RStoolbox 0.1.3
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New:
* new logical argument `predict` for superClass. Disables prediction of full raster (validation is still conducted).
* new generic predict() function for superClass objects. Useful to separate model training and prediction. 
* new example data set (landcover training polygons) for lsat example data under /extdata/trainingPolygons.rds 

Fixes:
* fix histMatch for single layers (affected also 'ihs' pan-sharpening)
* fix superClass validation sampling for factors (character based factors could lead to wrong factor conversions and wrong validation results)
* improved handling of of training polygons with overlaps and shared borders in superClass
* improved checks and error messages for insufficient training polygons

RStoolbox 0.1.2
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New:
New model for superClass: maximum likelihood classification (model = "mlc")

Fixes:
* Restrict calculation of EVI/EVI2 to reflectance data (#3)
* Enforce valid value ranges in radCor: radiance: [0,+Inf], reflectance: [0,1]. Includes a new argument `clamp` to turn this on or off (on by default).

RStoolbox 0.1.1
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Added kernlab to suggested packages to be able to test \donttest{} examples

RStoolbox 0.1.0
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Initial release to CRAN (2015-09-05) with the following functions: 
 * classifyQA()
 * cloudMask()
 * cloudShadowMask()
 * coregisterImages()
 * decodeQA()
 * encodeQA()
 * estimateHaze()
 * fortify.raster()
 * fCover()
 * getMeta()
 * ggR()
 * ggRGB()
 * histMatch()
 * ImageMetaData()
 * normImage()
 * panSharpen()
 * pifMatch()
 * radCor()
 * rasterCVA()
 * rasterEntropy()
 * rasterPCA()
 * readEE()
 * readMeta()
 * readRSTBX()
 * readSLI()
 * rescaleImage()
 * rsOpts()
 * sam()
 * saveRSTBX()
 * spectralIndices()
 * stackMeta()
 * superClass()
 * tasseledCap()
 * topCor()
 * unsuperClass()
 * writeSLI() 
 
Included example data sets: 
* data(srtm)
* data(lsat)
* data(rlogo)


