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deforestable  

Classify RGB Images into Forest or Non-Forest
View on CRAN: Click here


Download and install deforestable package within the R console
Install from CRAN:
install.packages("deforestable")

Install from Github:
library("remotes")
install_github("cran/deforestable")

Install by package version:
library("remotes")
install_version("deforestable", "3.1.2")



Attach the package and use:
library("deforestable")
Maintained by
Dmitry Otryakhin
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-04-13
Latest Update: 2022-10-15
Description:
Implements two out-of box classifiers presented in for distinguishing forest and non-forest terrain images. Under these algorithms, there are frequentist approaches: one parametric, using stable distributions, and another one- non-parametric, using the squared Mahalanobis distance. The package also contains functions for data handling and building of new classifiers as well as some test data set.
How to cite:
Dmitry Otryakhin (2022). deforestable: Classify RGB Images into Forest or Non-Forest. R package version 3.1.2, https://cran.r-project.org/web/packages/deforestable. Accessed 07 Oct. 2026.
Previous versions and publish date:
(2026-07-09 07:31), 3.0.0 (2022-04-13 10:02), 3.1.0 (2022-10-10 13:50), 3.1.1 (2022-10-16 01:22)
Other packages that cited deforestable R package
View deforestable citation profile
Other R packages that deforestable depends, imports, suggests or enhances
Complete documentation for deforestable
Functions, R codes and Examples using the deforestable R package
Some associated functions: Class_ForestTrain . Koutparams . classify . createDataPartition . read_data . train . 
Some associated R codes: Classifiers_estimators.R . CompAcc.R . ECF_test.R . NonPar_CV.R . Par_CV.R . RcppExports.R . classes-methods.R . dataPart.R . data_handling.R . deforestable.R . nonparam.R . param.R . train.R .  Full deforestable package functions and examples
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