Imports: document, git2r, highr, knitr, reticulate, xfun

Imports: checkmate, document, git2r, highr, knitr, reticulate, xfun
Suggests: devtools, pkgload, rasciidoc, rmarkdown, rprojroot, RUnit,

Imports: checkmate, document, fritools, gert, highr, knitr, reticulate,

Imports: checkmate, document, fritools (>= 1.2.0), gert, highr, knitr,

Imports: checkmate, document (>= 3.3.0), fritools (>= 1.2.0), gert,

Imports: checkmate, document (>= 3.3.0), fritools (>= 1.3.0), gert,

Title: Supervised Raster Image Classification
Author: Daniel Wiesmann <daniel.wiesmann@ist.utl.pt> and David Quinn
Description: This package contains functions to perform supervised and
pixel based raster image classification. It can be used for
land-cover analysis in a straightforward way. Two
classification algorithms are implemented: a classifier using
Multinomial Logistic Regression and a Maximum Likelihood
Classifier (MLC). The output includes the classified raster and

Author: Daniel Wiesmann <daniel.wiesmann@ist.utl.pt> and David Quinn <djq@mit.edu>
Description: This package contains functions to perform supervised and pixel based raster image classification. It has been designed to facilitate land-cover analysis. Five classification algorithms can be used: Maximum Likelihood Classification, Multinomial Logistic Regression, Neural Networks, Random Forests and Support Vector Machines. The output includes the classified raster and standard classification accuracy assessment (accuracy matrix, overall accuracy and kappa coefficient). An option for in-sample verification is available.
Imports: methods, car, nnet, RSNNS, e1071, randomForest

Package: rasclass
Type: Package
Title: Supervised Raster Image Classification
Version: 0.2.1
Date: 2011-11-14
Author: Daniel Wiesmann <daniel.wiesmann@ist.utl.pt> and David Quinn
<djq@mit.edu>
Maintainer: Daniel Wiesmann <daniel.wiesmann@ist.utl.pt>
Description: This package contains functions to perform supervised and
pixel based raster image classification. It has been designed
to facilitate land-cover analysis. Five classification
algorithms can be used: Maximum Likelihood Classification,
Multinomial Logistic Regression, Neural Networks, Random
Forests and Support Vector Machines. The output includes the
classified raster and standard classification accuracy
assessment such as the accuracy matrix, the overall accuracy
and the kappa coefficient. An option for in-sample verification
is available.
License: GPL (>= 2)
LazyLoad: yes
Imports: methods, car, nnet, RSNNS, e1071, randomForest
Packaged: 2012-01-23 11:03:00 UTC; Tam
Repository: CRAN
Date/Publication: 2012-01-23 12:32:24