index.Rmd

```{r,message=FALSE,echo=FALSE}
  library("knitcitations")
#  bib <- read.bibtex("bibtexlib.bib")
  library("knitr")
  cite_options(max.names = 3, style = "html", hyperlink = "to.doc")
```
---
title: "Introduction"
---

Reproducible science or research provide the ability to replicate research results from a published study. This idea ultimately also includes the full computational environment comprising code and data to replicate the analysis `r citep("10.1126/science.1213847")`. 

Over recent years our research group has settled on using the R statistical and computing environment together with R studio, R markdown and latex with some or our own scripts and packages to conduct reproducible science. 

Why use R?
-----

R provides a toolbox with its packages that
allows analysis of most data conveniently without tedious reformatting on all
major computing platforms including Microsoft Windows, Linux, and Apple's OS X.
R is an open source statistical programming and graphing language that includes
tools for statistical, epidemiological, population genetic, genomic, phylogenetic, and
comparative genomic analyses. 

Note that the R user community is very active and that both R and its packages
are regularly updated, critically modified, and noted as deprecated (no longer
updated) as appropriate.

> Any R user needs to make sure all components are up-to-date and that versions
> are compatible.

References
----------
```{r, results = 'asis', echo=FALSE}
  bibliography()
```

<!--- ref --->