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          Democracy Data
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_param_dist          Get a distribution of model parameters
get_parameter_matrix    Get parameter matrix
get_parameter_names     Get parameter names
get_parameters          Get parameters
get_parents             Get list of parents of all nodes in a model
get_prior_distribution
                        Get a prior distribution from priors
get_priors              Get priors
get_query_types         Look up query types
get_type_prob           Get type probabilities
get_type_prob_multiple
                        Draw matrix of type probabilities, before or
                        after estimation
increasing              Make monotonicity statement (positive)
interacts               Make statement for any interaction
interpret_type          Interpret or find position in nodal type
make_confounds_df       Make a confounds dataframe
make_data               Make data
make_events             Make data in compact form
make_model              Make a model
make_parameter_matrix   Make parameter matrix
make_parameters         Make a 'true' parameter vector
make_prior_distribution
                        Make a prior distribution from priors
make_priors             Make Priors
make_values_task_list   Make values task list
non_decreasing          Make monotonicity statement (non negative)
non_increasing          Make monotonicity statement (non positive)
observe_data            Observe data, given a strategy
query_distribution      Calculate query distribution
query_model             Generate estimands dataframe
reveal_outcomes         Reveal outcomes
set_ambiguities_matrix
                        Set ambiguity matrix
set_confound            Set confound
set_confounds           Set confounds
set_confounds_df        Set a confounds_df
set_parameter_matrix    Set parameter matrix
set_parameters          Set parameters
set_prior_distribution
                        Add prior distribution draws
set_priors              Set prior distribution
set_restrictions        Restrict a model
simulate_data           simulate_data is an alias for make_data
substitutes             Make statement for substitutes
te                      Make treatment effect statement (positive)
update_model            Fit causal model using 'stan'
