Uses simulation to create prediction intervals for
    post-policy outcomes in interrupted time series (ITS) designs,
    following Miratrix (2020) <arXiv:2002.05746>. This package provides
    methods for fitting ITS models with lagged outcomes and variables to
    account for temporal dependencies.  It then conducts inference via
    simulation, simulating a set of plausible counterfactual post-policy
    series to compare to the observed post-policy series. This package
    also provides methods to visualize such data, and also to incorporate
    seasonality models and smoothing and aggregation/summarization.  This
    work partially funded by Arnold Ventures in collaboration with
    MDRC.

Simulate multivariate correlated data given nonparametric marginals and their joint structure characterized by a Pearson or Spearman correlation matrix. The simulator engages the problem from a purely computational perspective. It assumes no statistical models such as copulas or parametric distributions, and can approximate the target correlations regardless of theoretical feasibility. The algorithm integrates and advances the Iman-Conover (1982) approach <doi:10.1080/03610918208812265> and the Ruscio-Kaczetow iteration (2008) <doi:10.1080/00273170802285693>. Package functions are carefully implemented in C++ for squeezing computing speed, suitable for large input in a manycore environment. Precision of the approximation and computing speed both substantially outperform various CRAN packages to date. Benchmarks are detailed in function examples. A simple heuristic algorithm is additionally designed to optimize the joint distribution in the post-simulation stage. The heuristic demonstrated good potential of achieving the same level of precision of approximation without the enhanced Iman-Conover-Ruscio-Kaczetow. The package contains a copy of Permuted Congruential Generator.

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).