[](https://cran.r-project.org/package=biomod2)
[](https://github.com/biomodhub/biomod2)
[](https://github.com/biomodhub/biomod2/commits/master)
[](https://github.com/biomodhub/biomod2/actions/workflows/R-CMD-check.yml)

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Species distribution modeling,
calibration and evaluation,
ensemble modeling
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https://biomodhub.github.io/biomod2/
### Installation
- **Stable version** [](https://cran.r-project.org/package=biomod2) from [cran](https://CRAN.R-project.org/package=biomod2) :
```R
install.packages("biomod2", dependencies = TRUE)
```
- **Development version** [](https://github.com/biomodhub/biomod2) from [biomodhub](https://github.com/biomodhub/biomod2) :
```R
library(pak)
pak::pak("biomodhub/biomod2", dependencies = TRUE)
```
All changes between versions are detailed in [News](https://biomodhub.github.io/biomod2/articles/news.html).
A new video tutorial is available on the [Video page](https://biomodhub.github.io/biomod2/articles/videos_2_biomod2.html).
### `biomod 4.3-4-8` - Abundance modeling
Please **feel free to indicate if you notice some strange new behaviors** !
#### What is changed ?
- Nothing for presence/absence (or presence-only) modelling.
- `bm_RangeSize` computes the difference between binary predictions,
and is called by `BIOMOD_RangeSize` which can take as input `BIOMOD.projection.out` objects.
- Vignettes and example tutorials have been updated.
#### What is new ?
- **Non-binary** data ! Check the [Main functions (abund)](https://biomodhub.github.io/biomod2/articles/examples_1_mainFunctions_AB.html) page for examples.
- 6 values can be given to `data.type` new parameter in `BIOMOD_FormatingData` :
*binary*, *count*, *multiclass*, *ordinal*, *relative*, *abundance*.
- 2 new ensemble models have been added to deal with qualitative data type : `EMmode` and `EMfreq`.
- **DNN** (Deep Neural Network) model has been added through `cito` package. It can be used with all data types.
Be sure to have a look at [cito documentation](https://citoverse.github.io/cito/reference/cito.html) before, especially the part about the `torch` installation.
- `bm_ModelAnalysis` renders classical graphics to explore the quality of single models
- `BIOMOD_Report` renders `.html` reports to help summarize modeling steps
### `biomod 4.2-6` - Improved OptionsBigBoss and new model
#### What is changed ?
- To improve the models, we made some change in the options for [**`OptionsBigboss`**](https://biomodhub.github.io/biomod2/reference/OptionsBigboss.html). (This only affects the ANN, CTA and RF models.) You can check all your options with the `get_options()` function.
- To reduce the tuning calculation time, we update the tuning ranges for ANN, FDA and MARS models.
#### What is new ?
- `biomod2` has a new model: **RFd**. It's a Random Forest model with a down-sampling method.
- You can now define _seed.val_ for `bm_PseudoAbsences()` and `BIOMOD_FormatingData()`.
- New _fact.aggr_ argument, for pseudo-absence selection with the random and disk methods, allows to reduce the resolution of the environment.
- Possibility to give the same options for all datasets with _"for_all_datasets"_ in `bm_ModelingOptions()`.
### `biomod 4.2-5` - Modeling options & Tuning Update
#### What is changed ?
- modeling options are now automatically retrieved from single models functions, normally allowing the use of all arguments taken into account by these functions
- tuning has been cleaned up, but keep in mind that it is still a quite long running process
- in consequence, `BIOMOD_ModelingOptions` and `BIOMOD_Tuning` functions become secondary functions (`bm_ModelingOptions` and `bm_Tuning`), and modeling options can be directly built through `BIOMOD_Modeling` function
#### What is new ?
- `ModelsTable` and `OptionsBigboss` datasets (*note that improvement of bigboss modeling options is planned in near future*)
- 3 new vignettes have been created :
- [data preparation](https://biomodhub.github.io/biomod2/articles/vignette_dataPreparation.html) (*questions you should ask yourself before modeling*)
- [cross-validation](https://biomodhub.github.io/biomod2/articles/vignette_crossValidation.html) (*to prepare your own calibration / validation datasets*)
- [modeling options](https://biomodhub.github.io/biomod2/articles/vignette_modelingOptions.html) (*to help you navigate through the new way of parametrizing single models*)
### `biomod 4.2` - Terra Update
#### What is changed ?
- `biomod2` now relies on the new [`terra`](https://github.com/rspatial/terra) package that aims at replacing `raster`and `sp`.
- `biomod2` is still compatible with old format such as `RasterStack`and `SpatialPointsDataFrame`.
- `biomod2` function will sometimes return `SpatRaster` from package `terra` that you can always convert into `RasterStack` using function `stack` in `raster`.
### `biomod 4.1` is now available
`/!\` Package fresh start... meaning some changes in function names and parameters. We apologize for the trouble `>{o.o}<`
Sorry for the inconvenience, and please **feel free to indicate if you notice some strange new behaviors** !
#### What is changed ?
- all code functions have been cleaned, and old / unused functions have been removed
- function names have been standardized (`BIOMOD_` for main functions, `bm_` for secondary functions)
- parameter names have been standardized (same typo, same names for similar parameters across functions)
- all documentation and examples have been cleaned up
#### What is new ?
- plot functions have been re-written with `ggplot2`
- [`biomod2` website](https://biomodhub.github.io/biomod2/) has been created, with proper `roxygen2` documentation and help vignettes
#### But... why ?
- “*For every minute spent on organizing, an hour is earned.*” — Benjamin Franklin
- better documentation, better formation, better help provided
- new improvements to come (update of single models, implementation of abundance models, etc)