Package index • margot

Describe Longitudinal Data

margot_count_dyads(): count dyads in longitudinal data

margot_count_ids(): count individual participants in longitudinal data

margot_plot_boxplot(): Create panel data Boxplots using ggplot2

margot_plot_boxplot_covariate(): create boxplots with covariates using ggplot2

margot_plot_discontinuity(): Create a Discontinuity Plot for Multiple Events

margot_plot_histogram(): Create a Histogram with Mean and Standard Deviation Highlights for Each Wave and Variable

margot_plot_response_timeline(): Plot Panel Study Response Timeline

margot_plot_slope(): Create a Slope Plot for Multiple Variables

margot_plot_slope_covariate(): Create a Slope Plot using ggeffects

margot_plot_slope_covariate_batch(): Create a Combined Slope Plot using ggeffects and patchwork

margot_plot_slope_covariate_combo(): Create a Combined Slope Plot using ggeffects and patchwork

margot_summary_panel(): Generate Summary Panel for Margot Study

Check assumptions

margot_assess_overlap(): Assess Covariate Overlap from Causal Forest Models

margot_make_tables(): Create Summary Tables Using table1 with Custom Formatting

margot_plot_categorical(): Create a coloured histogram with summary lines and optional median

margot_plot_individual_responses(): Create Individual Longitudinal Response Plots

margot_plot_shift(): Visualise Shifts in Data Distributions with Highlighted Ranges

margot_propensity_model_and_plots(): Create Propensity Score Model and Associated Plots

margot_transition_table(): Format a Transition Table with observed‑indicator filtering

Prepare data for models

create_ordered_variable(): Create Ordered Variable Based on Quantile Breaks or Custom Breaks with Informative Labels

impute_and_combine(): Perform multiple imputation on a list of data frames and combine the results

margot_adjust_weights(): Adjust Weights for Censoring and Sample Design with Progress Reporting

margot_censor(): Transform year_measured Variable Based on Clustered Conditions Within Waves

margot_censor_lead(): Apply Lead-Based Censoring to Longitudinal Data

margot_compute_gender_weights_by_wave(): Compute Gender-Based Sample Weights Using Baseline Wave Proportions

margot_filter(): Filter Data Based on Exposure Variables

margot_impute_carry_forward(): Impute Missing Values Using Carry Forward in Longitudinal Data

margot_log_transform_vars(): Log-transform Variables in a Data Frame

margot_process_binary_vars(): Process Binary Variables in a Data Frame

margot_process_longitudinal_data(): process longitudinal data for three waves

margot_process_longitudinal_data_wider(): Process longitudinal dyadic data in wide format with censoring by missing exposure and silent dummy-coding

margot_wide_machine(): Transform longitudinal data to wide format with baseline imputation and optional NA indicators

Treatment effect estimation

margot_causal_forest(): Run Multiple Generalized Random Forest (GRF) Causal Forest Models with Enhanced Qini Cross-Validation

margot_lmtp(): Batch Process LMTP Models

margot_naive_regressions(): Perform Naive Cross-Sectional Regressions

margot_policy_tree(): Recompute Policy Trees with Custom Parameters

margot_policy_tree_stability(): Stability Analysis for Policy Trees

margot_policy_tree_bootstrap(): Bootstrap Analysis for Policy Trees

margot_assess_variable_correlation(): Assess Variable Correlations for Policy Tree Analysis

margot_identify_variable_clusters(): Identify Variable Clusters for Policy Tree Analysis

margot_stability_diagnostics(): Diagnose Policy Tree Stability

margot_recalculate_policy_trees(): Recalculate Policy Trees with Custom Covariates

margot_recompute_ate(): Recompute Average Treatment Effects with Different Target Samples

margot_recompute_ate_batch(): Batch Recompute ATEs for Multiple Target Samples

Interpret and Report Results

margot_bind_tables(): Bind and format domain-specific tables or a single table

margot_bind_models(): Combine multiple batched model outputs (with covariates & metadata)

margot_compare_groups(): Compare subgroups from a causal forest model.

margot_correct_combined_table(): Correct a "combined table" for multiplicity **and** recompute *E*-values

margot_flip_forests(): Flip (Reverse) Causal Forest Treatment Effects

margot_inspect_qini(): Inspect qini diagnostics for one or several models

margot_interpret_stability(): Interpret Policy Tree Stability Results

margot_interpret_stability_batch(): Batch Interpret Policy Tree Stability Results

margot_interpret_bootstrap(): Interpret Bootstrap Policy Tree Results

margot_interpret_policy_batch(): Batch process policy tree interpretations

margot_interpret_policy_tree(): Interpret Policy Tree Results

margot_interpret_qini(): Interpret Qini Results

margot_interpret_rate(): Interpret RATE Estimates

margot_interpret_rate_cv(): Interpret CV RATE Results

margot_interpret_heterogeneity(): Interpret Heterogeneity Evidence from Multiple Sources

margot_interpret_rate_comparison(): Compare AUTOC and QINI RATE Tables

margot_lmtp_evalue(): Combine LMTP Summary and E-Value Calculation

margot_model_evalue(): Combine Model Summary and E-Value Calculation for Various Causal Models

margot_omnibus_hetero_test(): Omnibus Heterogeneity Test for GRF Models

margot_planned_subgroups_batch(): Batch process heterogeneity analyses across multiple outcome domains

margot_policy(): Batch Processing of Policy Trees and Related Visualisations

margot_qini(): Generate QINI Curves and Difference Gain Summaries

margot_rate(): Assemble RATE tables (AUTOC and QINI)

margot_rate_batch(): Batch-compute RATEs for each outcome in a margot_causal_forest result

margot_rate_cv(): Cross-Validation Test for Treatment Effect Heterogeneity

margot_recompute_qini_aipw(): Recompute QINI Curves Using AIPW Scores

margot_recompute_qini_ipw(): Recompute QINI Curves Using IPW Scores

margot_rescue_qini(): Post-process models to recover Qini curves via propensity trimming

margot_resort_contrast_lmtp(): Resort/Reorder LMTP Contrasts by Recomputing from Models

margot_subset_model(): Subset Model Results for Binary and Categorical Exposures

margot_subset_batch(): Batch Process Subset Models for Causal Forests

margot_summary_cate_difference_gain(): Compute Difference in Gains and Integrated Difference Between Reference and Comparison Curves

get_consensus_info(): Get consensus tree information

get_variable_importance(): Extract variable importance from bootstrap results

summary(<margot_stability_policy_tree>): Summary method for stability policy tree results

print(<margot_stability_policy_tree>): Print method for stability policy tree results

summary(<margot_bootstrap_policy_tree>): Summary method for bootstrap policy tree results

print(<margot_bootstrap_policy_tree>): Print method for bootstrap policy tree results

print(<margot_correlation_assessment>): Print method for margot_correlation_assessment

Visualise Causal Effect Estimates

margot_plot(): Create a Margot Plot with Proper Multiplicity Correction

margot_plot_decision_tree(): Plot a Decision Tree from Margot Causal-Forest Results (robust labelling)

margot_plot_multi_arm(): Create a Multi-arm Margot Plot with User-specified Contrast

margot_plot_policy_combo(): Create a Combined Decision Tree and Policy Relationship Graph

margot_plot_policy_tree(): Plot a policy tree (depth-adaptive)

margot_plot_qini(): Plot Qini Curves from margot_multi_arm_causal_forest Results

margot_plot_qini_batch(): Batch Process and Plot QINI Curves for Multiple Models

margot_plot_qini_batch_cost_sensitivity(): Batch Plot QINI Curves Across Treatment Costs for Multiple Models

margot_plot_qini_cost_sensitivity(): Plot QINI Curves Across Treatment Cost Scenarios

margot_plot_qini_cost_summary(): Create Summary Plot of Optimal Treatment Fractions Across Costs

margot_qini_cost_sensitivity(): Perform Cost Sensitivity Analysis for QINI Curves

margot_qini_scale_note(): Generate Explanatory Note for QINI Scale

margot_qini_scale_subtitle(): Generate Brief Scale Description for Plot Subtitles

margot_plot_rate(): Plot Rank Average Treatment Effect

margot_plot_rate_batch(): Batch Process and Plot RATE Curves for Multiple Models

margot_plot_tau(): Create Faceted Tau Hat Distribution Plots

margot_plot_cv_results(): Plot Cross-Validation Heterogeneity Test Results

margot_plot_cv_summary(): Create summary plot for cross-validation heterogeneity results

margot_view_lmtp_structure(): Helper function to view available models and contrasts

Utility functions

back_transform_logmean(): Back-transform Log-transformed Mean

back_transform_log_z(): Back Transform Z-Score to Original Log-Transformed Scale

back_transform_zscore(): Back Transform Z-Score to Original Scale

here_read(): Read Data Frame or Object from RDS File in a Specified Directory

here_save(): Save Data Frame as RDS File in a Specified Directory

here_read_qs(): Read Data Frame or Object from qs File in a Specified Directory

here_save_qs(): Save Data Frame or Object to qs File in a Specified Directory with Enhanced Compression

lmtp_evalue_tab(): Calculate E-values for LMTP Output

margot_back_transform_log_z(): Create Z-score to Original Scale Mapping for Log-Transformed Data

margot_debug_qini(): Debug Qini Curve Data

margot_get_labels(): Get display labels for multiple variable names

margot_invert_measure(): Invert Measure Values for Reverse Scoring

margot_log_transform_vars(): Log-transform Variables in a Data Frame

margot_plot_create_options(): Create Plot Options for Margot Plot

margot_plot_exposure(): Create a separate exposure plot

margot_plot_qini_simple(): Plot QINI Curves (Simplified Version)

margot_qini_alternative(): Compute QINI Curves (Alternative Implementation)

margot_qini_diagnostic(): Diagnose QINI Gain Discrepancies

margot_process_binary_vars(): Process Binary Variables in a Data Frame

margot_prop_missing(): Proportion of missing data at baseline

margot_rescue_qini(): Post-process models to recover Qini curves via propensity trimming

margot_reversed_labels(): Update label map by marking reversed outcomes

margot_save_png(): Save Margot Plot as PNG

margot_size(): Calculate the size of an R object in megabytes

margot_trim_sample_weights(): Standardise and (optionally) trim sample weights at both ends

prepare_panel_data(): Prepare Panel Data for Timeline Visualization

pretty_number(): Format Numbers with Commas

remove_numeric_attributes(): Remove Attributes from Numeric Columns in a Data Frame

regress_with_covariates(): Generalized Linear Regression with Covariates

select_and_rename_cols(): Select and Rename Columns Based on Criteria

Data and Simulation

df_margot_example: Margot Example Dataset

fetch_margot_data(): Fetch Margot Example Data

list_margot_data(): List Available Margot Datasets

clear_margot_cache(): Clear Margot Data Cache

margot_simulate(): Simulate longitudinal exposures, outcomes, and covariates

margot_simulate_test_data_flip(): Generate Test Data with Flipped Outcomes