README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

<!-- badges: start -->

<!-- [![pages-build-deployment](https://github.com/BrunaLab/lagged-ipms/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/BrunaLab/lagged-ipms/actions/workflows/pages/pages-build-deployment) -->

<!-- DOI badges go here -->

<!-- badges: end -->

This repository contains data and code for the manuscript *"Context-dependent consequences of including delayed responses to climate in Integral Projection Models"* <!-- by Eric R. Scott, María Uriarte, and Emilio M. Bruna.  -->

We investigated the consequences of including lagged effects, such as those modeled by distributed lag non-linear models, into integral projection models (i.e., IPMs). We compared population growth rates projected with deterministic IPMs, stochastic IPMs (using 'matrix-shuffling'), and stochastic IPMs with lagged effects.

# Reproducibility

This research compendium has been developed using the statistical programming language R. To work with the compendium, you will need installed on your computer the [R software](https://cloud.r-project.org/) (v4.3 for best compatibility) itself and [RStudio Desktop](https://rstudio.com/products/rstudio/download/).

You can download the compendium as a zip file from from this URL: [master.zip](/archive/master.zip).

To run the compendium and reproduce all outputs:

-   Open the project in RStudio by double-clicking the `.Rproj` file.
-   This project uses `renv` for package management, which should be installed automatically when opening the project. Run `renv::restore()` to install all required packages (this will have the highest likelihood of succeeding if run on R v4.3.0).
-   You can run `renv::deactivate()` if you want to turn of `renv` but keep the lockfile as a record of package versions used.
-   This project uses `targets` for workflow management. The functions used in the project are defined in `R/` and the steps are defined in `_targets.R` and summarized in the schematic below. Run `targets::tar_make()` or `targets::tar_make_clustermq()` from the R console to run all code and produce all outputs.

```{r echo=FALSE, message=FALSE, warning=FALSE, results='asis'}
cat(
  "```mermaid",
  targets::tar_mermaid(targets_only = TRUE),
  "```",
  sep = "\n"
)
#this should display correctly on GitHub, or code can be pasted into https://mermaid.live
```

<!-- There is some additional technical information for collaborators on the ['notes for collaborators' page](https://brunalab.github.io/lagged-ipms/notes/index.html). The most recent draft of the manuscript is available [here](https://brunalab.github.io/lagged-ipms/docs/ipm_comparison_ms.pdf). -->

### NOTES:

-   The targets `lambda_bt_dlnm_ff` and `lambda_bt_dlnm_cf` will take a very long time to run (days), so you may want to comment those out before attempting to reproduce results.

<!-- -   If you want to make use of HPC, follow instructions [here](https://github.com/BrunaLab/hipergator-targets-ssh) to customize templates and options. -->