BDgraph-package | Bayesian Structure Learning in Graphical Models |
adj2link | Extract links from an adjacency matrix |
auc | Compute the area under the ROC curve |
BDgraph | Bayesian Structure Learning in Graphical Models |
bdgraph | Search algorithm in graphical models |
bdgraph.dw | Search algorithm for Gaussian copula graphical models for count data |
bdgraph.mpl | Search algorithm in graphical models using marginal pseudo-likehlihood |
bdgraph.npn | Nonparametric transfer |
bdgraph.sim | Graph data simulation |
bdw.reg | Bayesian estimation of (zero-inflated) Discrete Weibull regression |
bf | Bayes factor for two graphs |
calc_joint_dist | Bayesian Structure Learning in Graphical Models |
compare | Graph structure comparison |
compute_measures | Bayesian Structure Learning in Graphical Models |
compute_tp_fp | Bayesian Structure Learning in Graphical Models |
conf.mat | Confusion Matrix |
conf.mat.plot | Plot Confusion Matrix |
covariance | Estimated covariance matrix |
ddweibull | The Discrete Weibull Distribution (Type 1) |
ddweibull_reg | Bayesian Structure Learning in Graphical Models |
detect_cores | Bayesian Structure Learning in Graphical Models |
geneExpression | Human gene expression dataset |
generate_clique_factors | Bayesian Structure Learning in Graphical Models |
get_bounds_dw | Bayesian Structure Learning in Graphical Models |
get_cores | Bayesian Structure Learning in Graphical Models |
get_Ds_tgm_R | Bayesian Structure Learning in Graphical Models |
get_graph | Bayesian Structure Learning in Graphical Models |
get_g_prior | Bayesian Structure Learning in Graphical Models |
get_g_start | Bayesian Structure Learning in Graphical Models |
get_K_start | Bayesian Structure Learning in Graphical Models |
get_S_n_p | Bayesian Structure Learning in Graphical Models |
get_Ts_R | Bayesian Structure Learning in Graphical Models |
global_hc | Bayesian Structure Learning in Graphical Models |
global_hc_binary | Bayesian Structure Learning in Graphical Models |
gnorm | Normalizing constant for G-Wishart |
graph.sim | Graph simulation |
hill_climb_mpl | Bayesian Structure Learning in Graphical Models |
hill_climb_mpl_binary | Bayesian Structure Learning in Graphical Models |
link2adj | Extract links from an adjacency matrix |
local_mb_hc | Bayesian Structure Learning in Graphical Models |
local_mb_hc_binary | Bayesian Structure Learning in Graphical Models |
log_mpl_binary | Bayesian Structure Learning in Graphical Models |
log_mpl_disrete | Bayesian Structure Learning in Graphical Models |
log_post_cond_dw | Bayesian Structure Learning in Graphical Models |
near_positive_definite | Bayesian Structure Learning in Graphical Models |
pdweibull | The Discrete Weibull Distribution (Type 1) |
pgraph | Posterior probabilities of the graphs |
plinks | Estimated posterior link probabilities |
plot.bdgraph | Plot function for 'S3' class "'bdgraph'" |
plot.graph | Plot function for 'S3' class '"graph"' |
plot.sim | Plot function for 'S3' class "'sim'" |
plotcoda | Convergence plot |
plotroc | ROC plot |
posterior.predict | Posterior Predictive Samples |
precision | Estimated precision matrix |
predict.bdgraph | Predict function for 'S3' class "'bdgraph'" |
print.bdgraph | Print function for 'S3' class "'bdgraph'" |
print.sim | Print function for 'S3' class "'sim'" |
qdweibull | The Discrete Weibull Distribution (Type 1) |
rdweibull | The Discrete Weibull Distribution (Type 1) |
reinis | Risk factors of coronary heart disease |
rgwish | Sampling from G-Wishart distribution |
rmvnorm | Generate data from the multivariate Normal distribution |
roc | Build a ROC curve |
rwish | Sampling from Wishart distribution |
sample_ug | Bayesian Structure Learning in Graphical Models |
select | Graph selection |
sparsity | Compute the sparsity of a graph |
summary.bdgraph | Summary function for 'S3' class "'bdgraph'" |
surveyData | Labor force survey data |
traceplot | Trace plot of graph size |
transfer | transfer for count data |
update_mu_R | Bayesian Structure Learning in Graphical Models |
update_tu_R | Bayesian Structure Learning in Graphical Models |