Functions for the stratigraphic analysis of phylogenetic trees.

An implementation of the stratification index proposed by Zhou (2012) <DOI:10.1177/0081175012452207>.
  The package provides two functions, srank, which returns stratum-specific
  information, including population share and average percentile rank; and strat,
  which returns the stratification index and its approximate standard error.
  When a grouping factor is specified, strat also provides a detailed decomposition
  of the overall stratification into between-group and within-group components.

Build a project framework for users with 
    access to only the most basic of automation tools.

Non-proportional hazard (NPH) is commonly observed in immuno-oncology studies, where the survival curves of the treatment and control groups show delayed separation. To properly account for NPH, several statistical methods have been developed. One such method is Max-Combo test, which is a straightforward and flexible hypothesis testing method that can simultaneously test for constant, early, middle, and late treatment effects. However, the majority of the Max-Combo test performed in clinical studies are unstratified, ignoring the important prognostic stratification factors. To fill this gap, we have developed an R package for stratified Max-Combo testing that accounts for stratified baseline factors. Our package explores various methods for calculating combined test statistics, estimating joint distributions, and determining the p-values.

Functions in this package provide solution to classical problem in
  survey methodology - an optimum sample allocation in stratified sampling. In
  this context, the optimum allocation is in the classical Tschuprow-Neyman's
  sense and it satisfies additional lower or upper bounds restrictions imposed
  on sample sizes in strata. There are few different algorithms available to
  use, and one them is based on popular sample allocation method that applies
  Neyman allocation to recursively reduced set of strata.
  This package also provides the function that computes a solution to the
  minimum cost allocation problem, which is a minor modification of the
  classical optimum sample allocation. This problem lies in the determination
  of a vector of strata sample sizes that minimizes total cost of the survey,
  under assumed fixed level of the stratified estimator's variance. As in the
  case of the classical optimum allocation, the problem of minimum cost
  allocation can be complemented by imposing upper-bounds constraints on sample
  sizes in strata.