Make, Update, and Query Binary Causal Models

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Documentation for package ‘CausalQueries’ version 1.0.1

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CausalQueries-package 'CausalQueries'
all_data_types All data types
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
get_ambiguities_matrix Get ambiguities matrix
get_causal_types Get causal types
get_event_prob Draw event probabilities
get_nodal_types Get list of types for nodes in a DAG
get_parameters Setting parameters
get_parameter_matrix Get parameter matrix
get_parameter_names Get parameter names
get_param_dist Get a distribution of model parameters
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_prior_distribution Get a prior distribution from 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 Draw matrix of type probabilities, before or after estimation
get_type_prob_multiple_c generates n draws from type probability distribution for each type in P
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
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
strategy_statements Generate strategy statements given data
substitutes Make statement for substitutes
te Make treatment effect statement (positive)
update_model Fit causal model using 'stan'