Sección 2 Redes bayesianas

Los paquetes que usaremos en esta sección son:

  • CRAN: tidyverse (dplyr, ggplot2, purrr) de Hadley Wickham (2017), bnlearn de Scutari and Ness (2019), igraph Csárdi (2019) y gRain de Højsgaard (2016).
install.packages(c("tidyverse", "bnlearn", "igraph, "gRain", "BiocManager"))
  • Bioconductor: Rgraphviz de Hansen et al. (2019), RBGL de Carey, Long, and Gentleman (2019):
install.packages("BiocManager")
BiocManager::install("Rgraphviz")

Y las referencias bibliográficas son Koller and Friedman (2009), Ross (1998) y Wasserman (2004).

Referencias

Carey, Vince, Li Long, and R. Gentleman. 2019. RBGL: An Interface to the Boost Graph Library. http://www.bioconductor.org.

Csárdi, Gábor. 2019. Igraph: Network Analysis and Visualization. https://CRAN.R-project.org/package=igraph.

Hansen, Kasper Daniel, Jeff Gentry, Li Long, Robert Gentleman, Seth Falcon, Florian Hahne, and Deepayan Sarkar. 2019. Rgraphviz: Provides Plotting Capabilities for R Graph Objects.

Højsgaard, Søren. 2016. GRain: Graphical Independence Networks. https://CRAN.R-project.org/package=gRain.

Koller, Daphne, and Nir Friedman. 2009. Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning. The MIT Press.

Ross, Sheldon M. 1998. A First Course in Probability. Fifth. Upper Saddle River, N.J.: Prentice Hall.

Scutari, Marco, and Robert Ness. 2019. Bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference. https://CRAN.R-project.org/package=bnlearn.

Wasserman, Larry. 2004. All of Statistics: A Concise Course in Statistical Inference. Springer Texts in Statistics. New York: Springer. https://doi.org/10.1007/978-0-387-21736-9.

Wickham, Hadley. 2017. Tidyverse: Easily Install and Load the ’Tidyverse’. https://CRAN.R-project.org/package=tidyverse.