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' |