This is a implementation of design methods for multi-state reliability demonstration tests (MSRDT) with failure count data, 
    which is associated with the work from the published paper "Multi-state Reliability Demonstration Tests" by Suiyao Chen et al. (2017) <doi:10.1080/08982112.2017.1314493>.
    It implements two types of MSRDT, multiple periods (MP) and multiple failure modes (MFM). 
    For MP, two different scenarios with criteria on cumulative periods (Cum) or separate periods (Sep) are implemented respectively.
    It also provides the implementation of conventional design method, namely binomial tests for failure count data.

A collection of helper functions for analyzing Second Primary Cancer data, 
    including functions to reshape data, to calculate patient states and analyze cancer incidence.

Tools that extend the functionality of the 'RODBC' package to work
  with Microsoft SQL Server databases. Makes it easier to browse the database
  and examine individual tables and views.

Constructs trees for multivariate survival data using marginal and frailty models.
    Grows, prunes, and selects the best-sized tree.

Contains functions for data preparation, descriptives, hazard estimation and prediction with Aalen-Johansen or simulation in competing risks and multi-state models, see Putter, Fiocco, Geskus (2007) <doi:10.1002/sim.2712>.

This is a tool for epidemiologist, medical data analyst, 
    medical or public health professionals. It contains three domains of functions:
    1) data management, 2) statistical analysis and 3) calculating 
    epidemiological measures.

Implements a minimum-spanning-tree-based heuristic for k-means clustering using a union-find disjoint set and the algorithm in Kruskal (1956) <doi:10.1090/S0002-9939-1956-0078686-7>.

A collection of statistical tests for the detection of differential
    item functioning (DIF) in multistage tests. Methods entail logistic regression,
    an adaptation of the simultaneous item bias test (SIBTEST), and various score-based tests.
    The presented tests provide itemwise test for DIF along categorical, ordinal or metric covariates. Methods for uniform and non-uniform 
    DIF effects are available depending on which method is used.