Make, Update, and Query Binary Causal Models


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Documentation for package ‘CausalQueries’ version 1.3.0

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CausalQueries-package 'CausalQueries'
CausalQueries 'CausalQueries'
collapse_data Data helpers
complements Query helpers
data_helpers Data helpers
decreasing Query helpers
democracy_data Development and Democratization: Data for replication of analysis in *Integrated Inferences*
draw_causal_type Draw a single causal type given a parameter vector
expand_data Data helpers
get_all_data_types Get all data types
get_event_probabilities Draw event probabilities
get_parameters Setting parameters
get_priors Setting priors
get_query_types Look up query types
grab Helpers for inspecting causal models
increasing Query helpers
inspect Helpers for inspecting causal models
inspection Helpers for inspecting causal models
institutions_data Institutions and growth: Data for replication of analysis in *Integrated Inferences*
interacts Query helpers
interpret_type Interpret or find position in nodal type
lipids_data Lipids: Data for Chickering and Pearl replication
make_data Data helpers
make_events Data helpers
make_model Make a model
make_parameters Setting parameters
make_priors Setting priors
non_decreasing Query helpers
non_increasing Query helpers
parameter_setting Setting parameters
print.causal_model Print a short summary for a causal model
print.model_query Print a tightened summary of model queries
print.summary.causal_model Summarizing causal models
print.summary.model_query Summarizing model queries
prior_setting Setting priors
query_distribution Calculate query distribution
query_helpers Query helpers
query_model Generate data frame for batches of causal queries
realise_outcomes Realise outcomes
set_confound Set confound
set_parameters Setting parameters
set_priors Setting priors
set_prior_distribution Add prior distribution draws
set_restrictions Restrict a model
substitutes Query helpers
summary.causal_model Summarizing causal models
summary.model_query Summarizing model queries
te Query helpers
update_model Fit causal model using 'stan'