Function reference • multitool

Creating a Pipeline

Add your analysis pipeline steps

add_subgroups(): Add sub groups to the multiverse pipeline

add_filters(): Add filtering/exclusion criteria to a multiverse pipeline

add_variables(): Add a set of variable alternatives to a multiverse pipeline

add_model(): Add a model and formula to a multiverse pipeline

add_preprocess(): Add arbitrary preprocessing code to a multiverse analysis pipeline

add_postprocess(): Add arbitrary postprocessing code to a multiverse pipeline

add_summary_stats(): Add a set of descriptive statistics to compute over a set of variables

add_reliabilities(): Add item reliabilities to a multiverse pipeline

add_correlations(): Add correlations from the correlation package in easystats

add_parameter_keys(): Add parameter keys names for later use in summarizing model effects

expand_decisions(): Expand a set of multiverse decisions into all possible combinations

View, Check, and Test

View metadata, check the code, and test the pipeline

create_blueprint_graph(): Create a Analysis Pipeline diagram

detect_multiverse_n(): Detect total number of analysis pipelines

detect_n_subgroups(): Detect total number of subgroups in your pipelines

detect_n_filters(): Detect total number of filtering expressions your pipelines

detect_n_variables(): Detect total number of variable sets in your pipelines

detect_n_models(): Detect total number of models in your pipelines

summarize_filter_ns(): Summarize samples sizes for each unique filtering expression

show_code_subgroups() show_code_filter() show_code_preprocess() show_code_model() show_code_postprocess() show_code_summary_stats() show_code_corrs() show_code_reliabilities(): Show multiverse data code pipelines

Run your Pipeline

Execute the whole pipeline and all alternatives

run_multiverse(): Run a multiverse based on a complete decision grid

run_descriptives(): Run a multiverse-style descriptive analysis based on a complete decision grid

Unpack Results

Unpack your results for viewing, plotting, and understanding

reveal(): Reveal the contents of a multiverse analysis

reveal_model_parameters(): Reveal the model parameters of a multiverse analysis

reveal_model_performance(): Reveal the model performance/fit indices from a multiverse analysis

reveal_model_warnings(): Reveal any warnings about your models during a multiverse analysis

reveal_model_messages(): Reveal any messages about your models during a multiverse analysis

reveal_reliabilities(): Reveal a set of multiverse cronbach's alpha statistics

reveal_corrs(): Reveal a set of multiverse correlations

reveal_summary_stats(): Reveal a set of summary statistics from a multiverse analysis

condense(): Summarize multiverse parameters