---
title: "NetworkInference: Quick Start Guide"
author: "Fridolin Linder"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{NetworkInference: Quick Start Guide}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

---

# Introduction

---

This package provides an R implementation of the netinf algorithm 
created by @gomez2010inferring (see [here](http://snap.stanford.edu/netinf/) 
for more information and the original C++ implementation). Given a set of events 
that spread between a set of nodes the algorithm infers the most likely stable 
diffusion network that is underlying the diffusion process.

---

# Installation

---

The package can be installed from [CRAN](https://CRAN.R-project.org/):
```{r, eval=FALSE}
install.packages("NetworkInference")
```

The latest development version can be installed from 
[github](https://github.com/desmarais-lab/NetworkInference):
```{r, eval=FALSE}
#install.packages(devtools)
devtools::install_github('desmarais-lab/NetworkInference')
```

---

# Quick start guide

---

To get started, get your data into the `cascades` format required by the `netinf` 
function:

```{r, results='hide', message=FALSE}
library(NetworkInference)

# Simulate random cascade data
df <- simulate_rnd_cascades(50, n_node = 20)

# Cast data into `cascades` object
## From long format
cascades <- as_cascade_long(df)

## From wide format
df_matrix <- as.matrix(cascades) ### Create example matrix
cascades <- as_cascade_wide(df_matrix)
```

Then fit the model:
```{r}
result <- netinf(cascades, quiet = TRUE, p_value_cutoff = 0.05)
```

```{r, eval=FALSE}
head(result)
```
```{r, results="asis", echo=FALSE}
pander::pandoc.table(head(result))
```

