---
title: "nbsurv Workflow"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{nbsurv Workflow}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Overview

`nbsurv` implements a conditional naive Bayes model for right-censored survival
data. At each prediction horizon, the method treats survival past the horizon
as a binary classification problem and combines:

- a marginal Kaplan-Meier survival estimate,
- inverse-probability of censoring weighting for the event class,
- Gaussian likelihoods for continuous predictors, and
- Laplace-smoothed categorical likelihoods.

The package also includes utilities for horizon-specific evaluation,
cross-validation, and hyper-parameter tuning.

## Fit a model

```{r}
library(nbsurv)
library(survival)

lung <- stats::na.omit(lung)
lung$status <- as.integer(lung$status == 2)

fit <- nbsurv(
  Surv(time, status) ~ age + sex + ph.ecog,
  data = lung
)

fit
```

## Generate predictions

```{r}
times <- c(100, 200, 400, 800)

surv_pred <- predict(
  fit,
  newdata = lung[1:5, ],
  times = times
)

event_pred <- predict(
  fit,
  newdata = lung[1:5, ],
  times = times,
  type = "event"
)

surv_pred
event_pred
```

The returned survival matrix is post-processed to be monotone in time.

## Evaluate the fitted model

```{r}
metrics <- evaluate_nbsurv(
  fit,
  newdata = lung,
  times = times
)

metrics
```

`evaluate_nbsurv()` reports horizon-specific IPCW Brier scores and
concordance values.

## Cross-validation

```{r}
cv_fit <- cv_nbsurv(
  Surv(time, status) ~ age + sex + ph.ecog,
  data = lung,
  folds = 3,
  times = times,
  seed = 1
)

cv_fit$summary
```

## Tune hyper-parameters

```{r}
grid <- data.frame(
  scale = c(TRUE, FALSE),
  laplace = c(1, 2),
  min_sd = c(0.05, 0.10)
)
grid$time_grid <- I(list(NULL, NULL))

tuned <- tune_nbsurv(
  Surv(time, status) ~ age + sex + ph.ecog,
  data = lung,
  param_grid = grid,
  folds = 3,
  times = c(100, 200, 400),
  seed = 1
)

tuned$results
tuned$best_params
```

## Plot fitted curves

```{r}
plot(fit, times = c(100, 200, 400), n_curves = 3)
```