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
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_param_dist          Get a distribution of model parameters
get_parameter_matrix    Get parameter matrix
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_prior_distribution
                        Get a prior distribution from 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_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
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
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'
