README.Rmd

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# R package unmarked

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`unmarked` is an [R](https://www.r-project.org/) package for analyzing ecological data arising from several popular sampling techniques.  The sampling methods include point counts, occurrence sampling, distance sampling, removal, double observer, and many others.  `unmarked` uses hierarchical models to incorporate covariates of the latent abundance (or occupancy) and imperfect detection processes.

## Installation

The latest stable version of unmarked can be downloaded from [CRAN](https://cran.r-project.org/package=unmarked):

```{r, eval=FALSE}
install.packages("unmarked")
```

The latest development version can be installed from Github:

```{r, eval=FALSE}
install.packages("remotes")
remotes::install_github("rbchan/unmarked")
```

## Support

Support is provided through the [unmarked Google group](http://groups.google.com/group/unmarked).
The package [website](https://rbchan.github.io/unmarked) has more information.
You can report bugs [here](https://github.com/rbchan/unmarked/issues), by posting to the Google group, or by emailing [the current maintainer](https://kenkellner.com).

## Example analysis

Below we demonstrate a simple single-season occupancy analysis using `unmarked`.
First, load in a dataset from a CSV file and format:

```{r}
library(unmarked)
wt <- read.csv(system.file("csv","widewt.csv", package="unmarked"))

# Presence/absence matrix
y <- wt[,2:4]

# Site and observation covariates
siteCovs <-  wt[,c("elev", "forest", "length")]
obsCovs <- list(date=wt[,c("date.1", "date.2", "date.3")]) 
```

Create an `unmarkedFrame`, a special type of `data.frame` for `unmarked` analyses:

```{r}
umf <- unmarkedFrameOccu(y = y, siteCovs = siteCovs, obsCovs = obsCovs)
summary(umf)
```

Fit a null occupancy model and a model with covariates, using the `occu` function:

```{r}
(mod_null <- occu(~1~1, data=umf))
(mod_covs <- occu(~date~elev, data=umf))
```

Rank them using AIC:

```{r}
fl <- fitList(null=mod_null, covs=mod_covs)
modSel(fl)
```

Estimate occupancy probability using the top-ranked model at the first six sites:

```{r}
head(predict(mod_covs, type='state'))
```

Predict occupancy probability at a new site with given covariate values:

```{r}
nd <- data.frame(elev = 1.2)
predict(mod_covs, type="state", newdata=nd)
```