A network-based systems biology tool for flexible identification of phenotype-specific subpathways in the cancer gene expression data 
  with multiple categories (such as multiple subtype or developmental stages of cancer). Subtype Set Enrichment Analysis (SubSEA) and Dynamic Changed 
  Subpathway Analysis (DCSA) are developed to flexible identify subtype specific and dynamic changed subpathways respectively. The operation modes 
  include extraction of subpathways from biological pathways, inference of subpathway activities in the context of gene expression data, identification 
  of subtype specific subpathways with SubSEA, identification of dynamic changed subpathways associated with the cancer developmental stage with DCSA, 
  and visualization of the activities of resulting subpathways by using box plots and heat maps. Its capabilities render the tool could find the specific
  abnormal subpathways in the cancer dataset with multi-phenotype samples.

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).