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Logo biomod2

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Species distribution modeling,
calibration and evaluation,
ensemble modeling
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https://biomodhub.github.io/biomod2/
### Installation
- **Stable version** [![v](https://www.r-pkg.org/badges/version/biomod2?color=yellow)](https://cran.r-project.org/package=biomod2) from [cran](https://CRAN.R-project.org/package=biomod2) : ```R install.packages("biomod2", dependencies = TRUE) ```
- **Development version** [![v](https://img.shields.io/badge/devel%20version-4.3--4--8-blue.svg)](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

Main workflow









### `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)