Implements propensity score weighting methods for estimating
    counterfactual survival functions, marginal hazard ratios, and
    weighted Kaplan-Meier and cumulative risk curves in observational
    studies with time-to-event outcomes. Supports binary and multiple
    treatment groups with inverse probability of treatment weighting (IPW),
    overlap weighting (OW), and average treatment effect on the treated
    (ATT). Includes symmetric trimming (Crump extension) for extreme
    propensity scores. Variance estimation via analytical M-estimation or
    bootstrap. Methods based on Li et al. (2018) <doi:10.1080/01621459.2016.1260466>,
    Li & Li (2019) <doi:10.1214/19-AOAS1282>, and Cheng et al. (2022)
    <doi:10.1093/aje/kwac043>.

Calculating Pst values to assess differentiation among populations from a set of quantitative traits is the primary purpose of such a package. The bootstrap method provides confidence intervals and distribution histograms of Pst. Variations of Pst in function of the parameter c/h^2 are studied as well. Finally, the package proposes different transformations especially to eliminate any variation resulting from allometric growth (calculation of residuals from linear regressions, Reist standardizations or Aitchison transformation).

Implements the Panel Smooth Transition Regression (PSTR) framework for nonlinear panel data modelling.
    The modelling procedure consists of three stages: Specification, Estimation and Evaluation.
    The package provides tools for model specification testing,
    to do PSTR model estimation, and to do model evaluation.
    The implemented tests allow for cluster dependence and are heteroskedasticity-consistent.
    The wild bootstrap and wild cluster bootstrap tests are also implemented.
    Parallel computation (as an option) is implemented in some functions, especially the bootstrap tests.
    The package supports parallel computation, which is useful for large-scale bootstrap procedures.

Estimating causal effects in the presence of post-treatment confounding
    using principal stratification. 'PStrata' allows for customized monotonicity
    assumptions and exclusion restriction assumptions, with automatic full Bayesian
    inference supported by 'Stan'. The main workflow is PStrataModel() to specify the
    model, fit() to run MCMC sampling, estimate() to extract potential outcomes, and
    contrast() to compute causal effects. Visualization tools are provided for
    diagnosis and interpretation. See Liu and Li (2023)
    <doi:10.48550/arXiv.2304.02740> for details.