--- title: "News" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{News} %\VignetteEngine{knitr::knitr} %\VignetteEncoding{UTF-8} --- ## Development updates ### 2024 ##### Bugfix - 4.2-6-2 : add `MAXENT.partitions`, `MAXENT.kfolds`, `MAXENT.user.grp` and `MAXENT.tune.args` parameters in `bm_Tuning` function - 4.2-6-1 : fixed path failure within species name (e.g. data format like `tif`) #### 4.2-6 RFd and some tuning (2024-07-22) - Warning ! Some options for `OptionsBigboss` have been modified (concerns only ANN, CTA and RF models) - Some changes for the tuning ranges. - Add `RFd` : Random Forest with a down-sampling method. - Add `seed.val` for `bm_pseudoAbsences` and `BIOMOD_FormatingData`. - Add `fact.aggr` argument for pseudo-absences selection with the random and disk methods. It allows to reduce the resolution of the environment. - Possibility to give the same options to all datasets with _"for_all_datasets"_ in `bm_ModelingOptions`. ##### Bugfix - 4.2-5-2 : + fixed `BIOMOD_EnsembleModeling` when multiple PA datasets (`obs` and `fit` not matching when calling `bm_FindOptimStat`) + always keep same order for variable names for `new.env` in `BIOMOD_Projection` and `BIOMOD_EnsembleForecasting` - 4.2-5-1 : + removed `tests/` folder (unused) + fixed check for variable names for `new.env` in `BIOMOD_Projection` and `BIOMOD_EnsembleForecasting` + fixed connection issue with `MAXENT` tuning #### 4.2-5 Modeling options and Tuning (2024-05-02) - Submitted on CRAN ##### Major Changes - Add print function for `BIOMOD.options.dataset` and `BIOMOD.models.options` classes - Add `GAM.binary.gam.gam` and `GAM.binary.mgcv.bam` in `OptionsBigboss` dataset - Add checks for `obs` and `fit` parameters in `bm_FindOptimStat` - In `bm_Tuning` function : + add `MAXENT.algorithm` and `MAXENT.parallel` parameters + be sure to update default tuning parameters if not all provided + fix tuning for `XGBOOST` and `SRE` + fix optimization of formula + switch from `gamSpline` to `gamLoess` method to tune `GAM.gam.gam` model, and add `GAM.span` and `GAM.degree` parameters - Fix multiple cases when using different settings of PA / cross-validation / modeling options through all the different secundary functions and `BIOMOD_Modeling` ##### Minor Changes - Add vignette for cross-validation (_to prepare your own calibration / validation datasets_) - Add vignette for modeling options (_to help you navigate through the new way of parameterizing single models_) - Update examples and documentation ##### Bugfix - Add `try` and error message in call for models in `bm_RunModelsLoop` - Check for predictions `>1000` - Correct `BIOMOD_EnsembleForecasting` when using `data.frame` - Correct predictions for `EMci` (removing round) ### 2023 #### 4.2-5 Modeling options and Tuning (2023-09-12) ##### Major Changes - Reformat modeling options, how they are created and how they are given to models : + Create `BIOMOD.options.default` and `BIOMOD.options.dataset` classes, retrieving default parameters and values with `formalArgs` function + Modeling options can now be different for PA x cross-validation datasets x models + Add `ModelsTable` and `OptionsBigboss` datasets containing single models informations and pre-defined modeling options + Move `BIOMOD_ModelingOptions` to `bm_ModelingOptions` + Call `bm_ModelingOptions` directly in `BIOMOD_Modeling` and add related `OPT.[...]` parameters - Move `BIOMOD_Tuning` to `bm_Tuning` and adapt it to match with new modeling options - Modify the call to single models in `bm_RunModelsLoop` in a more generalized way dealing with new modeling options ##### Minor Changes - Remove `BIOMOD_PresenceOnly` function and add `BOYCE` and `MPA` indices into `bm_FindOptimStat` function - Save ensemble projections with `FLT4S` datatype only when EMcv is activated, otherwise use `INT2S` - Add vignette for data preparation (_questions you should ask yourself before modeling_) - Correct getters (variable importance, built models) - Update examples and documentation ##### Bugfix - 4.2-4-10 : fixed `wrap` applied to a `data.frame` in `BIOMOD_Projection` - 4.2-4-9 : fixed `predict` method for `RF` with `do.classif = FALSE` - 4.2-4-8 : improved tests in `bm_PlotEvalMean` - 4.2-4-7 : fixed `do.classif` ignored in `BIOMOD_ModelingOptions` - 4.2-4-6 : fixed parallelization in `BIOMOD_Projection` - 4.2-4-5 : fixed summary method for `BIOMOD.formated.data` and `BIOMOD.formated.data.PA` - 4.2-4-4 : fixed `bm_PlotResponseCurves` for ensemble models merged by algo (for Maxent) - 4.2-4-3 : + added `point.size` argument to `plot.BIOMOD.Formated.data` + added `maxcell` argument to `plot.BIOMOD.projected.out` - 4.2-4-2 : set XGBOOST `verbose = 0` (from `verbose = 1`) - 4.2-4-1 : fixed `BIOMOD_FormatingData` checks for `resp.xy` #### 4.2-4 XGBOOST (2023-06-21) ##### Major Changes * added XGBOOST as a possible algorithm in `BIOMOD_Modeling` ##### Minor Changes * changed `CV.do.full.models` default value to `FALSE` ##### Bugfix - 4.2-3-5 : * fixed some more issues related to categorical variables badly interacting with missing values. Projection and Ensemble Forecasting are now only calculated on cells without any missing values. * removed obsolete code for SRE pseudo absences sampling with categorical variables, as SRE do not work with categorical variables * added internal function to get mask of data `.get_data_mask` - 4.2-3-4 : fixed bug in `BIOMOD_Modeling` when using `sampsize` as a vector. argument `strata` was badly formatted - 4.2-3-3 : fixed bug in `BIOMOD_EnsembleModeling` for additional projection with only one environmental variables - 4.2-3-2 : fixed bugs in `BIOMOD_EnsembleForecasting` when several projection are running simultaneously and using the same temporary directory - 4.2-3-1 : fixed bugs in `bm_CrossValidation` with `user.defined` tables badly formatted (TRUE/FALSE for data not in the given PA dataset are now properly transformed into NA) #### 4.2-3 Cross-Validation and Pseudo-Absences (2023-05-09) ##### Major Changes * Improved pseudo-absence management: it is now possible to have pseudo-absence dataset of different size and algorithm can be setup to run on different pseudo-absence dataset (with `models.pa` argument in `BIOMOD_Modeling`). * Rework and harmonization of cross-validation function. `BIOMOD_CrossValidation` have been renamed `bm_CrossValidation` and cross-validation with k-fold, stratified and environmental strategy now work properly with pseudo-absence dataset. All cross-validation strategy can now be called directly through `BIOMOD_Modeling`. ##### Minor Changes * improved Documentation (`get_evaluations`, `BIOMOD_EnsembleModeling`, `bm_RunModelsLoop`, `bm_RunModel`) * updated website tutorial to use current biomod2 version * removed unused parameter `save.output`. output are now automatically saved. * improved management of categorical raster for both `terra` and `raster` * `CV.perc` (formerly `data.split.perc`) now uses a 0-1 range (instead of 0-100) * deprecated arguments for `BIOMOD_EnsembleModeling` now gives an error. * added argument `metric.select.dataset` to `BIOMOD_EnsembleModeling` to choose the dataset which evaluation metric should be used to filter and/or weigh the ensemble models. Default value is now 'validation' instead of 'evaluation'. * added argument `na.rm` to `BIOMOD_EnsembleModeling` to harmonize the management of `NA` among individual model predictions. ##### Bugfix * validation metric calculation now properly use the calibration threshold (i.e a threshold optimized on calibration data instead of validation data). This can lead to less optimistic threshold-dependent validation metric. * fixed SRE projection assuming the same variables ordering in calibration and projection data * ensemble model can again be calculated over models without validation * correct print for multiple values in `RF$sampsize` parameter in `BIOMOD_ModelingOptions` * fixed layer name in `BIOMOD_Projection` and `BIOMOD_EnsembleForecasting` when `terraOption(todisk = TRUE)` is activated (for large or numerous raster). * fixed Ensemble Models based on models without cross-validation ("allRun") * model is now robust to using `data.table` object (that are converted into standard `data.frame`). * fixed projection raster name when using `do.stack = FALSE` and `resp.name` with `.` inside. * fixed using user-defined pseudo-absences along with `filter.raster = TRUE` in `bm_PseudoAbsence`. * fixed weights calculation when using only one pseudo-absence dataset * fixed summary and show method for `BIOMOD.formated.data.PA` ##### Internal * add internal function `get_species_data` and `get_eval_data` * removed `.BIOMOD_Modeling.prepare.data` * reorganised `bm_RunModelsLoop` to do the PA loop within the function * `calib.lines` and `eval.lines` variable names are standardised (no more `calibLines` or `eval_lines`) * removed dependency to `data.table` (removed use of `rbindlist`) * added `.get_env_class` to reduce code redundancy * renamed `categorical_stack_to_terra` into `.categorical_stack_to_terra` * dispatched some of `BIOMOD_FormatingData` checks into `bm_PseudoAbsences` #### 4.2-2 Improvement patch (2023-01-13) ##### Major Changes * `'.tif'` is available as an output format for raster projection * `'.tif'` is the new default output format for raster projection * Improved `plot` and `summary` methods for `BIOMOD_FormatingData` output. These method now support the use of `calib.lines` to explore how the cross-validation dataset are structured. * Updated `plot` methods for `BIOMOD.projection.out` objects so that it uses `ggplot2` for nicer plots. * Binary and Filtered transformation are now properly stored in `BIOMOD.projection.out` objects. They can be loaded from the disk with `get_predictions` or represented through `BIOMOD.projection.out` plot method. * `get_predictions` now return a proper `data.frame` (unless projection on spatial data) with many additional information available. Old behavior can be reproduced by using `get_predictions(x, model.as.col = TRUE)`. * `get_evaluations` now return a cleaner `data.frame` with more consistent information available. ##### Minor Changes * Simplified maxent model names: 'MAXENT.Phillips' -> 'MAXENT' (based on `maxent.jar`); 'MAXENT.Phillips.2' -> 'MAXNET' (based on `maxnet` package). * `BIOMOD_FormatingData` now gives warning when several input data points are located in the same raster cells * Added options `filter.raster` in `BIOMOD_FormatingData` to filter data points so that none are located in the same raster cells. * `BIOMOD_EnsembleModeling` now have an argument `em.algo` to select the ensemble algorithm to be computed. Separate arguments are now deprecated (`prob.mean`, `prob.median`, `prob.cv`, `prob.ci`, `committee.averaging`, `prob.mean.weight`). Building all possible ensemble models can now be done with `em.algo = c('EMmean','EMmedian','EMcv','EMci','EMca','EMwmean')`. * Some possible values for `em.by` have slightly changed: 'PA_dataset' -> 'PA', 'PA_dataset+repet' -> 'PA+run' and 'PA_dataset+algo' -> 'PA+algo' * Added an appropriate message when all models fail for `BIOMOD_Modeling` and `BIOMOD_EnsembleModeling`. ##### Bugfix * Fixed `MAXENT.Phillips.2` and single variable models. * Fixed ensemble models when several filtering metrics were asked and some combination of ensemble/metrics had no models * Bugfix for projection for ensemble models to ensure that the proper set of models was selected * Bugfix for `BIOMOD_CrossValidation` for block-stratified sampling * Bugfix for `BIOMOD_CrossValidation` for pseudo-absences ##### Internal Changes * Array have disappeared from most internal functions * Removed `rasterVis` from `Suggests` * Added `tidyterra` and `ggtext` to `Suggests` * Added checks to `get_evaluation` when models have no evaluations. ### 2022 #### 4.2-1 Bugfix patch ##### Major Changes * Package `sp` is back into `Imports` due to the need to use `sp::read.asciigrid` * Added control for `terra` version number (>= 1.6-33) as `terra` 1.6-41 was released on CRAN. * With `do.stack = TRUE`, only stacked projection are now saved to the disk. ##### Minor Changes * Added `initial_heap_size` and `max_heap_size` in `MAXENT.Phillips` modeling options * Improved projection efficiency for raster with `MAXENT.Phillips`. ##### Bugfix * Fixed `MAXENT.Phillips` predict method for large dataset (require `sp::read.asciigrid`). * Fixed ensemble models using a single PA dataset with `em.by = 'all'` or `'algo'`. * Models using repetition dataset cannot be merged anymore with models using Full dataset in `BIOMOD_EnsembleModeling`. * Fixed error in `BIOMOD_EnsembleForecasting` when a single evaluation metric was available and binary/filtered transformation were asked for. * Bugfix for plot method for `BIOMOD.formated.data.PA` object. * Fixed `MAXENT.Phillips` for Windows. * Fixed using `do.stack = FALSE` with `BIOMOD_Projection`. * Fixed `EMcv` ensemble modeling for `data.frame` by removing dependency to `raster::cv`. * Fixed `free` method with `PackedSpatRaster` * Fixed `BIOMOD_FormatingData` in case where no coordinates are given ##### Internal * Updated github workflow : removed obsolete ubuntu 18.04 ; added test on r-devel ; added cache for R packages. * Updated `MAXENT.Phillips` `predict2` method for `SpatRaster` so that it saves environmental data as `.asc` and do not use the `data.frame` method. * Fixed some automatic boolean conversion * Cleaned up `BIOMOD.formated.data@data.mask` slot. `data.mask` can now be safely saved and re-opened ; `data.mask` can now store a different extent for evaluation dataset #### 4.2-0 Terra Update ##### Major Changes * Package now rely only on `terra` (`> 1.6.33`) and do not automatically import `raster` and `sp`. * Moved `raster` and `sp` package into `SUGGESTS` rather than `DEPENDS`. * `raster` and `sp` input data type are still supported. * Package dataset now are now documented and loaded with `data()` . ##### Minor Changes * `bm_BinaryTransformation` now always returns `0`/`1` and never `TRUE`/`FALSE` * Added a check to `bm_PlotResponseCurves` for `new.env` possible data types. * `BIOMOD_Projection` and `BIOMOD_EnsembleForecasting` now properly support matrix as `new.env` * `get_prediction` on `biomod.projection.out` generated from `BIOMOD_Projection` based on `SpatRaster` with arg `as.data.frame = TRUE` are now possible. * `bm_BinaryTransformation` now return same type of object as its input * Improved communication for `BIOMOD_RangeSize`, indicating how comparison are done depending on the number of models in current vs future. * Added argument check for `BIOMOD_CrossValidation` ##### Bugfix * MAXENT.Phillips models can now properly fail * bugfix for `bm_BinaryTransformation` with `data.frame`/`matrix` and `do.filtering = TRUE` * Removed obsolete warning about CTA and categorical variables when using raster * `bm_PlotResponseCurves` now work with factors in univariate representation * `bm_PlotResponseCurves` properly handles `SpatRaster` and `Raster` as `new.env` * Bugfix for predictions with `MAXENT.Phillips` and a single environmental variable * Bugfix for `BIOMOD_EnsembleForecasting` so that it properly accounts for `new.env.xy` when projecting on `matrix` or `data.frame`. * `BIOMOD_EnsembleModeling` now works when called for a single ensemble model * Improved argument check for `BIOMOD_RangeSize`. Comparisons with non-binary values throw errors. * bugfix for `BIOMOD_RangeSize` and data.frame method * `BIOMOD_RangeSize` `data.frame` method now handles 1 current vs n future projection * bugfix for `BIOMOD_PresenceOnly` that can now work when evaluation data are provided * bugfix for `BIOMOD_PresenceOnly` that can now work when only the EM have been provided * expanded support for `BIOMOD_PresenceOnly` to `SpatRaster` and `SpatVector`. * `build_clamping_mask` now support categorical variables * fixed ensemble model EMcv based on a single environmental variable ##### Internal Changes * New internal function `.categorical2numeric` to transform categorical variables into numeric within a `data.frame`. * New internal function `.get_categorical_names` to retrieve categorical variable names from a `data.frame`. * Split `load_stored_object` method into a method for `BIOMOD.stored.SpatRaster` and a method for all other `BIOMOD.stored.data`. * `BIOMOD.stored.SpatRaster` stores `PackedSpatraster` and not `SpatRaster`. * New internal function `.CompteurSp` based on old function CompteurSp that was defined within a function. * Removed obsolete function `check_data_range()`. #### 4.1-3 ##### Bugfix * Ensemble models that fails (e.g. EMcv with only one models) will not crash the full ensemble run. Instead a warning is generally displayed at the beginning and the resulting object will list failed models. * Fixed CTA raster prediction for categorical variables. * Fixed binary transformation in `BIOMOD_EnsembleForecasting`. #### Internal Changes * New internal function .get_kept_models to generate list of models kept by ensemble modeling depending on `metric.select`. * Improved checks for `BIOMOD_EnsembleModeling` to generate warnings when ensemble models are expected to be run with <= 1 models. * Repaired support for cross-validation table given as `data.frame` instead of `matrix`. #### 4.1-2 (2022-09-29) ##### Major changes * `dir.name` can now be provided as project argument so that results may be saved in a custom folder. * `predict` with `CTA` algorithm and categorical variables on raster is now possible. * Changed evaluation for EM models merging PA datasets (`em.by = "algo"` or `em.by = "all"`) so that evaluation uses the union of PA data sets instead of the whole environmental space supplied. ##### Minor changes * Individual EM models projected as raster are now saved with `INT2S` data format when `on_0_1000` is set to `TRUE`. * Homogenize the use of load functions (*use* `get_[...]`*,* `load_stored_object` *and* `BIOMOD_LoadModels`*, instead of* `get(load(...))`) and the workflow within `get_[...]` functions (*use* `load_stored_object` *and similar arguments such as* `as.data.frame`*,* `full.name`*, ...*). * Adapting predict workflow to properly use S4 class. * Homogenize `BIOMOD.ensemble.models.out` and `BIOMOD.models.out` objects * Add slots in `BIOMOD.ensemble.models.out` object for evaluations, variables importance and predictions. * Moved `.Models.save.objects` in `BIOMOD_modeling` to `.fill_BIOMOD.models.out` in `biomod2_internal.R`. * Save slots within `BIOMOD.ensemble.models.out` and use `load_stored_object` to directly get them within `get_[...]` functions. ##### Bug Fix * Validation data can now be properly combined with Pseudo-Absence in `BIOMOD_FormatingData`, instead of throwing an error linked to `data.mask`. * Argument `on_0_1000` can now be passed without errors so that projection may either be on a range from 0 to 1 or from 0 to 1000. The latter option being more effective memory-wise. * Completed argument check for function `BIOMOD_EnsembleModeling` so that `em.by` can not be of `length > 1`. * Corrected function `.get_models_assembling` so that it did not confound `MAXENT.Phillips2` with `MAXENT.Phillips` when grouping models by algorithm in `BIOMOD_EnsembleModeling`. * `get_predictions` method for `BIOMOD.ensemble.models.out` now accepts an `evaluation` arg. Evaluation values, variables' importance and Calibration/Evaluation predictions for ensemble models are now properly saved by `BIOMOD_EnsembleModeling()`. * Evaluation metrics are no longer calculated for models `prob.ci.inf` et `prob.ci.sup`. * Package now properly pass R CMD check. * `BIOMOD_PresenceOnly` now properly manage `NA`. * Corrected `bm_PlotResponseCurves` to only plot `show.variables`. * `get_predictions.BIOMOD.projection.out` now properly works when asked for a subset of model. ##### Miscellaneous * Using models with a single predictor requires updating `gbm` package to its development version at rpatin/gbm can be used. (see issue **) #### 4.1-1 (2022-08-30) * add `do.progress` parameter (to render or not progress bar) and `dir.name` parameter in `BIOMOD_FormatingData` and `biomod2` objects (**Mathieu B. request**) * fix `BIOMOD_PresenceOnly` function by removing `ecospat` dependency #### 4.1 (2022-07-12) * fix bugs following major release 4.0 #### 4.0 (2022-03-01) * MAJOR RELEASE * clean all functions, reorganize files, remove old / unused functions * standardize function names and parameter names * update `roxygen2` documentation for all functions, including examples * create github website to host documentation, examples, vignettes, news ### 2021 #### 3.5-3 (2021-11-02) * clean BIOMOD classes definitions and functions (`biomod2_classes` files) * clean `BIOMOD_FormatingData` function * clean `BIOMOD_ModelingOptions` function * fix `BIOMOD_FormatingData` : test class condition only a first element (to deal with `matrix` / `array` objects) * fix `BIOMOD_EnsembleForecasting` for `EMcv` model when only one single model was kept #### 3.5-2 (2021-10-18) * fix `BIOMOD_PresenceOnly` function (previously `BIOMOD_presenceonly`) * fix `BIOMOD_CrossValidation` function (previously `BIOMOD_cv`) * fix internal function to find `MinMax` values, when factor included : should get clamping mask to work ### 2018-2019 #### 3.3-20 (2019-03-05) * Remove maxent Tsurukoa because not maintained anymore (required by CRAN team) #### 3.3-18 (2018-07-04) * fix the gbm multicore issue #### 3.3-17 (2018-04-23) * correct the single presence pseudo-absences generation bug (**Matthias G.**) ### 2016 #### 3.3-6 (2016-01-14) * add `get_predictions` function for ensemble models #### 3.3-5 (2016-01-04) * MARS models are now computed throw `earth` package (was `mda` in previous versions) * MARS now supports factorial explanatory variables * MARS now supports `formula` ### 2015 #### 3.3-4 (2015-11-04) * update `BIOMOD_tuning` function (**Frank B.**) #### 3.3-3 (2015-10-27) * force sampling of each level of factorial variables * add `betamultiplier` parameter to tune MAXENT.Phillips (**Frank B. request**) #### 3.3-00 (2015-10-05) * MAJOR RELEASE * optimize the memory consumption of projections and ensemble projections procedure * add the possibility to run `MAXENT.Phillips` with proper background data * classical version of `MAXENT` has been renamed `MAXENT.Phillips` * add a new version of MAXENT `MAXENT.Tsuruoka` #### 3.2-00 (2015-07-28) * add 3 new functions in `biomod2` (**Frank B. contribution**) * `BIOMOD_cv` to control models cross validation procedure * `BIOMOD_presenceonly` to evaluate biomod models using boyce and mpa indices * `BIOMOD_tuning` to automatically tune `BIOMOD_ModelingOptions` parameters ### 2014 #### 3.1-59 (2014-10-23) * add model evaluation scores plotting function * dependence to `ggplot2` #### 3.1-53 (2014-08-06) * new ensemble models names to be more coherent with formal models names #### 3.1-44 (2014-05-20) * possibility to use user defined function to influence the way models are weighted in weighted mean ensemble models (**thanks to Frank B.**) #### 3.1-43 (2014-05-20) * add of `as.data.frame` argument for `get_evaluations()` function to enable formal and ensemble models evaluation scores merging #### 3.1-42 (2014-05-19) * enable ensemble forecasting models selection (**thanks to Robin E.**) ### 2013 #### 3.1-17 (2013-10-23) * add parameter to control amount of memory reserved for `MAXENT` calculations (via java) (**thanks to Burke G.**) * optimization of memory consumption in models projections when `do.stack` argument is set to `FALSE` * binary and filtering projections output re-activated #### 3.1-1 (2013-09-04) * limitation of package dependencies * fairely definition of package namespace * add functions to update `biomod2` objects from a version to the current one #### 3.0.2 (2013-07-23) * new functions to evaluate a-posteriori models quality * remove weights for models scaling and set it `FALSE` by default #### 3.0.0 (2013-07-01) * MAJOR RELEASES * ensemble models are now `biomod2` models objects (should be predicted, evaluated, and you can do variables importance) the same way than all formal `biomod2` models * possibility to produce ensemble models response plot * ensemble forecasting output is now a `biomod2_projection` object: should be plotted... * ensemble forecasting is now doable without doing previous projections (even if it is still advised). Can work with raw explanatory variables * getter and setter function have been renamed * new `variable_importance` function * ... #### 2.1.37 (2013-06-12) * change (temporally?) gam default package from `mgcv` to `gam` to deal with memory (cache) over-consuming (**thanks to Burke G.**) * update of `response.plot2` function (optimization + deal with factorial variables) #### 2.1.32 (2013-05-30) * weights for user defined pseudo-absences are now supported (**thanks to Rui F.**) * deal with unknown factors predictions (**thanks to Denis M.**) #### 2.1.13 (2013-03-06) * Add `ProbDensFunc()` function to package to produce nice plots that show inter-models variability #### 2.1.12 (2013-03-04) * add `rasterVis` dependency for nicer `biomod2` plots * `PA.dist.min` and `PA.dist.max` are now defined in meters when you work with unprojected rasters in disk pseudo absences selection #### 2.1.9 (2013-02-28) * possibility to indicate manually which data should be used for calibration (resp. for validation) of models within `BIOMOD_Modeling` #### 2.1.9 (2013-02-27) * one var modeling supported (**thanks Anne O.**) * new options for response curves plotting (`col`, `lty`, `data_species`...) #### 2.1.8 (2013-02-25) * response plot supports now formal models #### 2.1.0 (2013-02-21) * MAJOR RELEASE * CRAN SUBMISION * add of a `modeling.id` arg (`BIOMOD_Modeling`) for prevent from no wanted models overwriting and facilitate models tests and comparisons (**thanks Frank B.**) * change of `biomod2` dataset * vignettes and help files update (**thanks Sam P. & Signe N.**) * save link between modeling and projection objects * add `pROC` package dependency * add a modeling cleaner that remove modeling objects from both memory and hard drive: `RemoveProperly()` #### 2.0.11 (2013-02-18) * possibility to consider a user.defined pseudo absences selection (**thanks to Signe N.**) * possibility to switch off stepwise glm selection (***thanks Frank B.**) #### 2.0.9 (2013-02-15) * automatic save on hard drive of `BIOMOD_Projection` outputs #### 2.0.8 (2013-02-14) * `BIOMOD_LoadModels` supports multiple models input * deal with `NA` in evaluation table issue (***thanks Frank B.**) #### 2.0.7 (2013-02-12) * bug on weights corrected (**thanks to Lugi M.**) #### 2.0.3 (2013-01-18) * deal with `MAXENT` categorical variables and categorical raster input #### 2.0.0 (2013-01-17) * MAJOR RELEASE * CRAN SUBMISION * models built within `biomod2` are now defined as "biomod2 models objects" (own scaling models, own predict function, ...) * full paths are replaced by relative paths to favor portability * harmonization of names of objects that are saved on hard drive (more coherence between functions) * possibility to save projections directly in raster format (`.grd` or `.img`) ### Year 0 #### 1.x.x * development phase