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output: github_document
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```{r, echo = FALSE}
knitr::opts_chunk$set(
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# footBayes

The goal of ```footBayes``` is to propose a complete workflow to:

- fit the most well-known football models: double Poisson, bivariate Poisson, Skellam, student-t, according to both maximum likelihood and Bayesian methods (+ Hamiltonian Monte Carlo engine);

- visualize the teams' abilities, the model checks, the rank-league reconstruction;

- predict out-of-sample matches.


## Installation

Alternatively to CRAN, you can safely install ```footBayes``` from github with:

```{r gh-installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("leoegidi/footBayes")
```

## Example 

In what follows, a quick example to fit a Bayesian double Poisson model for the Italian Serie A (seasons 2000-2001, 2001-2002, 2002-2003), visualize the estimated teams' abilities, and predict the last four match days for the season 2002-2003:

```{r example, eval = FALSE}
library(footBayes)
require(dplyr)

# dataset for Italian serie A
data("italy")
italy <- as_tibble(italy)
italy_2000_2002<- italy %>%
 dplyr::select(Season, home, visitor, hgoal, vgoal) %>%
 filter(Season=="2000" |  Season=="2001" | Season =="2002")

fit1 <- stan_foot(data = italy_2000_2002,
                model="double_pois",
                predict = 36)             # double poisson fit (predict last 4 match-days)
foot_abilities(fit1, italy_2000_2002)     # teams abilities
pp_foot(italy_2000_2002, fit1)            # pp checks
foot_rank(italy_2000_2002, fit1)          # rank league reconstruction
foot_prob(fit1, italy_2000_2002)          # out-of-sample posterior pred. probabilities


```

For more and more technical details and references, see the vignette!