- ICALiNGAM
- DirectLiNGAM
remotes::install_github("gkikuchi/rlingam")
library(rlingam)
X <- gen_dummy_data(random_state = 10)
# icalingam
mdl <- ICALiNGAM$new()
mdl$fit(X)
# directlingam
mdl <- DirectLiNGAM$new()
mdl$fit(X)
print(mdl$causal_order)
print(mdl$adjacency_matrix)
plot_adjacency_mat(mdl$adjacency_matrix, node_labels = names(X))
Parameters for *LiNGAM$new():
- random_state (integer)
- random seed
- lasso_engine ("glmnet" or "lars")
- library to use to estimate adjacency matrix. default="glmnet"
- max_iter (integer) only for ICALiNGAM
- maximum iterations for fastICA. default=1000