structure_learning_algorithms/athomas_jtsamplers structure_learning_algorithms/bdgraph structure_learning_algorithms/bidag_itsearch structure_learning_algorithms/bidag_order_mcmc structure_learning_algorithms/bidag_partition_mcmc structure_learning_algorithms/bnlearn_fastiamb structure_learning_algorithms/bnlearn_gs structure_learning_algorithms/bnlearn_h2pc structure_learning_algorithms/bnlearn_hc structure_learning_algorithms/bnlearn_hpc structure_learning_algorithms/bnlearn_iamb structure_learning_algorithms/bnlearn_iambfdr structure_learning_algorithms/bnlearn_interiamb structure_learning_algorithms/bnlearn_mmhc structure_learning_algorithms/bnlearn_mmpc structure_learning_algorithms/bnlearn_pcstable structure_learning_algorithms/bnlearn_rsmax2 structure_learning_algorithms/bnlearn_sihitonpc structure_learning_algorithms/bnlearn_tabu structure_learning_algorithms/causaldag_gsp structure_learning_algorithms/causallearn_ges structure_learning_algorithms/causallearn_grasp structure_learning_algorithms/corr_thresh structure_learning_algorithms/dualpc structure_learning_algorithms/equsa_psilearner structure_learning_algorithms/gcastle_anm structure_learning_algorithms/gcastle_corl structure_learning_algorithms/gcastle_direct_lingam structure_learning_algorithms/gcastle_gae structure_learning_algorithms/gcastle_golem structure_learning_algorithms/gcastle_grandag structure_learning_algorithms/gcastle_ica_lingam structure_learning_algorithms/gcastle_mcsl structure_learning_algorithms/gcastle_notears structure_learning_algorithms/gcastle_notears_low_rank structure_learning_algorithms/gcastle_notears_nonlinear structure_learning_algorithms/gcastle_pc structure_learning_algorithms/gcastle_rl structure_learning_algorithms/gobnilp structure_learning_algorithms/grues structure_learning_algorithms/huge_glasso structure_learning_algorithms/huge_mb structure_learning_algorithms/huge_tiger structure_learning_algorithms/paralleldg structure_learning_algorithms/pcalg_gies structure_learning_algorithms/pcalg_pc structure_learning_algorithms/prec_thresh structure_learning_algorithms/rblip_asobs structure_learning_algorithms/sklearn_glasso structure_learning_algorithms/tetrad_boss structure_learning_algorithms/tetrad_fas structure_learning_algorithms/tetrad_fask structure_learning_algorithms/tetrad_fges structure_learning_algorithms/tetrad_fofc structure_learning_algorithms/tetrad_ftfc structure_learning_algorithms/tetrad_grasp structure_learning_algorithms/tetrad_ica-lingam structure_learning_algorithms/tetrad_pc structure_learning_algorithms/trilearn_pgibbs
Apart from the original parameters of the underlying software, each algorithm module is equipped with an additional parameter, timeout
, which is the maximum time in seconds allowed for the algorithm to run. After the timeout, the algorithm will be terminated and either an empty file will be created or the current best graph will be saved (if the algorithm supports that).
Modules for MCMC algorithms can be used seamlessly with the other modules. However, apart from the original parameters and timeout
, these modules have four additional fields:
mcmc_seed
is the random seed for the algorithm.mcmc_estimator
specifies which estimator to use (threshold or map).threshold
specifies the threshold for the posterior edge probabilities ifmcmc_estimator
is set to threshold.burnin_frac
is a value in (0, 1) that specifies the fraction of the samples at the beginning of the graph trajectory to be discarded as burn-in.
The available modules are listed below. To add new modules, see new_modules
.
Algorithm | Graph | Package | Module |
---|---|---|---|
Chordal graph samplers | DG | Alun Thomas | athomas_jtsamplers |
BDgraph | UG | BDgraph | bdgraph |
Iterative MCMC | DAG, CPDAG | BiDAG | bidag_itsearch |
Order MCMC | DAG, CPDAG | BiDAG | bidag_order_mcmc |
Partition MCMC | DAG, CPDAG | BiDAG | bidag_partition_mcmc |
Fast IAMB | DAG | bnlearn | bnlearn_fastiamb |
Grow-shrink | DAG | bnlearn | bnlearn_gs |
H2PC | DAG | bnlearn | bnlearn_h2pc |
HC | DAG | bnlearn | bnlearn_hc |
HPC | DAG | bnlearn | bnlearn_hpc |
IAMB | DAG | bnlearn | bnlearn_iamb |
IAMB-FDR | DAG | bnlearn | bnlearn_iambfdr |
INTER-IAMB | DAG | bnlearn | bnlearn_interiamb |
MMHC | DAG | bnlearn | bnlearn_mmhc |
MMPC | DAG | bnlearn | bnlearn_mmpc |
PC | DAG | bnlearn | bnlearn_pcstable |
RSMAX2 | DAG | bnlearn | bnlearn_rsmax2 |
S-I HITON-PC | DAG | bnlearn | bnlearn_sihitonpc |
Tabu | DAG | bnlearn | bnlearn_tabu |
GSP | DAG | CausalDAG | causaldag_gsp |
GRaSP | CPDAG | causal-learn | causallearn_grasp |
Corrmat thresh | UG | Benchpress | corr_thresh |
Dual PC | CG, CPDAG | dualPC | dualpc |
Psi-learning | UG | equSA | equsa_psilearner |
ANM | DAG | gCastle | gcastle_anm |
CORL | DAG | gCastle | gcastle_corl |
Direct LINGAM | DAG | gCastle | gcastle_direct_lingam |
GAE | DAG | gCastle | gcastle_gae |
GOLEM | DAG | gCastle | gcastle_golem |
GraNDAG | DAG | gCastle | gcastle_grandag |
ICALiNGAM | DAG | gCastle | gcastle_ica_lingam |
MCSL | DAG | gCastle | gcastle_mcsl |
NO TEARS | DAG | gCastle | gcastle_notears |
NO TEARS low rank | DAG | gCastle | gcastle_notears_low_rank |
NO TEARS non-linear | DAG | gCastle | gcastle_notears_nonlinear |
PC | DAG | gCastle | gcastle_pc |
RL | DAG | gCastle | gcastle_rl |
GOBNILP | DAG | GOBNILP (BitBucket) | gobnilp |
GrUES | UDG | gues | grues |
Graphical lasso | UG | huge | huge_glasso |
Meinshausen & Buhlmann cov est | UG | huge | huge_mb |
TIGER | UG | huge | huge_tiger |
Parallel DG | DG | parallelDG | paralleldg |
GIES | CPDAG | pcalg | pcalg_gies |
PC | CPDAG, CG | pcalg | pcalg_pc |
Precmat thresh | UG | Benchpress | prec_thresh |
ASOBS | DAG | r.blip | rblip_asobs |
Graphical Lasso | UG | scikit-learn | sklearn_glasso |
BOSS | CPDAG | causal-cmd | tetrad_boss |
FASK | DAG | causal-cmd | tetrad_fask |
FGES | CPDAG | causal-cmd | tetrad_fges |
FOFC | DAG | causal-cmd | tetrad_fofc |
FTFC | DAG | causal-cmd | tetrad_ftfc |
GRaSP | CPDAG | causal-cmd | tetrad_grasp |
ICA-LINGAM | DAG | causal-cmd | tetrad_ica-lingam |
PC | DAG | causal-cmd | tetrad_pc |
Particle Gibbs | DG | trilearn | trilearn_pgibbs |