Package index • margot

Simulate

run_simulations(): Run Simulations for Estimating ATE

simulate_ate_data_with_weights(): Simulate Data for Average Treatment Effect (ATE) with Sample Weights

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

match_mi_general(): General Matching Function for Multiple Imputation Data

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

margot_plot_categorical(): Create a Coloured Histogram with Quantile or Custom Breaks

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_summary_tables(): Generate summary tables and plots for longitudinal data

margot_transition_table(): Format a Transition Table

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_amelia_to_mice(): convert an amelia object to a mice object

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

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 for multiple waves

margot_wide(): Transform longitudinal data to wide format with labels

margot_wide_impute_baseline(): Transform to wide data with labels and impute baseline missing values

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

Treatment effect estimation

causal_contrast_engine(): Compute Causal Contrasts

causal_contrast_marginal(): Causal Contrast Marginal Effects Estimation

double_robust_marginal(): Double Robust Marginal Estimation and Tabulation

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

margot_lmtp(): Batch Process LMTP Models

margot_multi_arm_causal_forest(): Run Multiple Generalized Random Forest (GRF) Multi-Arm Causal Forest Models with Enhanced Features

Interpret and Report Results

margot_bind_tables(): Bind and Format Domain-Specific Tables

margot_compare_groups(): Compare Treatment Effects Between Groups

margot_flip_forests(): Flip CATE Estimates for Selected Outcomes in GRF Model Results

margot_interpret_marginal(): This function interprets the output of causal effect analysis, providing a compact report. It only reports coefficients and E-values with **Evidence** or **Strong evidence** for causality, unless all estimates with E_Value > 1 (and E_Val_bound > 1) are requested. Each outcome's interpretation starts with a separate paragraph heading. Additionally, it includes a final paragraph indicating that all other effect estimates presented either weak or unreliable evidence for causality.

margot_interpret_policy_batch(): Batch process policy tree interpretations

margot_interpret_policy_tree(): Interpret Policy Tree Results

margot_interpret_qini(): Interpret Qini Results for Both Binary and Multi-Arm Treatments

margot_interpret_rate(): Interpret RATE estimates in markdown

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

margot_lmtp_tab(): Summarise LMTP Output into a Data Frame

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

margot_model_tab(): Summarise LMTP or Causal Forest Output into a Data Frame

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 Visualizations

margot_rate(): Format RATE Results into Readable Tables

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

Visualise Causal Effect Estimates

margot_plot(): Create a Margot Plot with Interpretation

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

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 Policy Tree Results

margot_plot_qini(): Plot Qini Curves from margot_multi_arm_causal_forest Results

margot_plot_rate(): Plot Rank Average Treatment Effect

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

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

construct_formula(): Construct a Formula for Regression Models

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_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_process_binary_vars(): Process Binary Variables in a Data Frame

margot_prop_missing(): Proportion of missing data at baseline

margot_save_png(): Save Margot Plot as PNG

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

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

transform_table_rownames(): Transform Table Row Names with CLI Feedback

Simulated data

df_nz: df_nz: Example Data Frame