Title: Methods for Estimating Optimal Dynamic Treatment Regimes
Description: Methods to estimate dynamic treatment regimes using Interactive Q-Learning, Q-Learning, weighted learning, and value-search methods based on Augmented Inverse Probability Weighted Estimators and Inverse Probability Weighted Estimators.

Description: Methods to estimate dynamic treatment regimes using Interactive
Q-Learning, Q-Learning, weighted learning, and value-search methods based on
Augmented Inverse Probability Weighted Estimators and Inverse Probability
Weighted Estimators. Dynamic Treatment Regimes: Statistical Methods for
Precision Medicine, Tsiatis, A. A., Davidian, M. D., Holloway, S. T., and Laber, E. B.,

Title: Inferring Longitudinal Dominance Hierarchies
Maintainer: Eli D. Strauss <straussed@gmail.com>
Description: Provides functions for inferring longitudinal dominance hierarchies, which describe dominance relationships and their dynamics in a single latent hierarchy over time. Strauss & Holekamp (in press).
Packaged: 2019-01-11 20:40:22 UTC; straussed

Packaged: 2020-02-13 14:51:45 UTC; straussed

Title: Dynamically visualized probability distributions and their
Description: The package is aimed at dynamically visualizing probability
distributions and their moments and all the commonly used distributions are
Author: Lei ZHANG, Hao JIANG and Chen XUE(Equally contributed, the order is
decided by the time the author joined the project. )
Packaged: 2013-11-30 02:46:36 UTC; Administrator

Packaged: 2013-12-05 02:30:41 UTC; Administrator

Title: Local Gaussian Process Model for Large-Scale Dynamic Computer
Description: Fits localized GP model for dynamic computer experiments via singular value decomposition of the response matrix Y for large N (the number of observations) using the algorithm proposed by Zhang et al. (2018) <arXiv:1611.09488>. The current version only supports 64-bit architecture.
Authors@R: c(person(given="Ru", family="Zhang", role = c("aut", "cre"), email="heavenmarshal@gmail.com"),

Description: Fits localized GP model for dynamic computer experiments via singular value decomposition of the response matrix Y for large N (the number of observations) using the algorithm proposed by Zhang et al. (2018) <arXiv:1611.09488>. The current version only supports 64-bit version of R.