Package: CausalQueries 1.2.1

Till Tietz

CausalQueries: Make, Update, and Query Binary Causal Models

Users can declare causal models over binary nodes, update beliefs about causal types given data, and calculate arbitrary queries. Updating is implemented in 'stan'. See Humphreys and Jacobs, 2023, Integrated Inferences (<doi:10.1017/9781316718636>) and Pearl, 2009 Causality (<doi:10.1017/CBO9780511803161>).

Authors:Clara Bicalho [ctb], Jasper Cooper [ctb], Macartan Humphreys [aut], Till Tietz [aut, cre], Alan Jacobs [aut], Merlin Heidemanns [ctb], Lily Medina [aut], Julio Solis [ctb], Georgiy Syunyaev [aut]

CausalQueries_1.2.1.tar.gz
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CausalQueries_1.2.1.tgz(r-4.4-x86_64)CausalQueries_1.2.1.tgz(r-4.4-arm64)CausalQueries_1.2.1.tgz(r-4.3-x86_64)CausalQueries_1.2.1.tgz(r-4.3-arm64)
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CausalQueries.pdf |CausalQueries.html✨
CausalQueries/json (API)
NEWS

# Install 'CausalQueries' in R:
install.packages('CausalQueries', repos = c('https://integrated-inferences.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/integrated-inferences/causalqueries/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • democracy_data - Development and Democratization: Data for replication of analysis in *Integrated Inferences*
  • institutions_data - Institutions and growth: Data for replication of analysis in *Integrated Inferences*
  • lipids_data - Lipids: Data for Chickering and Pearl replication

On CRAN:

bayescausaldagsmixedmethodsstan

8.77 score 24 stars 47 scripts 868 downloads 33 exports 81 dependencies

Last updated 14 hours agofrom:9aecabf57e. Checks:OK: 2 NOTE: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-win-x86_64NOTENov 20 2024
R-4.5-linux-x86_64OKNov 20 2024
R-4.4-win-x86_64NOTENov 20 2024
R-4.4-mac-x86_64NOTENov 20 2024
R-4.4-mac-aarch64NOTENov 20 2024
R-4.3-win-x86_64NOTENov 20 2024
R-4.3-mac-x86_64NOTENov 20 2024
R-4.3-mac-aarch64NOTENov 20 2024

Exports:collapse_datacomplementsdecreasingdraw_causal_typeexpand_dataget_all_data_typesget_event_probabilitiesget_query_typesgrabincreasinginspectinteractsinterpret_typemake_datamake_eventsmake_modelmake_parametersmake_priorsnon_decreasingnon_increasingplot_modelquery_distributionquery_modelrealise_outcomesset_confoundset_parameter_matrixset_parametersset_prior_distributionset_priorsset_restrictionssubstitutesteupdate_model

Dependencies:abindbackportsBHcachemcallrcheckmateclicolorspacecpp11descdirmultdistributionaldplyrevaluatefansifarverfastmapgenericsggforceggplot2ggraphggrepelgluegraphlayoutsgridExtragtablehighrigraphinlineisobandknitrlabelinglatex2explatticelifecycleloomagrittrMASSMatrixmatrixStatsmemoisemgcvmunsellnlmenumDerivpillarpkgbuildpkgconfigpolyclipposteriorprocessxpspurrrQuickJSRR6RColorBrewerRcppRcppArmadilloRcppEigenRcppParallelrlangrstanrstantoolsscalesStanHeadersstringistringrsystemfontstensorAtibbletidygraphtidyrtidyselecttweenrutf8vctrsviridisviridisLitewithrxfunyaml

Getting Started

Rendered froma-getting-started.Rmdusingknitr::rmarkdownon Nov 20 2024.

Last update: 2024-11-01
Started: 2024-09-26

Inspecting posteriors

Rendered fromd-posteriors.Rmdusingknitr::rmarkdownon Nov 20 2024.

Last update: 2024-09-30
Started: 2024-09-30

Plotting models

Rendered fromb-plotting.Rmdusingknitr::rmarkdownon Nov 20 2024.

Last update: 2024-09-30
Started: 2024-09-26

Through the front door

Rendered fromc-front-door.Rmdusingknitr::rmarkdownon Nov 20 2024.

Last update: 2024-09-30
Started: 2024-09-26

Readme and manuals

Help Manual

Help pageTopics
'CausalQueries'CausalQueries-package CausalQueries
Data helperscollapse_data data_helpers expand_data make_data make_events
Development and Democratization: Data for replication of analysis in *Integrated Inferences*democracy_data
Draw a single causal type given a parameter vectordraw_causal_type
Get all data typesget_all_data_types
Draw event probabilitiesget_event_probabilities
Look up query typesget_query_types
Helpers for inspecting causal modelsgrab inspect inspection
Institutions and growth: Data for replication of analysis in *Integrated Inferences*institutions_data
Interpret or find position in nodal typeinterpret_type
Lipids: Data for Chickering and Pearl replicationlipids_data
Make a modelmake_model
Setting parametersget_parameters make_parameters parameter_setting set_parameters
Print a short summary for a causal modelprint.causal_model
Print a tightened summary of model queriesprint.model_query
Setting priorsget_priors make_priors prior_setting set_priors
Calculate query distributionquery_distribution
Query helperscomplements decreasing increasing interacts non_decreasing non_increasing query_helpers substitutes te
Generate estimands data framequery_model
Realise outcomesrealise_outcomes
Set confoundset_confound
Add prior distribution drawsset_prior_distribution
Restrict a modelset_restrictions
Summarizing causal modelsprint.summary.causal_model summary.causal_model
Summarizing model queriesprint.summary.model_query summary.model_query
Fit causal model using 'stan'update_model