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).

The propensity score is one of the most widely used tools in
    studying the causal effect of a treatment, intervention, or policy. Given that
    the propensity score is usually unknown, it has to be estimated, implying that
    the reliability of many treatment effect estimators depends on the correct
    specification of the (parametric) propensity score. This package implements the
    data-driven nonparametric diagnostic tools for detecting propensity score
    misspecification proposed by Sant'Anna and Song (2019) <doi:10.1016/j.jeconom.2019.02.002>.

For a data matrix with m rows and n columns (m>=n), the power
	     method is used to compute, simultaneously, the eigendecomposition
	     of a square symmetric matrix. This result is used to obtain the
	     singular value decomposition (SVD) and the principal component
             analysis (PCA) results. Compared to the classical SVD method,
	     the first r singular values can be computed.