Make, Update, and Query Binary Causal Models


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Documentation for package ‘CausalQueries’ version 0.0.3

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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_parameters Get 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_priors Get 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_confounds_df Make a confounds dataframe
make_data Make data
make_events Make data in compact form
make_model Make a model
make_parameters Make a 'true' parameter vector
make_parameter_matrix Make parameter matrix
make_priors Make Priors
make_prior_distribution Make a prior distribution from 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_parameters Set parameters
set_parameter_matrix Set parameter matrix
set_priors Set prior distribution
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
te Make treatment effect statement (positive)
update_model Fit causal model using 'stan'