afttest: Model Diagnostics for Accelerated Failure Time Models
================

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The R package **afttest** is intended to be a user-friendly *supplementary*
package to the base package **aftgee**.

# Features

In addition to the R interface, **afttest** provides a C++ header-only library 
integrated with **Rcpp**, which allows the construction of spline basis 
functions directly in C++ with the help of **Rcpp** and **RcppArmadillo**. 

# Installation of CRAN Version

You can install the released version from
[CRAN](https://CRAN.R-project.org/package=afttest).

``` r
install.packages("afttest")
```

# Development

The latest version of the package is under development at
[GitHub](https://github.com/WoojungBae/afttest). If it is able to pass
the automated package checks, one may install it by

``` r
if (!require(devtools)) install.packages("devtools")
devtools::install_github("WoojungBae/afttest")
```

# Performance

The implementation of the main functions has been written in C++ with the 
help of the **Rcpp** and **RcppArmadillo** packages. The computational 
performance has thus been boosted.

# Replication Materials & File Manifest

As per the computational demands of the Monte Carlo simulations, the replication 
materials for the empirical results are divided into the following 
self-contained files.

### 1. Main Analysis (Fast Execution)
* **`afttest_replication_reduced_script.R`** 
This is the main replication script for the manuscript. It reproduces the
results reported in the paper that are intended for routine verification on a
standard laptop or desktop computer. In particular, it generates the empirical
analysis, summary outputs, and the reduced simulation results reported in the
article.

* **`afttest_replication_script.R`**
* **Description:** This is the primary script containing the code to reproduce 
the main empirical data analyses, summary statistics, and all **Figures** 
presented in the manuscript (e.g., the Primary Biliary Cirrhosis dataset analysis).
* **Execution Time:** Fast (Runs on a standard desktop in minutes).

### 2. Table 1: Computational Time Simulation (Long-Running)
* **`package_sim2_hpc_TIME.R`** & **`batchAFTsim2_hpc_TIME.sbatch`**
* **Description:** These files generate the data required to reproduce 
**Table 1** (Average running times comparing the asymptotic linear approximation 
vs. standard resampling).
* **Execution Time:** **[WARNING: EXTREMELY LONG-RUNNING]** These simulations 
are highly computationally intensive. The provided `.sbatch` file is configured 
for execution on a High-Performance Computing (HPC) cluster (e.g., University of 
Florida HiPerGator).

### 3. Table 2: Empirical Rejection Rates Simulation (Long-Running)
* **`package_sim2_hpc.R`** & **`batchAFTsim2_hpc.sbatch`**
* **Description:** These files generate the data required to reproduce 
**Table 2** (Empirical Type I error and power/rejection rates).
* **Execution Time:** **[WARNING: EXTREMELY LONG-RUNNING]** Similar to Table 1, 
this script involves thousands of Monte Carlo replications and resampling paths. 
It is designed to be submitted to an HPC cluster via the provided `.sbatch` file.

# Important Note on the Simulations Reported in the Paper

Due to the journal’s time and computational constraints, the simulation results 
reported in the paper were based on a reduced set of Monte Carlo runs and 
parameter settings.

## Simulations reported in the paper

- **Table 1:** Only the omnibus test was considered, with sample sizes 
**n = 100** and **n = 500**, each based on **10 simulation runs**, comparing 
**`linApprox = TRUE`** versus **`linApprox = FALSE`**.
- **Table 2:** For each combination of **omnibus**, **link**, and **covForm**, 
simulations were run with sample sizes **n = 100** and **n = 500**, each based on 
**100 simulation runs**, with **`gamma = 0.0`** and **`0.5`**. Results report the 
empirical **Type I error** and **power** for **`linApprox = TRUE`**.

# Extended Simulations Available in the Replication Folder

Running the scripts in the replication folder produces a broader set of simulation 
results than those reported in the paper.

- **Table 1:** For each combination of **omnibus**, **link**, and **covForm**, 
simulations are run with sample sizes **n = 100** and **n = 500**, each based on 
**1000 simulation runs**.
- **Table 2:** For each combination of **omnibus**, **link**, and **covForm**, 
simulations are run with sample sizes **n = 100** and **n = 500**, each based on 
**1000 simulation runs**, with **`gamma = 0.0, 0.1, 0.2, 0.3, 0.4,`** and **`0.5`**. 
Results report the empirical **Type I error** and **power** for **`linApprox = TRUE`**.

# Instructions for Execution
1. **Ensure all files are in the same working directory.**
2. **To reproduce the main text examples and figures:** Open R or RStudio, set 
your working directory to this folder, and source the main script:
   
       source("afttest_replication_script.R")
   
3. **To reproduce Tables 1 and 2 (HPC environment):** If you have access to a 
Slurm-based HPC cluster, submit the batch jobs directly from the terminal:
   
       sbatch batchAFTsim2_hpc_TIME.sbatch
       sbatch batchAFTsim2_hpc.sbatch
   
   *Note: To run the simulation scripts locally for verification, open the 
   respective `R` scripts, drastically reduce the `sim_per_file` parameter, and 
   execute them in your R console.*

# Reference

Bae, W., Choi, D., Yan, J., Kang, S. (2026). Diagnostics for semiparametric 
accelerated failure time models with R Package afttest. 
*Journal of Statistical Software, to appear*, **arxiv.org/abs/2511.09823**.

Choi, D., Bae, W., Yan, J., and Kang, S. (2024). A general model-checking 
procedure for semiparametric accelerated failure time models. 
*Statistics and Computing*, **34**(3) 117.

Bae, W., Choi, D., Yan, J., Kang, S. (2022). afttest: Model Diagnostics for 
Accelerated Failure Time Models. *CRAN*, 
**https://CRAN.R-project.org/package=afttest**.

Chiou, S., Kang, S., and Yan, J. (2014). Fitting accelerated failure time model 
in routine survival analysis with R package aftgee. 
*Journal of Statistical Software*, **61**(11): 1--23.