---
title: "Citation in Rmarkdown file"
author: "By: Wyclife Agumba Oluoch (wyclifeoluoch@gmail.com)"
date: "`r Sys.time()`"
bibliography: [bib/cited.bib, bib/packages.bib]
link-citations: yes
nocite: '@*'
output: 
  html_document:
    toc: true
    toc_float: true
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r libs, echo = F, warning = F, include = F, message = FALSE}

packages <- c('base', 'knitr', 'rmarkdown', 
              'tidyverse', 'here', 'todor', 
              'lintr', 'DT', 'kableExtra', 
              'roxygen2', 'testthat', 'usethis',
              'devtools', 'tinytex'
              )

installed_packages <- packages %in% rownames(installed.packages())
if (any(installed_packages == FALSE)){
  install.packages(packages[!installed_packages])
}

# Loading libraries
lapply(packages, 
       library, 
       character.only = TRUE) |> 
  invisible()
```

```{r write_bib, echo = F, warning = F, include = F, message = F}

knitr::write_bib(c(
  .packages(), packages
), 'bib/packages.bib')
```

# In Brief

In this article, I demonstrate how to include citations of `R` packages and other external sources of literature  within an `Rmarkdown` file in `R` [@R-base]. Some of the packages include `tidyverse` version `r packageVersion('tidyverse')` [@R-tidyverse]. I also included external citations which are not packages [_sensu_ @naimi2016sdm, pp.3; @naimi2014positional]. Similarly, Naimi [-@naimi2016sdm] showed that predictor variables should be scaled to ease the interpretation of the variable importance values. This is especially true when not using such models like random forest. @soberon2004biodiversity [pp. 25] had shown similar patterns in their earlier study.

Additional important literature for `sdm` include the works of [@zurell2020standard]. This is relevant especially for the reproducibility of the `sdm` or `enm` process. Zurell [-@zurell2020standard] showed that `enm` process can be done in a manner that allows for reproducibility. The work on citing biodiversity occurrence data is also well articulated [@owens2021occcite]. Owens and Rahbeck [-@owens2023volumodel] have also brought in interesting aspects of modeling into third dimension especially for marine species. These works are making handling modern `sdm` more interesting and creating new opportunities for furthering research.

There is also a new package which is called `grateful` which is easing the task of citing base `R` packages. I will give it a try some day later.

The R codes for the .Rmd and other associated files on my [gitHub](https://github.com/Wycology/citation) repository. 

# References