README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# score4cast

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The goal of score4cast is to provide a convenient interface to score ecological forecasts that conform the EFI standard. 
score4cast emphasizes the use of strictly proper scores (see [scoringRules]() R package or Gneiting & Raferty's landmark 2007 paper) for _probablistic_ forecasts.
The EFI format provides a simple but flexible way to express both ensemble and parametric forecasts in a standard tabular layout. 


## Installation

You can install the development version of score4cast from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("eco4cast/score4cast")
```

## Example

A forecast (in standardized format) is scored against a target (in standardized format):


```{r example}
library(score4cast)
ex_data <- system.file("extdata/standard-format-examples.R", package="score4cast")
source(ex_data)

scores <- score(ex_forecast, ex_target)
scores
```

```{r echo = FALSE, message=FALSE}
library(tidyverse)
```

## Example using Bernoulli distribution and extra columns for grouping

```{r}
forecast <- tibble(datetime = as_date("2023-01-02"),
             site_id = "fcre",
             depth = c(1,2),
             model_id = "test",
             reference_datetime = as_date("2023-01-02"),
             variable = "temp",
             family = "bernoulli",
             parameter = "prob",
             prediction = c(0.3, 0.1))

target <- tibble(datetime = as_date("2023-01-02"),
             site_id = "fcre",
             depth = c(1,2),
             variable = "temp",
             observation = c(1,0))

crps_logs_score(forecast,target, extra_groups = "depth")
```