# coxmnar: Cox Regression with Missing Not at Random Failure Indicators

`coxmnar` implements estimation for the Cox proportional hazards model when the failure indicator (cause of failure) is missing not at random (MNAR), following the methodology of **Liu and Liu (2026)** (*Statistics and Computing*, 36, 112).

## Features

- **Adjusted Imputation (AI)** ($\hat{\beta}_n^{AI}$) and **Adjusted Complete (AC)** ($\hat{\beta}_n^{AC}$) estimators under MNAR.
- Benchmark estimators: Complete-Case (**CC**), Missing-at-Random (**MAR**), and Full-Data (**full**).
- Joint maximum likelihood estimation of outcome and missingness propensity models via L-BFGS-B.
- Nadaraya-Watson kernel propensity smoothing with 5 kernel choices and automatic bandwidth selection.
- Nonparametric bootstrap and asymptotic sandwich variance estimators.
- Data simulator `simulate_coxmnar_data()` implementing paper simulation DGPs.

## Installation

You can install the development version of `coxmnar` from GitHub:

```r
# install.packages("devtools")
devtools::install_github("shikhartyagi/coxmnar")
```

## Quick Start

```r
library(coxmnar)

# Simulate survival data with MNAR missing failure indicators
set.seed(123)
sim_data <- simulate_coxmnar_data(n = 200, beta0 = 0.2, mechanism = "mnar", seed = 123)

# Fit Adjusted-Imputation MNAR estimator
fit <- coxmnar(
  cause ~ Z,
  time = "time",
  data = sim_data,
  method = "ai",
  B = 100L,
  seed = 123
)

summary(fit)
```

## Citation

To cite `coxmnar` in publications:

```r
citation("coxmnar")
```

## Reference

- Liu, Y., & Liu, K. (2026). Estimation in the Cox proportional hazards model with missing not at random failure indicators. *Statistics and Computing*, 36, 112. <doi:10.1007/s11222-026-10857-1>
