stagedtrees | R Documentation |
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 X1, X2, …, a staged event tree represents the factorization P(X1, X2, X3, …) = P(X1)P(X2 | X1) P(X3 | X1, X2) … . Additionally, the stages structure indicates which conditional probabilities are equal.
Model selection algorithms:
full model full
independence model indep
Hill-Climbing stages_hc
Backward Hill-Climbing stages_bhc
Fast Backward Hill-Climbing stages_fbhc
Backward Hill-Climbing Random stages_bhcr
Backward joining stages_bj
Hierarchical Clustering stages_hclust
K-Means Clustering stages_kmeans
Optimal order search search_best
Greedy order search search_greedy
Probabilities, log-likelihood and predictions:
Marginal/Conditional probabilities prob
Log-Likelihood logLik.sevt
Predict method predict.sevt
Confidence intervals confint.sevt
Plot, explore and compare:
Plot plot.sevt
Compare compare_stages
Stages inclusion inclusions_stages
Stages info summary.sevt
List of parents as_parentslist
Barplot construction barplot.sevt
Likelihood-ratio test lr_test
Context-specific interventional distance cid
Modify models:
Join and isolate unobserved situations join_unobserved
Join two stages join_stages
Rename a stage rename_stage
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.
data("PhDArticles") mf <- full(PhDArticles, join_unobserved = TRUE) mod <- stages_fbhc(mf) plot(mod)
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