Provides functions to perform
    reproducible parallel foreach loops, using independent
    random streams as generated by L'Ecuyer's combined
    multiple-recursive generator [L'Ecuyer (1999), <DOI:10.1287/opre.47.1.159>].
    It enables to easily convert standard '%dopar%' loops into
    fully reproducible loops, independently of the number
    of workers, the task scheduling strategy, or the chosen
    parallel environment and associated foreach backend.

Contains data sets, examples and software from the book Design of Observational Studies by Paul R. Rosenbaum, New York: Springer, <doi:10.1007/978-1-4419-1213-8>, ISBN 978-1-4419-1212-1.

Contains data sets, examples and software from the Second Edition of "Design of Observational Studies"; see Rosenbaum, P.R. (2010)  <doi:10.1007/978-1-4419-1213-8>.

Identification of causal effects from arbitrary observational and 
    experimental probability distributions via do-calculus and standard 
    probability manipulations using a search-based algorithm by 
    Tikka, Hyttinen and Karvanen (2021) <doi:10.18637/jss.v099.i05>. 
    Allows for the presence of  mechanisms related to selection bias 
    (Bareinboim and Tian, 2015) <doi:10.1609/aaai.v29i1.9679>, 
    transportability (Bareinboim and Pearl, 2014) 
    <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>, 
    missing data (Mohan, Pearl, and Tian, 2013) 
    <http://ftp.cs.ucla.edu/pub/stat_ser/r410.pdf>) and arbitrary combinations 
    of these. Also supports identification in the presence of context-specific 
    independence (CSI) relations through labeled directed acyclic graphs 
    (LDAG). For details on CSIs see (Corander et al., 2019) 
    <doi:10.1016/j.apal.2019.04.004>.

Provides the user with an interactive application which can be used to facilitate the planning of dose finding studies by applying the theory of optimal experimental design.

The DoseFinding package provides functions for the design and analysis
	     of dose-finding experiments (with focus on pharmaceutical Phase
	     II clinical trials). It provides functions for: multiple contrast
	     tests, fitting non-linear dose-response models (using Bayesian and
	     non-Bayesian estimation), calculating optimal designs and an
	     implementation of the MCPMod methodology (Pinheiro et al. (2014)
	     <doi:10.1002/sim.6052>).