output as optim(). Using optimParallel() can significantly reduce the optimization time.
Suggests: R.rsp, roxygen2, spam, microbenchmark, testthat, ggplot2,

Title: Parallel Version of the L-BFGS-B Optimization Method
Description: Provides a parallel version of the L-BFGS-B method of optim(). The main function of the package is optimParallel(), which has the same usage and output as optim(). Using optimParallel() can significantly reduce the optimization time.

Description: A package intended to assist in the choice of the sample strategy to implement in a survey. It compares five strategies having into account the information available in an auxiliary variable and two superpopulation models, called working and true models.

Description: Intended to assist in the choice of the sampling strategy to implement in a survey. It compares five strategies having into account the information available in an auxiliary variable and two superpopulation models, called working and true models.

Description: Intended to assist in the choice of the sampling strategy to implement in a survey.

Title: Bayesian Methods for Optimal Threshold Estimation
Authors@R: person("Yoann", "Blangero", email = "yblangero@gmail.com", role = c("aut", "cre"))
Description: Functions to estimate the optimal threshold of diagnostic markers or treatment selection markers. The optimal threshold is the marker value that maximizes the utility of the marker based-strategy (for diagnostic or treatment selection) in a given population. The utility function depends on the type of marker (diagnostic or treatment selection), but always takes into account the preferences of the patients or the physician in the decision process. For estimating the optimal threshold, ones must specify the distributions of the marker in different groups (defined according to the type of marker, diagnostic or treatment selection) and provides data to estimate the parameters of these distributions. Ones must also provide some features of the target populations (disease prevalence or treatment efficacies) as well as the preferences of patients or physicians. The functions rely on Bayesian inference which helps producing several indicators derived from the optimal threshold. See Blangero, Y, Rabilloud, M, Ecochard, R, and Subtil, F (2019) <doi:10.1177/0962280218821394> for the original article that describes the estimation method for treatment selection markers and Subtil, F, and Rabilloud, M (2019) <doi:10.1002/bimj.200900242> for diagnostic markers.
Collate: Package.R import_package.R scaledT.R ClassUnions.R
ClassDiagRelUtility.R cdf.R gradient.R hessian.R ARS.R
Imports: ars, rjags, HDInterval, mgcv, utils, coda, grDevices, methods,