Functions and S4 methods to create and manage discrete time Markov
    chains more easily. In addition functions to perform statistical (fitting
    and drawing random variates) and probabilistic (analysis of their structural
    proprieties) analysis are provided. See Spedicato (2017) <doi:10.32614/RJ-2017-036>.
    Some functions for continuous times Markov chains depend on the suggested ctmcd package.

Functions for cost-optimal control charts with a focus on health care applications. Compared to assumptions in traditional control chart theory, here, we allow random shift sizes, random repair and random sampling times. The package focuses on X-bar charts with a sample size of 1 (representing the monitoring of a single patient at a time). The methods are described in Zempleni et al. (2004) <doi:10.1002/asmb.521>, Dobi and Zempleni (2019) <doi:10.1002/qre.2518> and Dobi and Zempleni (2019) <http://ac.inf.elte.hu/Vol_049_2019/129_49.pdf>.

Fit mixture of Markov chains of higher orders from multiple
    sequences. It is also compatible with ordinary 1-component, 1-order or
    single-sequence Markov chains. Various utility functions are provided
    to derive transition patterns, transition probabilities per component
    and component priors. In addition, print(), predict() and component
    extracting/replacing methods are also defined as a convention of
    mixture models.

The inference in multi-state models is traditionally performed under
             a Markov assumption that claims that past and future of the process
             are independent given the present state. In this package, we 
             consider tests of the Markov assumption that are applicable to 
             general multi-state models. Three approaches using existing 
             methodology are considered: a simple method based on including
             covariates depending on the history in Cox models for the transition
             intensities; methods based on measuring the discrepancy of the 
             non-Markov estimators of the transition probabilities to the 
             Markov Aalen-Johansen estimators; and, finally, methods that were
             developed by considering summaries from families of log-rank 
             statistics where patients are grouped by the state occupied of the
             process at a particular time point (see Soutinho G, Meira-Machado L
             (2021) <doi:10.1007/s00180-021-01139-7> and Titman AC, Putter H
             (2020) <doi:10.1093/biostatistics/kxaa030>).