R: Make, Update, and Query Binary Causal Models

# Make, Update, and Query Binary Causal Models

## Documentation for package ‘CausalQueries’ version 1.1.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

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_parameters | Setting parameters
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_priors | Setting 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_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_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
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.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.parameter_mapping | Print a short summary for paramater mapping matrix
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.summary.causal_model | Summarizing causal models
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_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
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'