Direct LINGAM (gCastle)

Module name	gcastle_direct_lingam
Package	gCastle
Version	1.0.3
Language	Python
Docs	here
Paper	Shimizu et al.[1]
Graph type	DAG
MCMC	No
Edge constraints	No
Data type	C
Data missingness
Intervention type
Docker	bpimages/gcastle:1.0.3

Direct Learning Algorithm for Linear Non-Gaussian Acyclic Model

A direct learning algorithm for linear non-Gaussian acyclic model (LiNGAM).

Example

Config file: gcastle.json

Command:

snakemake --cores all --use-apptainer --configfile config/gcastle.json

Fig. 50 shows the pattern graph’s FP/P vs. TP/P benchmark results for 12 gCastle algorithms (and comparison with BOSS (TETRAD) and Iterative MCMC (BiDAG)). The benchmark is based on 5 datasets corresponding to 5 realisations of a 20-variable random Gaussian SEM with Erdős-Rényi structure (expected degree 4, max parents 5). Each dataset contains 300 standardized samples. The SEM parameters are uniformly sampled from [0.25, 1].

Fig. 30 FP/P vs. TP/P for gCastle algorithms.

Example JSON

[
  {
    "id": "gcastle_direct_lingam",
    "measure": "pwling",
    "thresh": 0.3,
    "timeout": null
  }
]