R/stagedtrees.R

In stagedtrees: Staged Event Trees

#' Staged event trees.
#'
#' Algorithms to create, learn, fit and explore staged event tree models.
#' Functions to compute probabilities, make predictions from the fitted
#' models and to plot, analyze and manipulate staged event trees.
#'
#' A staged event tree is a representation of a particular
#' factorization of a joint probability over a product space.
#' In particular, given a vector of categorical random variables
#' \eqn{X1, X2, \ldots}, a staged event tree represents the factorization
#' \eqn{P(X1, X2, X3, \ldots) = P(X1)P(X2 | X1) P(X3 | X1, X2) \ldots }.
#' Additionally, the stages structure indicates which conditional probabilities
#' are equal.
#'
#'
#' Model selection algorithms:
#' * full model \code{\link{full}}
#' * independence model \code{\link{indep}}
#' * Hill-Climbing \code{\link{stages_hc}}
#' * Backward Hill-Climbing \code{\link{stages_bhc}}
#' * Fast Backward Hill-Climbing \code{\link{stages_fbhc}}
#' * Backward Hill-Climbing Random \code{\link{stages_bhcr}}
#' * Backward joining \code{\link{stages_bj}}
#' * Hierarchical Clustering \code{\link{stages_hclust}}
#' * K-Means Clustering \code{\link{stages_kmeans}}
#' * Optimal order search \code{\link{search_best}}
#' * Greedy order search \code{\link{search_greedy}}
#'
#' Probabilities, log-likelihood and predictions:
#' * Marginal/Conditional probabilities \code{\link{prob}}
#' * Log-Likelihood \code{\link{logLik.sevt}}
#' * Predict method \code{\link{predict.sevt}}
#' * Confidence intervals \code{\link{confint.sevt}}
#'
#' Plot, explore and compare:
#' * Plot \code{\link{plot.sevt}}
#' * Compare \code{\link{compare_stages}}
#' * Stages inclusion \code{\link{inclusions_stages}}
#' * Stages info \code{\link{summary.sevt}}
#' * List of parents \code{\link{as_parentslist}}
#' * Barplot construction \code{\link{barplot.sevt}}
#' * Likelihood-ratio test \code{\link{lr_test}}
#' * Context-specific interventional distance \code{\link{cid}}
#'
#' Modify models:
#'  * Join and isolate unobserved situations \code{\link{join_unobserved}}
#'  * Join two stages \code{\link{join_stages}}
#'  * Rename a stage \code{\link{rename_stage}}
#' @docType package
#' @name stagedtrees
#' @references
#' Collazo R. A., Görgen C. and Smith J. Q.
#' Chain event graphs. CRC Press, 2018.
#'
#' Görgen C., Bigatti A., Riccomagno E. and Smith J. Q.
#' Discovery of statistical equivalence classes using computer algebra.
#' _International Journal of Approximate Reasoning_, vol. 95, pp. 167-184, 2018.
#'
#' Barclay L. M., Hutton J. L. and Smith J. Q.
#' Refining a Bayesian network using a chain event graph.
#' _International Journal of Approximate Reasoning_, vol. 54, pp. 1300-1309, 2013.
#'
#' Smith J. Q. and Anderson P. E.
#' Conditional independence and chain event graphs.
#' _Artificial Intelligence_, vol. 172, pp. 42-68, 2008.
#'
#' Thwaites P. A., Smith, J. Q.
#' A new method for tackling asymmetric decision problems.
#' _International Journal of Approximate Reasoning_, vol. 88, pp. 624–639, 2017.
#' @examples
#' data("PhDArticles")
#' mf <- full(PhDArticles, join_unobserved = TRUE)
#' mod <- stages_fbhc(mf)
#' plot(mod)
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