Function reference
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CausalQueries-package - 'CausalQueries'
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collapse_data() - Make compact data with data strategies
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complements() - Make statement for complements
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data_type_names() - Data type names
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decreasing() - Make monotonicity statement (negative)
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democracy_data - Development and Democratization: Data for replication of analysis in *Integrated Inferences*
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draw_causal_type() - Draw a single causal type given a parameter vector
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expand_data() - Expand compact data object to data frame
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expand_wildcard() - Expand wildcard
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find_rounding_threshold() - helper to find rounding thresholds for print methods
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get_all_data_types() - Get all data types
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get_ambiguities_matrix() - Get ambiguities matrix
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get_event_probabilities() - Draw event probabilities
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get_parameter_names() - Get parameter names
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get_parents() - Get list of parents of all nodes in a model
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get_parmap() - Get parmap: a matrix mapping from parameters to data types
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get_query_types() - Look up query types
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get_type_prob() - Get type probabilities
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get_type_prob_c() - generates one draw from type probability distribution for each type in P
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get_type_prob_multiple_c() - generates n draws from type probability distribution for each type in P
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grab() - Grab
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increasing() - Make monotonicity statement (positive)
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institutions_data - Institutions and growth: Data for replication of analysis in *Integrated Inferences*
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interacts() - Make statement for any interaction
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interpret_type() - Interpret or find position in nodal type
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lipids_data - Lipids: Data for Chickering and Pearl replication
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make_data() - Make data
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make_events() - Make data in compact form
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make_model() - Make a model
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make_parameter_matrix() - Make parameter matrix
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make_parmap() - Make parmap: a matrix mapping from parameters to data types
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make_prior_distribution() - Make a prior distribution from priors
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non_decreasing() - Make monotonicity statement (non negative)
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non_increasing() - Make monotonicity statement (non positive)
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observe_data() - Observe data, given a strategy
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make_parameters()set_parameters()get_parameters() - Setting parameters
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print(<causal_model>) - Print a short summary for a causal model
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print(<causal_types>) - Print a short summary for causal_model causal-types
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print(<dag>) - Print a short summary for a causal_model DAG
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print(<event_probabilities>) - Print a short summary for event probabilities
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print(<model_query>) - Print a tightened summary of model queries
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print(<nodal_types>) - Print a short summary for causal_model nodal-types
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print(<nodes>) - Print a short summary for a causal_model nodes
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print(<parameters>) - Print a short summary for causal_model parameters
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print(<parameters_df>) - Print a short summary for a causal_model parameters data-frame
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print(<parameters_posterior>) - Print a short summary for causal_model parameter posterior distributions
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print(<parameters_prior>) - Print a short summary for causal_model parameter prior distributions
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print(<parents_df>) - Print a short summary for a causal_model parents data-frame
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print(<posterior_event_probabilities>) - Print a short summary of posterior_event_probabilities
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print(<stan_summary>) - Print a short summary for stan fit
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print(<statement>) - Print a short summary for a causal_model statement
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print(<type_posterior>) - Print a short summary for causal-type posterior distributions
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print(<type_prior>) - Print a short summary for causal-type prior distributions
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make_priors()set_priors()get_priors() - Setting priors
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query_distribution() - Calculate query distribution
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query_model() - Generate estimands dataframe
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realise_outcomes() - Realise outcomes
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set_ambiguities_matrix() - Set ambiguity matrix
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set_confound() - Set confound
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set_parameter_matrix() - Set parameter matrix
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set_parmap() - Set parmap: a matrix mapping from parameters to data types
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set_prior_distribution() - Add prior distribution draws
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set_restrictions() - Restrict a model
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simulate_data() - simulate_data is an alias for make_data
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substitutes() - Make statement for substitutes
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summarise_distribution() - helper to compute mean and sd of a distribution data.frame
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summary(<causal_model>)print(<summary.causal_model>) - Summarizing causal models
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te() - Make treatment effect statement (positive)
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update_model() - Fit causal model using 'stan'