Package index • lingamr

Direct LiNGAM

Core causal discovery on i.i.d. data

lingam_direct(): Direct LiNGAM
lingam_high_dim(): High-Dimensional Direct LiNGAM
make_prior_knowledge(): Create a prior knowledge matrix
summary_lingam(): Summarize the goodness-of-fit of a Direct LiNGAM model at once
estimate_total_effect(): Estimate the total causal effect between two specified variables
estimate_all_total_effects(): Estimate the total causal effects between all variables at once
get_error_independence_p_values(): Compute p-values for the independence test of the errors
get_causal_order_stability(): Evaluate the stability of the causal order from bootstrap

lingam_direct_bootstrap(): Bootstrap for Direct LiNGAM
get_probabilities(): Get bootstrap probabilities
get_causal_direction_counts(): Get counts, proportions, and causal effects of causal directions
get_directed_acyclic_graph_counts(): Get DAG counts
get_adjacency_matrix_summary(): Create an adjacency matrix of representative causal-effect values from bootstrap results
get_total_causal_effects(): Get a list of total causal effects
get_paths(): Get all paths between two specified variables and their bootstrap probabilities
plot_bootstrap_probabilities(): Draw bootstrap probabilities with DiagrammeR

lingam_var(): VAR-LiNGAM for time series causal discovery
lingam_var_bootstrap(): Bootstrap for VAR-LiNGAM
get_var_probabilities(): Bootstrap probabilities for a VAR-LiNGAM model
get_var_paths(): Enumerate bootstrap paths between two variables in a VAR-LiNGAM model
estimate_var_total_effect(): Estimate a total causal effect in a VAR-LiNGAM model
check_var_stationarity(): Check the stationarity of a fitted VAR-LiNGAM model
test_varlingam_residual_normality(): Test the non-Gaussianity of VAR-LiNGAM residuals
test_varlingam_residual_normality_all(): Run several normality tests on VAR-LiNGAM residuals at once
plot_varlingam_residual_qq(): Q-Q plots of VAR-LiNGAM residuals

Joint estimation across multiple datasets sharing a causal order

lingam_multi_group(): Multi-Group Direct LiNGAM
lingam_multi_group_bootstrap(): Bootstrap for Multi-Group Direct LiNGAM
get_group_result(): Extract a single group's result from a MultiGroupLingamResult

Causal discovery robust against latent confounders

lingam_parce(): Bottom-Up ParceLiNGAM
lingam_parce_bootstrap(): Bootstrap for Bottom-Up ParceLiNGAM
estimate_total_effect_parce(): Estimate the total causal effect between two variables (ParceLiNGAM)
get_error_independence_p_values_parce(): Compute p-values for the independence of ParceLiNGAM residuals (HSIC-based)

Causal discovery with latent confounder detection

lingam_rcd(): RCD (Repetitive Causal Discovery)
lingam_rcd_bootstrap(): Bootstrap for RCD
estimate_total_effect_rcd(): Estimate the total causal effect between two variables (RCD)
get_error_independence_p_values_rcd(): Compute p-values for the independence of RCD residuals (HSIC-based)

bootstrap_with_imputation(): Bootstrap with Multiple Imputation for Direct LiNGAM
as_bootstrap_result(): Collapse an ImputationBootstrapResult into a BootstrapResult

evaluate_model_fit(): Evaluate model fit of an estimated causal graph
test_residual_normality(): Test normality of residuals from Direct LiNGAM
plot_residual_qq(): plot QQ
plot_adjacency(): Plot a causal graph from an adjacency matrix with DiagrammeR

generate_lim_sample(): Generate sample data for LiM (3 mixed variables)
generate_lingam_hard_sample(): Generate a challenging sample data for Direct LiNGAM
generate_lingam_large_sample(): Generate large-scale sample data to benchmark Direct LiNGAM scalability
generate_lingam_paradox_data(): Generate Paradoxical Data Where DirectLiNGAM Struggles
generate_lingam_sample_10(): Generate 10-variable sample data for Direct LiNGAM
generate_lingam_sample_6(): Generate sample data for Direct LiNGAM (6 variables)
generate_multi_group_sample(): Generate sample data for Multi-Group Direct LiNGAM (2 groups, 6 variables)
generate_parce_sample(): Generate sample data with a latent confounder (for BottomUpParceLiNGAM)
generate_rcd_sample(): Generate sample data with a latent confounder (for RCD)
generate_varlingam_sample(): Generate sample data from a VAR-LiNGAM model

tidy(<BootstrapResult>): Convert a BootstrapResult to a tidy data.frame
tidy(<ImputationBootstrapResult>): Convert an ImputationBootstrapResult to a tidy data.frame
tidy(<LiMResult>): Convert a LiMResult to a tidy data.frame
tidy(<LingamResult>): Convert a LingamResult to a tidy data.frame
tidy(<MultiGroupBootstrapResult>): Convert a MultiGroupBootstrapResult to a tidy data.frame
tidy(<MultiGroupLingamResult>): Convert a MultiGroupLingamResult to a tidy data.frame
tidy(<ParceLingamResult>): Convert a ParceLingamResult to a tidy data.frame
tidy(<RCDResult>): Convert an RCDResult to a tidy data.frame
glance(<LiMResult>): Get a one-row summary of a LiMResult
glance(<LingamResult>): Get a one-row summary of a LingamResult
glance(<MultiGroupLingamResult>): Get a one-row summary of a MultiGroupLingamResult
glance(<ParceLingamResult>): Get a one-row summary of a ParceLingamResult
glance(<RCDResult>): Get a one-row summary of an RCDResult
autoplot(<LiMResult>): Plot the causal graph of a LiMResult with ggplot2
autoplot(<LingamResult>): Plot the causal graph of a LingamResult with ggplot2
autoplot(<MultiGroupLingamResult>): Plot one group of a MultiGroupLingamResult with ggplot2
autoplot(<ParceLingamResult>): Plot the causal graph of a ParceLingamResult with ggplot2
autoplot(<RCDResult>): Plot the causal graph of an RCDResult with ggplot2

print(<BootstrapResult>): Display the contents of a BootstrapResult
print(<ImputationBootstrapResult>): Print method for ImputationBootstrapResult
print(<LiMResult>): Print method for LiMResult
print(<LingamResult>): Print method for LingamResult
print(<MultiGroupBootstrapResult>): Print method for MultiGroupBootstrapResult
print(<MultiGroupLingamResult>): Print method for MultiGroupLingamResult
print(<ParceLingamResult>): Print method for ParceLingamResult
print(<RCDResult>): Print method for RCDResult
print(<VARBootstrapResult>): Print a VARBootstrapResult
print(<VARLiNGAMResult>): Print method for VARLiNGAMResult
print(<causal_order_stability>): print method for causal_order_stability
print(<lingam_normality_test>): Print method for lingam_normality_test
print(<lingam_summary>): print method for lingam_summary
print(<var_stationarity>): Print method for var_stationarity