https://github.com/bmcclintock/combining-different-data-types-in-a-single-model

Materials for the ISEC workshop 'Combining different data types in a single model' 29th June 2018

https://github.com/bmcclintock/combining-different-data-types-in-a-single-model

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Materials for the ISEC workshop 'Combining different data types in a single model' 29th June 2018

https://github.com/bmcclintock/Combining-different-data-types-in-a-single-model/blob/master/

## ISEC workshop: Combining different data types in a single model

This repo contains information and materials for the ISEC workshop 'Combining different data types in a single model' 29th June 2018.
See the (draft) program for the day [here](https://docs.google.com/document/d/1j_arfcNETRIf7pmsJ3fZxuWrI4XWUzdJJjyMIh5bIkc/edit?usp=sharing).
The rest of this page contains information and materials about the three workshop tutorials.

---

### *Rahel Sollman* - **Combining spatial capture-recapture, telemetry, and point count data**

Rahel will talk about how to integrate abundance and individual location data into spatial capture-recapture models to estimate animal abundance.
Rahel will walk us through how to carry out these analyses using the `oSCR` package ([Github](https://github.com/jaroyle/oSCR)).

#### installation

You can download the most recent version of `oSCR` from GitHub, using the `devtools` package (which is on CRAN).

```r
# install devtools if you don't already have it installed, with:
# install.packages("devtools")
devtools::install_github("jaroyle/oSCR")
```
If you're a Windows user and this the first time you've tried installing a package from GitHub, you may need to install the [RTools software](https://cran.r-project.org/bin/windows/Rtools/) (note: this is not an R package), which provides the tools you need to build packages from source code.

#### examples

[ materials to be added ]

---

### *Brett McClintock* - **Combining animal movement and environmental data with hidden Markov models**

Brett will be talking about using Hidden Markov Models to combine movement data with other data streams to infer behavioural states of animals.
Brett and Theo Michelot developed the `momentuHMM` package ([CRAN](https://CRAN.R-project.org/package=momentuHMM), [GitHub](https://github.com/bmcclintock/momentuHMM)) to make it easier to do these types of analyses.
A paper introducing momentuHMM is freely available at Methods in Ecology and Evolution ([pdf](https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/2041-210X.12995)).

#### installation

To get started, you will need to load R and install the `momentuHMM` package from CRAN.

```r
install.packages("momentuHMM")
```

#### examples

The main example Brett will work through is an analysis of Northern Fur Seal foraging trips with position uncertainty.
You can download the code for the Northern Fur Seal example analysis [here](https://raw.githubusercontent.com/bmcclintock/momentuHMM/master/vignettes/nfsExample.R)
(you can either copy-paste the code into a file, or go to `save page as ...` in your browser)

There are a couple of more advanced example analyses that you can download and play with, and that Brett might have time to talk about:
 - Modelling Loggerhead Turtle movements relative to ocean currents with position uncertainty ([link](https://raw.githubusercontent.com/bmcclintock/momentuHMM/master/vignettes/turtleExample.R))
 - Modelling Northern Fulmar movements relative to fishing vessels ([link](https://raw.githubusercontent.com/bmcclintock/momentuHMM/master/vignettes/northernFulmarExample.R)) 

---

### *Bob OHara* - **Combining presence-only, presence-absence and abundance data with point-process models**

Bob will talk about combining different types of distribution data together in a single point process model of a species' distribution.
Bob has developed a package `PointedSDMS` ([GitHub](https://github.com/oharar/PointedSDMs)) to fit these models using the [INLA](http://www.r-inla.org/) inference software.

#### installation

You can install `PointedSDMs` from GitHub:

```r
devtools::install_github("oharar/PointedSDMs")
```

You'll also need to install INLA from the INLA team's own repository, using the following code:

```r
install.packages("INLA", repos = "https://inla.r-inla-download.org/R/stable"))
```

#### examples

[ materials to be added ]

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