Create and manage fault-tolerant task queues for the 'foreach' package using the 'Redis' key/value database.

Provides models to fit the dynamics of a regulated system experiencing exogenous inputs. 
    The underlying models use differential equations and linear mixed-effects regressions to estimate the 
    coefficients of the equation. With them, the functions can provide an estimated signal.
    The package provides simulation and analysis functions and also print, summary, plot and predict methods,
    adapted to the function outputs, for easy implementation and presentation of results.

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 et al. (2021) <doi:10.18637/jss.v099.i05>. Allows for the presence of mechanisms related to selection bias (Bareinboim, E. and Tian, J. (2015) <http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf>), transportability (Bareinboim, E. and Pearl, J. (2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>), missing data (Mohan, K. and Pearl, J. and Tian., J. (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>.