Simulate a virtual population of subjects that has demographic distributions (height, weight, and BMI) and correlations (height and weight), by sex and age, which mimic those reported in real-world anthropometric growth charts (CDC, WHO, or Fenton).

A toolbox for constructing potential landscapes for dynamical
    systems using Monte Carlo simulation.  The method is based on the
    potential landscape definition by Wang et al. (2008)
    <doi:10.1073/pnas.0800579105> (also see Zhou & Li, 2016
    <doi:10.1063/1.4943096> for further mathematical discussions) and can
    be used for a large variety of models.

We provide functions for estimation and inference of nonlinear and non-stationary time series regression using the sieve methods and bootstrapping procedure.

A process-oriented and trajectory-based Discrete-Event Simulation
    (DES) package for R. It is designed as a generic yet powerful framework. The
    architecture encloses a robust and fast simulation core written in 'C++' with
    automatic monitoring capabilities. It provides a rich and flexible R API that
    revolves around the concept of trajectory, a common path in the simulation
    model for entities of the same type. Documentation about 'simmer' is provided
    by several vignettes included in this package, via the paper by Ucar, Smeets
    & Azcorra (2019, <doi:10.18637/jss.v090.i02>), and the paper by Ucar,
    Hernández, Serrano & Azcorra (2018, <doi:10.1109/MCOM.2018.1700960>);
    see 'citation("simmer")' for details.

Provides wrappers for common activity patterns in 'simmer' trajectories.

A set of plotting methods for 'simmer' trajectories and simulations.

Allows users to quickly apply individual or multiple metrics to evaluate Monte Carlo simulation studies.

A major challenge in estimating treatment decision rules from a randomized clinical trial dataset with covariates measured at baseline lies in detecting relatively small treatment effect modification-related variability (i.e., the treatment-by-covariates interaction effects on treatment outcomes) against a relatively large non-treatment-related variability (i.e., the main effects of covariates on treatment outcomes). The class of Single-Index Models with Multiple-Links is a novel single-index model specifically designed to estimate a single-index (a linear combination) of the covariates associated with the treatment effect modification-related variability, while allowing a nonlinear association with the treatment outcomes via flexible link functions. The models provide a flexible regression approach to developing treatment decision rules based on patients' data measured at baseline. We refer to Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1016/j.jspi.2019.05.008> and Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1111/biom.13320> (that allows an unspecified X main effect) for detail of the method. The main function of this package is simml().