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_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_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_parameter_matrix   Make parameter matrix
make_parmap             Make parmap: a matrix mapping from parameters
                        to data types
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.parameter_mapping
                        Print a short summary for paramater mapping
                        matrix
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.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.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_parameter_matrix    Set parameter matrix
set_parmap              Set parmap: a matrix mapping from parameters to
                        data types
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'
