---
title: "RtForecastR walkthrough"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{RtForecastR walkthrough}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteDepends{rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", fig.width = 7, fig.height = 4.5)
```

```{r setup}
library(RtForecastR)
```

## Fit R_t and get a forecast

`rt_forecast()` estimates the filtered (real-time) and smoothed
(retrospective) effective reproduction number from a case-count time
series, and produces a genuine one-step-ahead out-of-sample forecast.

```{r}
data(measles_cdmx)
fit <- rt_forecast(measles_cdmx$time, measles_cdmx$cases,
                    mean_GI = 11/7, var_GI = (4/7)^2)
fit
```

```{r}
plot(fit, which = "Rt")
```

```{r}
plot(fit, which = "forecast")
```

## Checking calibration

`fit$predictions` holds in-sample one-step-ahead predictions - a quick
adequacy check:

```{r}
mae(fit$predictions$cases, fit$predictions$pred_next)
coverage(fit$predictions$cases, fit$predictions$pred_lo95, fit$predictions$pred_hi95)
```

For a genuine prospective evaluation, accumulate `fit$forecast` and the
following week's actual case count over several weeks and pass the
resulting quantile lists to `wis()`; see `?wis` and `?score_batches`.