---
title: "Romeb Package: An Introduction"

author:
  - Dandan Tang
  - Xin Tong

date: "`r Sys.Date()`"

output:
  rmarkdown::html_vignette:
    toc: true
    toc_depth: 2

vignette: >
  %\VignetteIndexEntry{Romeb Package: An Introduction}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(message = FALSE, warning = FALSE)
library(Romeb)
```

## Introduction

`Romeb` implements *robust median‑based Bayesian* growth curve modeling that
accommodate the three classical missing‑data mechanisms—MCAR, MAR and
MNAR-and complete data, particularly beneficial when data are nonnormally distributed or include outliers. A detailed tutorial can be found in Tang & Tong (2025).

## Function usage

The main interface is

```r
Romeb(
  Missing_Type,      # "MNAR", "MAR", "MCAR", or "no missing"
  data,              # matrix / data frame
  time,              # Numeric vector of measurement times (e.g., c(0,1,2,3)).
  seed,              # reproducibility seed
  K      = 0,        # number of auxiliary variables
  chain  = 1,        # number of MCMC chains
  Niter  = 6000,     # iterations per chain
  burnIn = 3000      # burn‑in iterations
)
```

### Arguments

| Argument | Description |
|----------|-------------|
| `Missing_Type` | Character string specifying the assumed missing‑data mechanism. One of `"MNAR"`, `"MAR"`, `"MCAR"`, `"no missing"`. |
| `data` | Matrix or data frame. If `K = 0`, **all** columns are treated as outcomes *y*; otherwise the first *K* columns are auxiliary variables and the next `Time` columns are outcomes. |
| `time` | Numeric vector of measurement times (e.g., c(0,1,2,3)). |
| `seed` | Integer seed ensuring reproducibility. |
| `K` | Non‑negative integer (default 0) giving the number of auxiliary variables. |
| `chain` | Number of parallel MCMC chains (default 1). |
| `Niter` | Total iterations **per chain** (default 6000). |
| `burnIn` | Iterations discarded as burn‑in (default 3000). |

## Output object

Running

```r
Res <- Romeb(...)
print(Res)             # or simply type Res
```

returns a compact table with the posterior median, Geweke *z*‑scores,
the 95% equal‑tail credible interval, and the 95% highest‑posterior‑density
(HPD) interval for each monitored parameter.

Further elements can be accessed directly:

| Element | Content |
|---------|---------|
| `Res$quantiles` | Posterior mean, SD, naïve and time‑series SEs, plus selected quantiles for every parameter *after* burn‑in. |
| `Res$geweke` | Vector of Geweke diagnostic *z*‑scores; values within ±1.96 indicate no evidence against lack of convergence. |
| `Res$credible_intervals` | 95% equal‑tail credible intervals (2.5% & 97.5% quantiles). |
| `Res$hpd_intervals` | 95% HPD intervals (shortest 95% region). |
| `Res$samps_full` | Complete `coda::mcmc.list` (including burn‑in).  Inspect with `coda::traceplot(Res$samps_full[,'par[i]'])` for par[i] . |

## Quick examples

Below we illustrate a workflow. 


```{r example-complete, eval = TRUE}
set.seed(123)
Y <- matrix(rnorm(300*5), nrow = 300, ncol = 5)  # tiny complete data set
result_full <- Romeb("no missing", data = Y, time = c(0, 1, 2, 3, 4), seed = 123)
print(result_full)
```
Note: par [1]: latent intercept, par [2]: latent slope: par [3]: variance of the latent intercept, par [4]: covariance between intercept and slope,  par [5]: variance of the latent slope. 

### MCAR example

```{r example-mcar, eval = FALSE}
set.seed(456)
Y <- matrix(rnorm(300 * 5), nrow = 300)
miss <- runif(length(Y)) < 0.1       # 10% missing completely at random
Y[miss] <- NA
result_mcar <- Romeb("MCAR", data = Y, time = c(0, 1, 2, 3, 4), seed = 456)
```

### MNAR with auxiliary variables

```{r example-mnar-aux, eval = FALSE}
set.seed(789)
X  <- matrix(rnorm(300 * 2), 300, 2)     # two auxiliaries
Y  <- matrix(rnorm(300 * 5), 300, 5)
Data <- cbind(X, Y)
result_mnar <- Romeb("MNAR", data = Data, time = c(0, 1, 2, 3, 4), K = 2, seed = 789)
```

## Inspecting convergence visually

```{r traceplot, fig.cap = "Trace plot for the first chain (par[1])", eval = TRUE}
# Uses the tiny example result_full from above
coda::traceplot(result_full$samps_full[,'par[1]'])
```

## How to cite

Please cite the package as:

> Tang,D.and Tong,X.(2025). *Romeb: An R Package for Robust Median-Based 
> Bayesian Growth Curve Modeling with Missing Data.* 


Bibliographic metadata can also be obtained via `citation("Romeb")`.