# Make, Update, and Query Binary Causal Models

## Documentation for package ‘CausalQueries’ version 0.1.0

- DESCRIPTION file.

## Help Pages

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_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_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_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'