diff --git a/DESCRIPTION b/DESCRIPTION
index 46f58ef..16babfe 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -5,8 +5,9 @@ Authors@R:
   person("Joe", "Thorley", role = c("aut", "cre"), email = "joe@poissonconsulting.ca", comment = c(ORCID = "0000-0002-7683-4592"))
 Description: Generates derived parameter(s) from Monte Carlo Markov Chain (MCMC) 
     samples using R code. This allows Bayesian models to be fitted without the 
-    inclusion of derived parameters which add unnecessary complexity and 
-    slow model fitting.
+    inclusion of derived parameters which add unnecessary clutter and 
+    slow model fitting. For more information on MCMC samples see 
+    Brooks et al. (2011) <isbn:978-1-4200-7941-8>.
 License: MIT + file LICENSE
 Depends:
     R (>= 3.4.0)
@@ -22,8 +23,6 @@ Suggests:
     plyr,
     doParallel,
     testthat
-Remotes:
-    poissonconsulting/mcmcr
 URL: https://github.com/poissonconsulting/mcmcderive
 BugReports: https://github.com/poissonconsulting/mcmcderive/issues
 Encoding: UTF-8
diff --git a/README.Rmd b/README.Rmd
index bcd9f9b..83f1e48 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -26,7 +26,8 @@ knitr::opts_chunk$set(
 ## Why `mcmcderive`?
 
 `mcmcderive` is an R package to generate derived parameter(s) from Monte Carlo Markov Chain (MCMC) samples using R code.
-This is useful because it means Bayesian models can be fitted without the inclusion of derived parameters which add unnecessary complexity and slow model fitting.
+This is useful because it means Bayesian models can be fitted without the inclusion of derived parameters which add unnecessary clutter and slow model fitting.
+For more information on MCMC samples see Brooks et al. (2011)
 
 ### Parallel Chains
 
@@ -77,3 +78,7 @@ Please report any [issues](https://github.com/poissonconsulting/mcmcderive/issue
 
 Please note that the 'mcmcderive' project is released with a [Contributor Code of Conduct](https://poissonconsulting.github.io/mcmcderive/CODE_OF_CONDUCT.html).
 By contributing to this project, you agree to abide by its terms.
+
+## References
+
+Brooks, S., Gelman, A., Jones, G.L., and Meng, X.-L. (Editors). 2011. Handbook for Markov Chain Monte Carlo. Taylor & Francis, Boca Raton.
diff --git a/README.md b/README.md
index 3b736b8..1fc6c0d 100644
--- a/README.md
+++ b/README.md
@@ -23,7 +23,8 @@ status](https://www.r-pkg.org/badges/version/mcmcderive)](https://cran.r-project
 `mcmcderive` is an R package to generate derived parameter(s) from Monte
 Carlo Markov Chain (MCMC) samples using R code. This is useful because
 it means Bayesian models can be fitted without the inclusion of derived
-parameters which add unnecessary complexity and slow model fitting.
+parameters which add unnecessary clutter and slow model fitting. For
+more information on MCMC samples see Brooks et al. (2011)
 
 ### Parallel Chains
 
@@ -113,3 +114,8 @@ Please note that the ‘mcmcderive’ project is released with a
 [Contributor Code of
 Conduct](https://poissonconsulting.github.io/mcmcderive/CODE_OF_CONDUCT.html).
 By contributing to this project, you agree to abide by its terms.
+
+## References
+
+Brooks, S., Gelman, A., Jones, G.L., and Meng, X.-L. (Editors). 2011.
+Handbook for Markov Chain Monte Carlo. Taylor & Francis, Boca Raton.
diff --git a/docs/index.html b/docs/index.html
index 56bbf5f..0f2f485 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -13,8 +13,9 @@
 <script src="pkgdown.js"></script><meta property="og:title" content="Derive MCMC Parameters">
 <meta property="og:description" content="Generates derived parameter(s) from Monte Carlo Markov Chain (MCMC) 
     samples using R code. This allows Bayesian models to be fitted without the 
-    inclusion of derived parameters which add unnecessary complexity and 
-    slow model fitting.">
+    inclusion of derived parameters which add unnecessary clutter and 
+    slow model fitting. For more information on MCMC samples see 
+    Brooks et al. (2011) &lt;isbn:978-1-4200-7941-8&gt;.">
 <meta name="twitter:card" content="summary">
 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
@@ -85,7 +86,7 @@
 <div id="why-mcmcderive" class="section level2">
 <h2 class="hasAnchor">
 <a href="#why-mcmcderive" class="anchor"></a>Why <code>mcmcderive</code>?</h2>
-<p><code>mcmcderive</code> is an R package to generate derived parameter(s) from Monte Carlo Markov Chain (MCMC) samples using R code. This is useful because it means Bayesian models can be fitted without the inclusion of derived parameters which add unnecessary complexity and slow model fitting.</p>
+<p><code>mcmcderive</code> is an R package to generate derived parameter(s) from Monte Carlo Markov Chain (MCMC) samples using R code. This is useful because it means Bayesian models can be fitted without the inclusion of derived parameters which add unnecessary clutter and slow model fitting. For more information on MCMC samples see Brooks et al. (2011)</p>
 <div id="parallel-chains" class="section level3">
 <h3 class="hasAnchor">
 <a href="#parallel-chains" class="anchor"></a>Parallel Chains</h3>
@@ -162,6 +163,11 @@ <h2 class="hasAnchor">
 <p><a href="https://github.com/poissonconsulting/mcmcderive/pulls">Pull requests</a> are always welcome.</p>
 <p>Please note that the ‘mcmcderive’ project is released with a <a href="https://poissonconsulting.github.io/mcmcderive/CODE_OF_CONDUCT.html">Contributor Code of Conduct</a>. By contributing to this project, you agree to abide by its terms.</p>
 </div>
+<div id="references" class="section level2">
+<h2 class="hasAnchor">
+<a href="#references" class="anchor"></a>References</h2>
+<p>Brooks, S., Gelman, A., Jones, G.L., and Meng, X.-L. (Editors). 2011. Handbook for Markov Chain Monte Carlo. Taylor &amp; Francis, Boca Raton.</p>
+</div>
 </div>
   </div>
 
diff --git a/man/mcmcderive-package.Rd b/man/mcmcderive-package.Rd
index 6610e46..4ac0fc7 100644
--- a/man/mcmcderive-package.Rd
+++ b/man/mcmcderive-package.Rd
@@ -8,8 +8,9 @@
 \description{
 Generates derived parameter(s) from Monte Carlo Markov Chain (MCMC) 
     samples using R code. This allows Bayesian models to be fitted without the 
-    inclusion of derived parameters which add unnecessary complexity and 
-    slow model fitting.
+    inclusion of derived parameters which add unnecessary clutter and 
+    slow model fitting. For more information on MCMC samples see 
+    Brooks et al. (2011) <isbn:978-1-4200-7941-8>.
 }
 \seealso{
 Useful links: