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 |