PREMIUM R MARKDOWN WORKFLOW · VERSION 1.0

Keep the uncertainty.
Recover the analysis.

A complete, editable workflow for auditing missing data, imputing continuous, binary, and ordinal variables, diagnosing chained equations, pooling longitudinal models, and testing sensitivity to unverifiable assumptions.

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One-time purchase · Individual-use license · Secure digital delivery

See the pattern firstAudit missingness by variable, participant, group, and wave.

Impute transparentlyDocument methods, predictors, seeds, chains, and plausible ranges.

Report uncertaintyPool models and examine sensitivity instead of hiding assumptions.

WHAT IS INSIDE

A complete missing-data workflow.

The package connects missingness description, model specification, diagnostics, pooled inference, comparison, and sensitivity analysis in one reproducible report.

Audit what is missing

Summarize and visualize missing responses by variable, case, program group, study wave, and pattern.

State the assumptions

Distinguish MCAR, MAR, and MNAR without claiming that an observed-data test can prove the mechanism.

Specify chained equations

Use predictive mean matching, logistic regression, ordinal logistic regression, and passive interactions with a documented predictor matrix.

Diagnose the imputations

Review chain behavior, logged events, observed and imputed distributions, and impossible completed values.

Fit and pool the models

Pool longitudinal mixed and binary logistic models across 20 completed datasets using Rubin's rules.

Test sensitivity

Compare complete cases and examine delta adjustments when departures from MAR could alter the conclusion.

DESIGNED FOR DOCTORAL RESEARCH

Use it when incomplete data are part of the methodological argument.

This workflow is useful when:

Your predictors, outcomes, auxiliaries, or repeated measures contain missing values.
Your dataset mixes continuous, binary, and ordinal variables.
You need to explain why variables entered the imputation model.
You need pooled regression estimates rather than one filled-in dataset.
Your committee expects diagnostics, a complete-case comparison, or sensitivity analysis.

Many irregular occasions, complex survey designs, survival outcomes, three-level data, or strongly MNAR processes require extensions beyond this introductory workflow.

PUBLIC PREVIEW

Review the complete analysis output.

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Missing Data & Multiple Imputation · Public Preview

The public preview shows the analysis output, figures, decision guidance, and dynamic reporting. The paid package includes the complete editable R Markdown source, reusable module, all analysis code, synthetic data, documentation, and exported results.

THE DOWNLOADABLE PACKAGE

Run the example.
Then adapt your study.

The synthetic longitudinal dataset contains documented intermittent gaps and monotone attrition across continuous, binary, and ordinal variables. The complete analysis runs before you introduce your own data.

Editable R Markdown source and rendered report
Reusable missing-data functions for other projects
Documented longitudinal synthetic dataset
Package installer and RStudio project
Missingness tables and accessible figures
Methods and predictor-matrix exports
Pooled mixed and logistic model results
Complete-case comparison and delta sensitivity analysis
Start guide, integration plan, and individual license

The workflow supports analysis and documentation. Missing-data assumptions and model choices remain the researcher's responsibility and should be reviewed with the dissertation advisor, committee, or a qualified statistician.

VERSION 1.0

Complete workflow.
$59 one time.

Pay securely and receive the version 1.0 ZIP with the editable analysis, reusable module, synthetic data, documentation, rendered report, and reusable exports.

Purchase for $59 Review purchase terms