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README.Rmd
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README.Rmd
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---
output:
github_document:
html_preview: false
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
fig.path = "man/figures/README-"
)
```
# **shar** | **S**pecies **h**abitat **a**ssociations in **R** <img src="man/figures/logo.png" align="right" alt="" width="150" />
<!-- badges: start -->
README Last updated: `r Sys.Date()`
| CI | Development | CRAN | License |
|----|-------------|------|---------|
| [](https://github.com/r-spatialecology/shar/actions/workflows/R-CMD-check.yaml) | [](https://www.repostatus.org/#active) | [](https://cran.r-project.org/package=shar) |[](https://joss.theoj.org/papers/1b786c028a5425858cb0e5428bd9173b) |
| [](https://codecov.io/gh/r-spatialecology/shar) | [](https://www.tidyverse.org/lifecycle/#stable) | [](http://cran.rstudio.com/web/packages/shar/index.html) | [](https://www.gnu.org/licenses/gpl-3.0) |
<!-- badges: end -->
**S**pecies-**h**abitat **a**ssociations in **R** provides a toolset of functions in the `R` programming language to analyze species-habitat associations. Therefore, information about the location of the species (as a point pattern) and the environmental conditions (as a raster) is needed. In order to analyse the data for significant habitat associations either the location data or the environmental data is randomized *n*-times. Then, counts within the habitats are compared between the observed and the randomized data. Positive or negative associations are present if the observed counts are higher or lower than the randomized counts (using quantile thresholds). Methods are described in Plotkin et al. (2000), Harms et al. (2001) and Wiegand & Moloney (2014). **shar** is mainly based on the [`spatstat`](http://spatstat.org) (Baddeley et al. 2015) and [`terra`](https://rspatial.org/terra/) (Hijmans 2022) package.
You can find more information and help using the corresponding [homepage](https://r-spatialecology.github.io/shar/).
## Installation
You can install the released version of **shar** from [CRAN](https://cran.r-project.org/web/packages/shar/index.html) with:
```{r install-CRAN, eval = FALSE}
install.packages("shar")
```
And the development version from [GitHub](https://github.com/r-spatialecology/shar) with:
```{r install-github, eval = FALSE}
# install.packages("remotes")
remotes::install_github("r-spatialecology/shar")
```
This also automatically installs all non-base `R` package dependencies, namely: `classInt`, `spatstat.explore`, `spatstat.geom`, `spatstat.model`, `spatstat.random`, and `terra`.
## How to use **shar**
Please refer to `vignette("Get started")` and the [homepage](https://r-spatialecology.github.io/shar/) to get an introduction to **shar**.
### Citation
The **shar** package is part of our academic work. To cite the package or acknowledge its use in publications, please cite the following paper.
> Hesselbarth, M.H.K., (2021). shar: A R package to analyze species-habitat associations using point pattern analysis. Journal of Open Source Software, 6(67), 3811. <https://doi.org/10.21105/joss.03811>.
If you use the `reconstruct_pattern_multi()` function, please also cite.
> Wudel C., Schlicht R., Berger U. (2023). Multi-trait point pattern reconstruction of plant ecosystems. Methods in Ecology and Evolution, 14, 2668–2679. <https://doi.org/10.1111/2041-210X.14206>.
The get a BibTex entry, please use `citation("shar")`.
## Contributing and Code of Conduct
Contributions to **shar** are highly welcomed and appreciated. This includes any form of feedback, bug reports, feature requests/suggestions, or general questions about the usage.
Please note that the **shar** package is released with a [Contributor Code of Conduct](CODE_OF_CONDUCT.md). By contributing to this project, you agree to abide by its terms.
To contribute to this project, please see the [Contributing guidelines](CONTRIBUTING.md).