README.Rmd

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output: github_document
---

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```{r, echo = FALSE}
knitr::opts_chunk$set(
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# NetworkInference: Inferring Latent Diffusion Networks

![](https://travis-ci.org/desmarais-lab/NetworkInference.svg) ![](http://www.r-pkg.org/badges/version/NetworkInference)
![](http://cranlogs.r-pkg.org/badges/NetworkInference)
![](http://cranlogs.r-pkg.org/badges/grand-total/NetworkInference?color=yellow)

## About

This package provides an R implementation of the netinf algorithm 
created by Gomez-Rodriguez, Leskovec, and Krause (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))
```