Package: bnmonitor 0.2.0

Manuele Leonelli

bnmonitor: An Implementation of Sensitivity Analysis in Bayesian Networks

An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. Reference: M. Leonelli, R. Ramanathan, R.L. Wilkerson (2022) <doi:10.1016/j.knosys.2023.110882>.

Authors:Manuele Leonelli [aut, cre], Ramsiya Ramanathan [aut], Rachel Wilkerson [aut]

bnmonitor_0.2.0.tar.gz
bnmonitor_0.2.0.zip(r-4.5)bnmonitor_0.2.0.zip(r-4.4)bnmonitor_0.2.0.zip(r-4.3)
bnmonitor_0.2.0.tgz(r-4.4-any)bnmonitor_0.2.0.tgz(r-4.3-any)
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bnmonitor_0.2.0.tgz(r-4.4-emscripten)bnmonitor_0.2.0.tgz(r-4.3-emscripten)
bnmonitor.pdf |bnmonitor.html✨
bnmonitor/json (API)

# Install 'bnmonitor' in R:

install.packages('bnmonitor', repos = c('https://manueleleonelli.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/manueleleonelli/bnmonitor/issues

Datasets:

cachexia_ci - Bayesian networks for a cachexia study
cachexia_data - Bayesian networks for a cachexia study
cachexia_gbn - Bayesian networks for a cachexia study
chds - Christchurch Health and Development Study
chds_bn - Christchurch Health and Development Study
chds_bn.fit - Christchurch Health and Development Study
control_ci - Bayesian networks for a cachexia study
control_gbn - Bayesian networks for a cachexia study
diabetes - Pima Indian Diabetes Data
fire_alarm - Bayesian network on fire alarm system
mathmarks - Math Marks Data
synthetic_bn - A synthetic Bayesian network
synthetic_ci - A synthetic continuous Bayesian network
synthetic_gbn - A synthetic continuous Bayesian network
travel - Bayesian network on travel survey

On CRAN:

34 exports 3 stars 1.20 score 97 dependencies 15 scripts 412 downloads

Last updated 18 hours agofrom:7d231e7bc9. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Sep 17 2024
R-4.5-win	OK	Sep 17 2024
R-4.5-linux	OK	Sep 17 2024
R-4.4-win	OK	Sep 17 2024
R-4.4-mac	OK	Sep 17 2024
R-4.3-win	OK	Jul 13 2024
R-4.3-mac	OK	Jul 13 2024

Exports:amalgamation asy_measure bn2ci bn2gbn CD col_covar_matrix covariance_var diameter dwi edge_strength ewi final_node_monitor Fro global_monitor influential_obs Jeffreys KL KL_bounds mean_var model_pres_cov mutual_info node_monitor orderp_covar partial_covar_matrix proportional_covar psd_check row_covar_matrix sensitivity sensquery seq_cond_monitor seq_marg_monitor seq_pa_ch_monitor total_covar_matrix uniform_covar

Dependencies:abind backports base64enc bnlearn broom bslib cachem checkmate cli cluster colorspace corpcor cpp11 data.table digest dplyr evaluate fansi farver fastmap fdrtool fontawesome foreign Formula fs generics ggplot2 glasso glue GPArotation gRain gRbase gridExtra gtable gtools highr Hmisc htmlTable htmltools htmlwidgets igraph isoband jpeg jquerylib jsonlite knitr labeling lattice lavaan lifecycle magrittr MASS Matrix memoise mgcv mime mnormt munsell nlme nnet numDeriv pbapply pbivnorm pillar pkgconfig plyr png psych purrr qgraph quadprog R6 rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen reshape2 rlang rmarkdown rpart rstudioapi sass scales stringi stringr tibble tidyr tidyselect tinytex utf8 vctrs viridis viridisLite withr xfun yaml

Help page	Topics
Amalgamation of levels	amalgamation
Measures of asymmetric independence	asy_measure
Integration with 'bn.fit' objects from 'bnlearn'	bn2 bn2ci bn2gbn
bnmonitor: A package for sensitivity analysis and robustness in Bayesian networks	bnmonitor
Bayesian networks for a cachexia study	cachexia cachexia_ci cachexia_data cachexia_gbn control_ci control_gbn
CD-distance	CD
Christchurch Health and Development Study	chds chds_bn chds_bn.fit
Standard variation of the covariance matrix	covariance_var
Co-variation schemes	covariation orderp_covar proportional_covar uniform_covar
Co-variation matrices	col_covar_matrix covariation_matrix partial_covar_matrix row_covar_matrix total_covar_matrix
Pima Indian Diabetes Data	diabetes
Diameters in a Bayesian network	diameter
Distance-weigthed influence	dwi
Strength of edges in a Bayesian network	edge_strength
Edge-weigthed influence	ewi
Final node monitors	final_node_monitor
Bayesian network on fire alarm system	fire_alarm
Frobenius norm	Fro
Frobenius norm for 'CI'	Fro.CI
Frobenius norm for 'GBN'	Fro.GBN
Global monitor	global_monitor
Influential observations	influential_obs
Jeffreys Divergence	Jeffreys
Jeffreys Divergence for 'CI'	Jeffreys.CI
Jeffreys Divergence for 'GBN'	Jeffreys.GBN
KL Divergence	KL
Bounds for the KL-divergence	KL_bounds
KL Divergence for 'bn.fit'	KL.bn.fit
KL Divergence for 'CI'	KL.CI
KL Divergence for 'GBN'	KL.GBN
Math Marks Data	mathmarks
Standard variation of the mean vector	mean_var
Model-Preserving co-variation	model_pres_cov
Mutual information	mutual_info
Node monitor	node_monitor
Plotting methods	plot plot.CD plot.diameter plot.dwi plot.edgestrength plot.ewi plot.final_node_monitor plot.fro plot.influential_obs plot.jeffreys plot.kl plot.mutualinfo plot.node_monitor plot.sensitivity plot.seq_cond_monitor plot.seq_marg_monitor plot.seq_pa_ch_monitor
Printing methods	print print.CD print.diameter print.dwi print.ewi print.final_node_monitor print.fro print.jeffreys print.kl print.mutualinfo print.node_monitor print.sensitivity print.seq_cond_monitor print.seq_marg_monitor print.seq_pa_ch_monitor
Check for positive semi-definiteness after a perturbation	psd_check psd_check.CI psd_check.GBN
Sensitivity function	sensitivity
Sensitivity of probability query	sensquery
Sequential node monitors	seq_cond_monitor seq_marg_monitor seq_node_monitor
Sequential parent-child node monitors	seq_pa_ch_monitor
A synthetic Bayesian network	synthetic_bn
A synthetic continuous Bayesian network	synthetic_cbn synthetic_ci synthetic_gbn
Bayesian network on travel survey	travel

Package: bnmonitor 0.2.0

bnmonitor: An Implementation of Sensitivity Analysis in Bayesian Networks

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