biomod2

BIOMOD is a computer platform for ensemble forecasting of species distributions, enabling the treatment of a range of methodological uncertainties in models and the examination of species-environment relationships.

https://github.com/biomodhub/biomod2

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
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Academic publication links
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Committers with academic emails
3 of 16 committers (18.8%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (12.9%) to scientific vocabulary

Last synced: 6 months ago · JSON representation

Repository

Basic Info

Host: GitHub
Owner: biomodhub
Language: R
Default Branch: master
Size: 29.2 MB

Statistics

Stars: 105
Watchers: 6
Forks: 28
Open Issues: 21
Releases: 9

Created almost 8 years ago · Last pushed 8 months ago

Metadata Files

Readme

README.md

Logo biomod2

------------------------------------------------------------
Species distribution modeling,
calibration and evaluation,
ensemble modeling
------------------------------------------------------------
https://biomodhub.github.io/biomod2/

Installation

Stable version from cran :

R install.packages("biomod2", dependencies = TRUE)

Development version from biomodhub :

R library(devtools) devtools::install_github("biomodhub/biomod2", dependencies = TRUE)

All changes between versions are detailed in News.

NEW video tutorial in Videos !

`biomod 4.3-4` - Abundance modelling, but better !

Please feel free to indicate if you notice some strange new behaviors !

What is changed ?

BIOMOD_RangeSize becomes bm_RangeSize and the new BIOMOD_RangeSize accepts BIOMOD.projection.out objects. See News for more information.

What is new ?

There is now a new data.type : multiclass for factor data but not ordered. It comes with two news ensemble models: EMmode and EMfreq (for the mode of the response and the frequency of that mode).
You can also welcome a new model DNN (for Deep Neural Network) with the package cito. It can be use for all datatypes. Be sure to have a look at the documentation of cito before, especially the part about the installation of torch.
Discover BIOMOD_Report, a new function to help you summarize all your modeling steps. Check the documentation and produce a beautiful report with all the information you need.

`biomod 4.3` - Abundance modelling

What is changed ?

Nothing for presences/absences (or presence-only) modelling.

What is new ?

You can now use non-binary data four your modelling. All the information can be found in the Abundance Vignette.

`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. (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-absences 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 "foralldatasets" in bm_ModelingOptions().

Main workflow

`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 (questions you should ask yourself before modeling)
- cross-validation (to prepare your own calibration / validation datasets)
- modeling options (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 package that aims at replacing rasterand sp.
biomod2 is still compatible with old format such as RasterStackand 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 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)

Owner

Name: biomod2 package, tools and scripts
Login: biomodhub
Kind: organization

Repositories: 2
Profile: https://github.com/biomodhub

GitHub Events

Total

Create event: 2
Release event: 1
Issues event: 234
Watch event: 18
Issue comment event: 450
Push event: 69
Pull request event: 7
Fork event: 6

Last Year

Create event: 2
Release event: 1
Issues event: 234
Watch event: 18
Issue comment event: 450
Push event: 69
Pull request event: 7
Fork event: 6

Committers

Last synced: 6 months ago

All Time

Total Commits: 1,105
Total Committers: 16
Avg Commits per committer: 69.063
Development Distribution Score (DDS): 0.566

Past Year

Commits: 113
Committers: 5
Avg Commits per committer: 22.6
Development Distribution Score (DDS): 0.451

Top Committers

Name	Email	Commits
MayaGueguen	m**n@g**m	480
Rémi Patin	r**n@n**r	210
rpatin	r**n@g**m	199
HeleneBlt	h**u@f**r	132
Unknown	d**2@g**m	39
rpatin	r**n@f**r	16
HÉLÈNE BLANCHETEAU	h**u@u**r	7
Rekyt	m**e@e**r	6
fbreiner	f**r@u**h	5
jamiemkass	j**s@g**m	4
Matthias Grenié	m**e@u**r	2
Damien Georges	g**d@i**r	1
HÉLÈNE BLANCHETEAU	b**e@a**r	1
magn4304	m**4@g**m	1
olivroy	5**y@u**m	1
rhijmans	r**s@g**m	1

Committer Domains (Top 20 + Academic)

univ-grenoble-alpes.fr: 2 free.fr: 2 ad.u-ga.fr: 1 iarc.fr: 1 unil.ch: 1 ens-lyon.fr: 1 github.com: 1 normale.fr: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 507
Total pull requests: 24
Average time to close issues: about 1 month
Average time to close pull requests: 2 days
Total issue authors: 241
Total pull request authors: 9
Average comments per issue: 3.15
Average comments per pull request: 0.25
Merged pull requests: 18
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 149
Pull requests: 10
Average time to close issues: 24 days
Average time to close pull requests: 1 day
Issue authors: 83
Pull request authors: 4
Average comments per issue: 1.95
Average comments per pull request: 0.1
Merged pull requests: 7
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

yuliaUU (16)
tongruiju (13)
zali37 (11)
LorenzoBernicchi (11)
ShreePoudel0 (11)
chenyongpeng1 (9)
mula-jpg (8)
GitBen01 (8)
carlosbedson (8)
guimaricato (7)
675979649 (7)
Farewe (7)
luispedrosantiago (7)
CatherineBuckland (6)
rpatin (6)

Pull Request Authors

HeleneBlt (6)
rpatin (5)
CeresBarros (3)
magn4304 (2)
MayaGueguen (2)
Rekyt (2)
olivroy (2)
elvbom (1)
rhijmans (1)

Top Labels

Issue Labels

help wanted (208) bug (146) modeling question (108) outdated (34) version 3.x.x or older (13) enhancement (12) external problem (9) devel (4)

Pull Request Labels

Packages

Total packages: 2
Total downloads:
- cran 4,517 last-month
Total docker downloads: 1,430

Total dependent packages: 7
(may contain duplicates)
Total dependent repositories: 11
(may contain duplicates)
Total versions: 24
Total maintainers: 1

cran.r-project.org: biomod2

Ensemble Platform for Species Distribution Modeling

Homepage: https://biomodhub.github.io/biomod2/
Documentation: http://cran.r-project.org/web/packages/biomod2/biomod2.pdf
License: GPL-3
Latest release: 3.5.1
published over 4 years ago

Versions: 21
Dependent Packages: 6
Dependent Repositories: 10
Downloads: 4,517 Last month
Docker Downloads: 1,430

Rankings

Forks count: 3.9%

Stargazers count: 5.8%

Dependent packages count: 7.3%

Downloads: 8.2%

Dependent repos count: 9.2%

Average: 9.9%

Docker downloads count: 24.9%

Maintainers (1)

maya.gueguen@univ-grenoble-alpes.fr

Last synced: 6 months ago

conda-forge.org: r-biomod2

Homepage: https://CRAN.R-project.org/package=biomod2
License: GPL-3.0-only
Latest release: 3.5.1
published over 4 years ago

Versions: 3
Dependent Packages: 1
Dependent Repositories: 1

Rankings

Dependent repos count: 24.4%

Dependent packages count: 29.0%

Average: 32.7%

Forks count: 36.7%

Stargazers count: 40.7%

Last synced: 6 months ago

Dependencies

DESCRIPTION cran

R >= 4.1 depends
ENMeval * imports
MASS * imports
PresenceAbsence * imports
abind * imports
dismo * imports
dplyr * imports
earth * imports
foreach * imports
gbm >= 2.1.3 imports
ggplot2 * imports
maxnet * imports
mda * imports
methods * imports
nnet * imports
pROC >= 1.15.0 imports
randomForest * imports
raster * imports
reshape * imports
reshape2 * imports
rpart * imports
sp * imports
stats * imports
utils * imports
Hmisc * suggests
car * suggests
caret * suggests
doParallel * suggests
ecospat * suggests
gam * suggests
ggpubr * suggests
mgcv * suggests
rasterVis * suggests
testthat * suggests

.github/workflows/R-CMD-check.yml actions

actions/cache v3 composite
actions/checkout v3 composite
actions/upload-artifact main composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite

biomod2

Science Score: 36.0%

Repository

Basic Info

Statistics

Metadata Files

README.md

Installation

biomod 4.3-4 - Abundance modelling, but better !

What is changed ?

What is new ?

biomod 4.3 - Abundance modelling

What is changed ?

What is new ?

biomod 4.2-6 - Improved OptionsBigBoss and new model

What is changed ?

What is new ?

biomod 4.2-5 - Modeling options & Tuning Update

What is changed ?

What is new ?

biomod 4.2 - Terra Update

What is changed ?

biomod 4.1 is now available

What is changed ?

What is new ?

But... why ?

Owner

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

cran.r-project.org: biomod2

Rankings

Maintainers (1)

conda-forge.org: r-biomod2

Rankings

Dependencies

`biomod 4.3-4` - Abundance modelling, but better !

`biomod 4.3` - Abundance modelling

`biomod 4.2-6` - Improved OptionsBigBoss and new model

`biomod 4.2-5` - Modeling options & Tuning Update

`biomod 4.2` - Terra Update

`biomod 4.1` is now available