CausalQueries1.1.1 package

Observe data, given a strategy

helper to check arguments

Warn about improper query specification and apply fixes

Check string_input

Clean condition

Clean parameter vector

Check parameters sum to 1 in param_set; normalize if needed; add names...

default_stan_control

Draw a single causal type given a parameter vector

Drop empty families

Expand compact data object to data frame

Helper to expand nodal expression

Expand wildcard

helper to find rounding thresholds for print methods

Print a tightened summary of model queries

Print a short summary for causal_model nodal-types

Print a short summary for causal_model nodes

Print a short summary for paramater mapping matrix

Print a short summary for causal_model parameters

Print a short summary for a causal_model parameters data-frame

Print a short summary for causal_model parameter posterior distributio...

Print a short summary for causal_model parameter prior distributions

Print a short summary for a causal_model parents data-frame

Print a short summary of posterior_event_probabilities

Print a short summary for stan fit

Print a short summary for a causal_model statement

Print a short summary for causal-type posterior distributions

Print a short summary for causal-type prior distributions

Setting priors

helper to get types from queries

Calculate query distribution

Helper to fill in missing do operators in causal expression

Adds a wildcard for every missing parent

Names for causal types

'CausalQueries'

Create parameter documentation to inherit

Make compact data with data strategies

collapse nodal types

Make statement for complements

make_par_values

make_par_values

make_par_values

helper to generate a matrix mapping from names of M to names of A

Data type names

Make monotonicity statement (negative)

Make data

Generate full dataset

Make data in compact form

Make a model

Make nodal types

make_par_values

make_par_values_stops

Make parameter matrix

function to make a parameters_df from nodal types

Make parmap: a matrix mapping from parameters to data types

Make a prior distribution from priors

Creates a data frame for case with no data

Creates a compact data frame for case with no data

n_check

Identify nodes in a statement

Make monotonicity statement (non negative)

Make monotonicity statement (non positive)

Setting parameters

Helper to turn parents_list into a list of data_realizations column po...

Produces the possible permutations of a set of nodes

Plots a DAG in ggplot style using a causal model input

Prepare data for 'stan'

Print a short summary for a causal model

Print a short summary for causal_model causal-types

Print a short summary for a causal_model DAG

Print a short summary for event probabilities

Generate estimands dataframe

Helper to turn query into a data expression

Realise outcomes

Reduce nodal types using labels

Reduce nodal types using statement

Reveal outcomes

Set ambiguity matrix

Fit causal model using 'stan'

Update causal types based on nodal types

Set confound

Set parameter matrix

Set parmap: a matrix mapping from parameters to data types

Add prior distribution draws

Restrict a model

set_sampling_args From 'rstanarm' (November 1st, 2019)

simulate_data is an alias for make_data

Get string between two regular expression patterns

Make statement for substitutes

helper to compute mean and sd of a distribution data.frame

Summarizing causal models

Make treatment effect statement (positive)

Generate type matrix

uncollapse nodal types

Unpack a wild card

Get a prior distribution from model

Look up query types

helper to get type distributions

Get type names

Get type probabilities

generates one draw from type probability distribution for each type in...

Draw matrix of type probabilities, before or after estimation

generates n draws from type probability distribution for each type in ...

Grab

Recursive substitution

Make monotonicity statement (positive)

Make statement for any interaction

Interpret or find position in nodal type

Check whether argument is a model

Returns a list with the nodes that are not directly pointing into a no...

Make ambiguities matrix

Get all data types

Get ambiguities matrix

Get causal types

get_data_families

helper to get estimands

Draw event probabilities

Get list of types for nodes in a DAG

Get a distribution of model parameters

Get parameter matrix

Get parameter names

Get list of parents of all nodes in a model

Get parmap: a matrix mapping from parameters to data types

Get the posterior distribution from a model