diff --git a/README.Rmd b/README.Rmd
index f92f695..eff68d6 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -19,14 +19,28 @@ knitr::opts_chunk$set(
 [![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
 <!-- badges: end -->
 
-`drmr` (pronounced _drummer_) is an `R` package for fitting dynamic range
-models. Inference is carried out in a Bayesian framework via Markov Chain Monte
-Carlo (MCMC) samples available in `Stan`.
+`drmr` (pronounced *drummer*) is an `R` package for fitting dynamic range models
+to spatiotemporal data on species abundance. Dynamic range models are spatial
+population models in which demographic rates (e.g., reproduction or mortality)
+are influenced by the environment. Inference is carried out in a Bayesian
+framework via Markov Chain Monte Carlo (MCMC) samples available in `Stan`.
+
+For details, please see Vignettes linked below and [da Cunha Godoy et
+al. 2026](https://ecoevorxiv.org/repository/view/12564/).
 
 ### Installation
 
 The installation of the development version from GitHub can be done via
-```r
+
+``` r
 remotes::install_github("pinskylab/drmr")
 ## or devtools::install_github("pinskylab/drmr")
 ```
+### Vignettes
+
+* [Get started](https://pinskylab.github.io/drmr/articles/get-started.html)
+* [Theoretical background](https://pinskylab.github.io/drmr/articles/theory.html)
+* [Algorithms](https://pinskylab.github.io/drmr/articles/algos.html)
+* [Initializing densities](https://pinskylab.github.io/drmr/articles/init.html)
+* [Parameterization of the density functions](https://pinskylab.github.io/drmr/articles/parametrization.html)
+* [Advanced features](https://pinskylab.github.io/drmr/articles/advanced-features.html)
diff --git a/README.md b/README.md
index a82c91f..a48eb43 100644
--- a/README.md
+++ b/README.md
@@ -9,8 +9,14 @@ experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](h
 <!-- badges: end -->
 
 `drmr` (pronounced *drummer*) is an `R` package for fitting dynamic
-range models. Inference is carried out in a Bayesian framework via
-Markov Chain Monte Carlo (MCMC) samples available in `Stan`.
+range models to spatiotemporal data on species abundance. Dynamic range
+models are spatial population models in which demographic rates (e.g.,
+reproduction or mortality) are influenced by the environment. Inference
+is carried out in a Bayesian framework via Markov Chain Monte Carlo
+(MCMC) samples available in `Stan`.
+
+For details, please see Vignettes linked below and [da Cunha Godoy et
+al. 2026](https://ecoevorxiv.org/repository/view/12564/).
 
 ### Installation
 
@@ -20,3 +26,17 @@ The installation of the development version from GitHub can be done via
 remotes::install_github("pinskylab/drmr")
 ## or devtools::install_github("pinskylab/drmr")
 ```
+
+### Vignettes
+
+- [Get
+  started](https://pinskylab.github.io/drmr/articles/get-started.html)
+- [Theoretical
+  background](https://pinskylab.github.io/drmr/articles/theory.html)
+- [Algorithms](https://pinskylab.github.io/drmr/articles/algos.html)
+- [Initializing
+  densities](https://pinskylab.github.io/drmr/articles/init.html)
+- [Parameterization of the density
+  functions](https://pinskylab.github.io/drmr/articles/parametrization.html)
+- [Advanced
+  features](https://pinskylab.github.io/drmr/articles/advanced-features.html)