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In data science, the letter represents a widely used programming language for statistical computing.

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RmissAX produces a gallery of ready‑to‑publish plots: rmissax full

## 8️⃣ Return a tidy list -------------------------------------------------- list(imputed_data = imp$pooled, imputed_sets = imp$imputations, diagnostics = list(pattern = pat, mcar = mcar_res, mar = mar_res, mnar = mnar_res, imputation = imp$diagnostics), plots = plots, report = report_path)

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rmissax --list-plugins # You should see `myplugin` listed.

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| Plot | What you see | |------|--------------| | missingness_heatmap | Cells = missing (blue) vs observed (white). | | density_overlay | Pre‑ vs post‑imputation density for each variable. | | trace_plot | MCMC‑style convergence of imputed values across iterations. | | pairwise_missingness | Correlation of missingness patterns (similar to VIM::aggr ). |

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: In R programming, functions handling missing data values often use the prefix miss (e.g., missForest , naniar packages).

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