Make, Update, and Query Binary Causal Models


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Documentation for package ‘CausalQueries’ version 1.1.1

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CausalQueries-package 'CausalQueries'
collapse_data Make compact data with data strategies
complements Make statement for complements
data_type_names Data type names
decreasing Make monotonicity statement (negative)
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 Expand compact data object to data frame
expand_wildcard Expand wildcard
find_rounding_threshold helper to find rounding thresholds for print methods
get_all_data_types Get all data types
get_ambiguities_matrix Get ambiguities matrix
get_event_probabilities Draw event probabilities
get_parameters Setting parameters
get_parameter_names Get parameter names
get_parents Get list of parents of all nodes in a model
get_parmap Get parmap: a matrix mapping from parameters to data types
get_priors Setting priors
get_query_types Look up query types
get_type_prob Get type probabilities
get_type_prob_c generates one draw from type probability distribution for each type in P
get_type_prob_multiple_c generates n draws from type probability distribution for each type in P
grab Grab
increasing Make monotonicity statement (positive)
institutions_data Institutions and growth: Data for replication of analysis in *Integrated Inferences*
interacts Make statement for any interaction
interpret_type Interpret or find position in nodal type
lipids_data Lipids: Data for Chickering and Pearl replication
make_data Make data
make_events Make data in compact form
make_model Make a model
make_parameters Setting parameters
make_parameter_matrix Make parameter matrix
make_parmap Make parmap: a matrix mapping from parameters to data types
make_priors Setting priors
make_prior_distribution Make a prior distribution from priors
non_decreasing Make monotonicity statement (non negative)
non_increasing Make monotonicity statement (non positive)
observe_data Observe data, given a strategy
parameter_setting Setting parameters
print.causal_model Print a short summary for a causal model
print.causal_types Print a short summary for causal_model causal-types
print.dag Print a short summary for a causal_model DAG
print.event_probabilities Print a short summary for event probabilities
print.model_query Print a tightened summary of model queries
print.nodal_types Print a short summary for causal_model nodal-types
print.nodes Print a short summary for causal_model nodes
print.parameters Print a short summary for causal_model parameters
print.parameters_df Print a short summary for a causal_model parameters data-frame
print.parameters_posterior Print a short summary for causal_model parameter posterior distributions
print.parameters_prior Print a short summary for causal_model parameter prior distributions
print.parameter_mapping Print a short summary for paramater mapping matrix
print.parents_df Print a short summary for a causal_model parents data-frame
print.posterior_event_probabilities Print a short summary of posterior_event_probabilities
print.stan_summary Print a short summary for stan fit
print.statement Print a short summary for a causal_model statement
print.summary.causal_model Summarizing causal models
print.type_distribution Print a short summary for causal-type posterior distributions
print.type_prior Print a short summary for causal-type prior distributions
prior_setting Setting priors
query_distribution Calculate query distribution
query_model Generate estimands dataframe
realise_outcomes Realise outcomes
set_ambiguities_matrix Set ambiguity matrix
set_confound Set confound
set_parameters Setting parameters
set_parameter_matrix Set parameter matrix
set_parmap Set parmap: a matrix mapping from parameters to data types
set_priors Setting priors
set_prior_distribution Add prior distribution draws
set_restrictions Restrict a model
simulate_data simulate_data is an alias for make_data
substitutes Make statement for substitutes
summarise_distribution helper to compute mean and sd of a distribution data.frame
summary.causal_model Summarizing causal models
te Make treatment effect statement (positive)
update_model Fit causal model using 'stan'