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#' Blackjack Dealer Outcome Probabilities
#'
#' A dataset containing the conditional probability of various dealer
#' outcomes given the "upcard". (The dealer and player each get two
#' cards; only one of the dealer's cards is shown, and this is called
#' the "upcard")
#'
#' @format A data frame with 70 rows and 3 variables:
#' \describe{
#' \item{dealerUpcard}{dealer upcard}
#' \item{dealerOutcome}{outcome of dealer's hand, under the rule that
#' cards are drawn until the dealer's hand total is at least 17}
#' \item{probability}{conditional probability of dealerOutcome given
#' dealerUpcard}
#' ...
#' }
#' @source \url{https://www.blackjackinfo.com/dealer-outcome-probabilities}
"BJDealer"
#' Black Jack Hybrid Decision Network
#'
#' An object of class \code{HydeNetwork} establishing a graphical model for a game
#' of Black Jack.
#'
#' @format A \code{HydeNetwork} object constructed using the code shown in
#' the example. The network has seven random nodes, three ten deterministic
#' nodes, three decision nodes, and one utility node. This is (almost) the same
#' network used in `vignette("DecisionNetworks", package="HydeNet")`.
#'
#' @examples
#' \dontrun{
#' BlackJack <-
#' HydeNetwork(~ initialAces | card1*card2
#' + initialPoints | card1*card2
#' + highUpcard | dealerUpcard
#' + hit1 | initialPoints*highUpcard
#' + acesAfterCard3 | initialAces*card3
#' + pointsAfterCard3 | card1*card2*card3*acesAfterCard3
#' + hit2 | pointsAfterCard3*highUpcard
#' + acesAfterCard4 | acesAfterCard3*card4
#' + pointsAfterCard4 | card1*card2*card3*card4*acesAfterCard4
#' + hit3 | pointsAfterCard4*highUpcard
#' + acesAfterCard5 | acesAfterCard4*card5
#' + pointsAfterCard5 | card1*card2*card3*card4*card5*acesAfterCard5
#' + playerFinalPoints | initialPoints*hit1*pointsAfterCard3
#' *hit2*pointsAfterCard4*hit3*pointsAfterCard5
#' + dealerFinalPoints | dealerUpcard
#' + payoff | playerFinalPoints*dealerFinalPoints)
#' cardProbs <- c(rep(1/13,8), 4/13, 1/13) # probs. for 2, 3, ..., 9, (10-K), A
#'
#' BlackJack <- setNode(BlackJack, card1, nodeType="dcat",
#' pi=vectorProbs(p=cardProbs, card1))
#' BlackJack <- setNode(BlackJack, card2, nodeType="dcat",
#' pi=vectorProbs(p=cardProbs, card2))
#' BlackJack <- setNode(BlackJack, card3, nodeType="dcat",
#' pi=vectorProbs(p=cardProbs, card3))
#' BlackJack <- setNode(BlackJack, card4, nodeType="dcat",
#' pi=vectorProbs(p=cardProbs, card4))
#' BlackJack <- setNode(BlackJack, card5, nodeType="dcat",
#' pi=vectorProbs(p=cardProbs, card5))
#'
#' BlackJack <- setNode(BlackJack, dealerUpcard, nodeType="dcat",
#' pi=vectorProbs(p=cardProbs, dealerUpcard))
#'
#' #Note: node dealerFinalPoints will be defined below, following some discussion
#' # about its conditional probability distribution.
#'
#' #####################################
#' # Deterministic Nodes
#' #####################################
#' BlackJack <- setNode(BlackJack, highUpcard,
#' "determ", define=fromFormula(),
#' nodeFormula = highUpcard ~ ifelse(dealerUpcard > 8, 1, 0))
#'
#' BlackJack <- setNode(BlackJack, initialAces,
#' "determ", define=fromFormula(),
#' nodeFormula = initialAces ~ ifelse(card1==10,1,0) +
#' ifelse(card2==10,1,0))
#'
#' BlackJack <- setNode(BlackJack, acesAfterCard3,
#' "determ", define=fromFormula(),
#' nodeFormula = acesAfterCard3 ~ initialAces + ifelse(card3==10,1,0))
#'
#' BlackJack <- setNode(BlackJack, acesAfterCard4,
#' "determ", define=fromFormula(),
#' nodeFormula = acesAfterCard4 ~ acesAfterCard3 + ifelse(card4==10,1,0))
#'
#' BlackJack <- setNode(BlackJack, acesAfterCard5,
#' "determ", define=fromFormula(),
#' nodeFormula = acesAfterCard5 ~ acesAfterCard4 + ifelse(card5==10,1,0))
#'
#' BlackJack <- setNode(BlackJack, initialPoints,
#' "determ", define=fromFormula(),
#' nodeFormula = initialPoints ~ card1+card2+2)
#'
#' BlackJack <- setNode(BlackJack, pointsAfterCard3, "determ", define=fromFormula(),
#' nodeFormula = pointsAfterCard3 ~
#' ifelse(acesAfterCard3 == 3,
#' 13,
#' ifelse(acesAfterCard3 == 2,
#' card1 + card2 + card3 + 3 - 10,
#' ifelse(acesAfterCard3 == 1,
#' ifelse(card1 + card2 + card3 + 3 > 22,
#' card1 + card2 + card3 + 3 - 10,
#' card1 + card2 + card3 + 3),
#' card1 + card2 + card3 + 3
#' )
#' )
#' )
#' )
#'
#' BlackJack <- setNode(BlackJack, pointsAfterCard4, "determ", define=fromFormula(),
#' nodeFormula = pointsAfterCard4 ~
#' ifelse(acesAfterCard4 == 4,
#' 14,
#' ifelse(acesAfterCard4 == 3,
#' ifelse(card1 + card2 + card3 + card4 + 4 > 38,
#' card1 + card2 + card3 + card4 + 4 - 30,
#' card1 + card2 + card3 + card4 + 4 - 20
#' ),
#' ifelse(acesAfterCard4 > 0,
#' ifelse(card1 + card2 + card3 + card4 + 4 > 22,
#' card1 + card2 + card3 + card4 + 4 - 10,
#' card1 + card2 + card3 + card4 + 4
#' ),
#' card1 + card2 + card3 + card4 + 4
#' )
#' )
#' )
#' )
#'
#' BlackJack <-
#' setNode(BlackJack, pointsAfterCard5, "determ", define=fromFormula(),
#' nodeFormula = pointsAfterCard5 ~
#' ifelse(acesAfterCard5 == 5,
#' 15,
#' ifelse(acesAfterCard5 == 4,
#' ifelse(card1 + card2 + card3 + card4 + card5 + 5 > 51,
#' card1 + card2 + card3 + card4 + card5 + 5 - 40,
#' card1 + card2 + card3 + card4 + card5 + 5 - 30
#' ),
#' ifelse(acesAfterCard5 == 3,
#' ifelse(card1 + card2 + card3 + card4 + card5 + 5 > 51,
#' card1 + card2 + card3 + card4 + card5 + 5 - 30,
#' card1 + card2 + card3 + card4 + card5 + 5 - 20
#' ),
#' ifelse(acesAfterCard5 == 2,
#' ifelse(card1 + card2 + card3 + card4 + card5 + 5 > 31,
#' card1 + card2 + card3 + card4 + card5 + 5 - 20,
#' card1 + card2 + card3 + card4 + card5 + 5 - 10
#' ),
#' ifelse(acesAfterCard5 > 0,
#' ifelse(card1 + card2 + card3 + card4 + card5 + 5 > 22,
#' card1 + card2 + card3 + card4 + card5 + 5 - 10,
#' card1 + card2 + card3 + card4 + card5 + 5
#' ),
#' card1 + card2 + card3 + card4 + card5 + 5
#' )
#' )
#' )
#' )
#' )
#' )
#'
#' BlackJack <- setNode(BlackJack, playerFinalPoints, "determ", define=fromFormula(),
#' nodeFormula = playerFinalPoints ~
#' ifelse(hit1 == 0,
#' initialPoints,
#' ifelse(hit2 == 0,
#' pointsAfterCard3,
#' ifelse(hit3 == 0, pointsAfterCard4, pointsAfterCard5)
#' )
#' )
#' )
#'
#' BlackJack <- setDecisionNodes(BlackJack, hit1, hit2, hit3)
#' BlackJack <- setUtilityNodes(BlackJack, payoff)
#' }
#'
"BlackJack"
#' Black Jack Network Training Dataset
#'
#' These are simulated data on 1,000 Black Jack hands.
#'
#' @format A data frame with 10000 rows and 7 variables:
#' \describe{
#' \item{dealerUpcard}{The card in the dealer's hand visible to all players}
#' \item{card1}{Value of the first card}
#' \item{card2}{Value of the second card}
#' \item{initialPoints}{Total points with the two cards}
#' \item{hit1}{Binary variable indicating if a hit was taken}
#' \item{card3}{Value of the third card}
#' \item{pointsAfterCard3}{Total points with three cards}
#' \item{hit2}{Binary variable indicating if a hit was taken}
#' \item{card4}{Value of the fourth card}
#' \item{pointsAfterCard4}{Total points with four cards}
#' \item{hit3}{Binary variable indicating if a hit was taken}
#' \item{card5}{Value of the fifth card}
#' \item{pointsAfterCard5}{Total points with five cards}
#' }
#' @source
#' Bicycle Cards, "Blackjack,"
#' Retrieved from http://www.bicyclecards.com/card-games/rule/blackjack
"BlackJackTrain"
#' JAGS Probability Distributions.
#'
#' A dataset listing the JAGS probability distributions and their parameters
#'
#' @format A data frame with 30 rows and 7 variables:
#' \describe{
#' \item{DistName}{Distribution Name}
#' \item{FnName}{JAGS Function Name}
#' \item{FnNameR}{R Function Name}
#' \item{xLow}{Minimum value for x, the random variable}
#' \item{xHigh}{Maximum value for x, the random variable}
#' \item{Parameters}{Names of the JAGS parameters}
#' \item{RParameter}{R function argument name}
#' \item{paramLimit}{Limits on the parameter}
#' \item{paramLogic}{The text of a logical check used in \code{setNode} to
#' ensure stated parameters are valid.}
#' \item{Rsupport}{Logical value, indicating if an R equivalent is
#' supported by \code{HydeNet}}
#' }
#' @source \url{http://people.stat.sc.edu/hansont/stat740/jags_user_manual.pdf}
"jagsDists"
#' JAGS Functions Compatible with R.
#'
#' A dataset listing the JAGS functions and their R equivalents.
#'
#' @format A data frame with 30 rows and 3 variables:
#' \describe{
#' \item{jags_function}{JAGS function name}
#' \item{r_function}{R function Name}
#' \item{r_package}{R package where the function is found.}
#' }
#' @source \url{http://people.stat.sc.edu/hansont/stat740/jags_user_manual.pdf}
"jagsFunctions"
#' Pulmonary Embolism Dataset
#'
#' These are simulated data on 10,000 cases with suspected pulmonary embolism at a hospital.
#'
#' @format A data frame with 10000 rows and 7 variables:
#' \describe{
#' \item{wells}{Wells score (integer ranging from 1 to 10 indicating the degree to which PE is suspected based on clinical review of symptoms)}
#' \item{pregnant}{Factor indicating pregnancy (No, Yes)}
#' \item{pe}{Factor indicating pulmonary embolism has occurred (No,Yes)}
#' \item{angio}{Result of pulmonary angiography test (Negative, Positive)}
#' \item{d.dimer}{Numeric result of diagnostic blood test called D-Dimer.}
#' \item{treat}{Factor indicating whether or not treatment for PE was administered (No,Yes)}
#' \item{death}{Factor indicating patient mortality (No,Yes)}
#' }
#' @source Simulated data - not from real patients.
"PE"
#' Example Conditional Probability Table Resulting from the \code{inputCPT} function.
#'
#' This is an example of the output generated by the \code{inputCPT} function as
#' illustrated in the article being submitted to JSS. It is saved as an object
#' named \code{h} in the article.
#'
#' @source No Source. It's really just made up.
"inputCPTExample"
#' Conditional Probability Table for side effects as a function of emesis and drug.
#'
#' This is a conditional probability table used in the emesis example of the JSS article.
#'
"SE.cpt"
#' Conditional Probability Table for resolution of side effects as a function drugs and emesis.
#'
#' This is a conditional probability table used in the emesis example of the JSS article.
#'
"Resolution.cpt"
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