# Presentation

To follow biomod2 evolution of organization and features, the presentation below details the different steps and modules of the package.

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#### Data formatting : BIOMOD_FormatingData

- Formatting data : combine observations, coordinates and explanatory variables,
  and sample pseudo-absences for presence-only data (through bm_PseudoAbsences)

- Cross-validation : create calibration / validation datasets (through bm_CrossValidation)

- Modeling options (with the help of ModelsTable and OptionsBigboss datasets)

  - Define specific options for single models (through bm_ModelingOptions)
  - And tune with your own data (through bm_Tuning)

#### Single models : BIOMOD_Modeling

- Evaluation : transform predicted probabilities in binary values (through bm_BinaryTransformation),
  and compute evaluation metrics (through bm_FindOptimStat)

- Variables’ importance : estimate the impact of each explanatory variable on predictions (through bm_VariablesImportance)

#### Ensemble models : BIOMOD_EnsembleModeling

- Filter single models based on one evaluation metric
- Combine them according to different combinations
- And build ensemble models

#### Exploring outputs

For single models:

- Explore basic statistical assumptions like residuals’ outliers, normality and heteroscedasticity (through bm_ModelAnalysis)

For single / ensemble models:

- Evaluation : bm_PlotEvalMean, bm_PlotEvalBoxplot
- Variables’ importance : bm_PlotVarImpBoxplot
- Response curves : bm_PlotResponseCurves

#### Projecting models : BIOMOD_Projection, BIOMOD_EnsembleForecasting

- Project models onto new conditions
- Transform predicted probabilities in binary values (through bm_BinaryTransformation)

#### Species range change : BIOMOD_RangeSize

#### Report : BIOMOD_Report

- Get all prints, summaries, biomod2 object outputs and plots
- Extract modelling informations to pre-fill ODMAP protocol
- Get command lines used to call main biomod2 functions