Package: gRain 1.4.5
gRain: Bayesian Networks
Probability propagation in Bayesian networks, also known as graphical independence networks. Documentation of the package is provided in vignettes included in the package and in the paper by Højsgaard (2012, <doi:10.18637/jss.v046.i10>). See 'citation("gRain")' for details.
Authors:
gRain_1.4.5.tar.gz
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gRain.pdf |gRain.html✨
gRain/json (API)
NEWS
# Install 'gRain' in R: |
install.packages('gRain', repos = c('https://hojsgaard.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/hojsgaard/grain/issues
Last updated 20 days agofrom:65f181eaa7. Checks:OK: 6 NOTE: 3. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 17 2024 |
R-4.5-win-x86_64 | OK | Oct 17 2024 |
R-4.5-linux-x86_64 | OK | Oct 17 2024 |
R-4.4-win-x86_64 | OK | Oct 17 2024 |
R-4.4-mac-x86_64 | OK | Oct 17 2024 |
R-4.4-mac-aarch64 | OK | Oct 17 2024 |
R-4.3-win-x86_64 | NOTE | Oct 17 2024 |
R-4.3-mac-x86_64 | NOTE | Oct 17 2024 |
R-4.3-mac-aarch64 | NOTE | Oct 17 2024 |
Exports:absorbEvidenceandtabandtablebooltabcompile_cptcompile_potcompileCPTcompilePOTcptcptableevidence_addevidence_dropevidence_getevidence_probextract_cptextract_margextract_potextractCPTextractMARGextractPOTgetEvidencegetFindinggetgingetgraingraingrain.CPTspecisCompiledisPropagatedloadHuginNetmarg2potmendelnodeNamesnodeStatesortabortableparse_cptpEvidencepFindingpot2margprintlistpropagateLSqgrainquerygrainrepeat_patternrepeatPatternreplace_cptreplaceCPTretractEvidenceretractFindingsaveHuginNetsetEvidencesetFindingsimplify_querysimulate.grainsplitVecuniverse
Dependencies:backportsbroomclicpp11dplyrfansigenericsgluegRbaseigraphlatticelifecyclemagrittrMatrixpillarpkgconfigpurrrR6RcppRcppArmadilloRcppEigenrlangstringistringrtibbletidyrtidyselectutf8vctrswithr
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Compile conditional probability tables / cliques potentials. | compileCPT compilePOT |
Extract conditional probabilities and clique potentials from data. | components_extract extract_cpt extract_marg extract_pot marg2pot pot2marg |
Compile conditional probability tables / cliques potentials. | compile_cpt compile_pot components_gather parse_cpt parse_cpt, parse_cpt.default parse_cpt.xtabs, |
Create conditional probability tables (CPTs) | cpt cptable |
Chest clinic example | example_chest example_chest_cpt |
Wet grass example | example_grass example_grass_cpt |
Set, retrieve, and retract finding in Bayesian network. | finding getFinding pFinding retractFinding setFinding |
gRain generics | generics isCompiled isPropagated nodeNames nodeNames.grain nodeStates nodeStates.grain rip.grain universe universe.grain vpar.cpt_grain vpar.cpt_spec |
Compile Bayesian network. | compile.cpt_grain compile.grain compile.pot_grain grain_compile |
Set, update and remove evidence. | evidence_add evidence_drop evidence_get evidence_prob grain_evidence |
Make predictions from Bayesian network | grain_predict predict.grain |
Propagate in a Bayesian network | grain_propagate propagate.grain propagateLS propagateLS__ |
Create Bayesian network | grain grain-main grain.CPTspec grain.cpt_spec grain.dModel grain.igraph grain.pot_spec |
Simulate from Bayesian network | grain-simulate simulate.grain |
Load and save Hugin net files | load-save-hugin loadHuginNet saveHuginNet |
Conditional probability tables based on logical dependencies | andtab andtable booltab logical ortab ortable |
Mendelian segregation | mendel |
Extract conditional probabilities and clique potentials from data. | extractCPT extractMARG extractPOT old_components_extract |
Set, update and remove evidence. | absorbEvidence getEvidence old_grain_evidence pEvidence retractEvidence setEvidence |
Replace CPTs in Bayesian network | old_replace_cpt replaceCPT |
Query a Bayesian network | ask ask.grain qgrain querygrain querygrain.grain |
Create repeated patterns in Bayesian networks | repeat_pattern |
Create repeated patterns in Bayesian networks | repeatPattern |
Replace CPTs in Bayesian network | replace_cpt replace_cpt.cpt_grain |
Simplify output query to a Bayesian network | simplify_query |