README.md

In gkikuchi/rlingam: R Implementation of LiNGAM algorithms

rlingam: R implementation of LiNGAM algorithms

Implemented Algorithms

• ICALiNGAM
• DirectLiNGAM

Install

remotes::install_github("gkikuchi/rlingam")

Usage

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

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

gkikuchi/rlingam documentation built on Jan. 7, 2022, 11:10 p.m.