Changes from FRESA.CAD 1.0 to FRESA.CAD version 2.0

	+ Added c++ libraries to speed-up feature selection.
		The c++ libraries functions were written using ARMADILLO and openMP.
		+ FRESAcommons.cpp : Auxiliary functions with ARMADILLO implementations of COX, logit and linear fitting 
		+ binaryFeatureSelectionCpp.cpp: Main functions for bootstrapping, selection and estimation of 
							features confidence intervals for binary classification models.
		+ regresionFeatureSelectionCpp.cpp: Main functions for bootstrapping, selection and estimation of 
							linear models coefficients.
		+ rankInverseNormalCpp.cpp: Function to standardize features based on their ranking

    + Improvements and bug-fix across the FRESA.CAD package to deal with exceptions and zero size models.
	+ Interface changes:
		*in bootstrapValidation(...,dataframe,...) 
			"dataframe" argument renamed "data"
		*in bootstrapValidationNeRI(...,dataframe,...) 
			"dataframe" argument renamed "data"
		*in bootVarNeRIElimination(...,bootLoops=250,bootFraction=1.0,...) 
			"bootLoops" and "bootFraction" arguments renamed "loops" and "fraction" respectively.
		*in crossValidationFeatureSelection(...,dataframe,...,backBootLoops,...,bootEstimations,...) 
			"dataframe", "backBootLoops" and "bootEstimations" arguments renamed 
			"data", "elimination.bootstrap.steps" and "bootstrap.steps" respectively
		*in crossValidationNeRIFeatureSelection(...,dataframe,...,backBootLoops,...) 
			"dataframe" and "backBootLoops" arguments renamed 
			"data" and "elimination.bootstrap.steps" respectively
		*in featureAdjustment(...,dataframe,...) 
			"dataframe" argument renamed "data"
		*FRESA.Model(...,k,...) 
			"k" argument renamed "nk"
		*getKNNpredictionFromFormula(modelFormula,...,k,...) 
			"modelFormula" and "k"  arguments renamed "model.formula" and "nk" respectively
		*getVarNeRI(...,dataframe,...) 
			"dataframe" argument renamed "data"
		*getVarReclassification(...,dataframe,...)
			"dataframe" argument renamed "data"
		*heatMaps(...,dataframe,...)
			"dataframe" argument renamed "data"
		*listTopCorrelatedVariables(...,dataframe,...)
			"dataframe" argument renamed "data"
		*medianPredict(...,newdata,...,k,...)
			"newdata" and "k" arguments renamed "testdata" and "nk" respectively
		*modelFitting(model,dataframe,...)
			"model" and "dataframe" arguments renamed "mode.formula" and "data" respectively
		*NeRIBasedFRESA.Model(...,dataframe,...)
			"dataframe" argument renamed "data"
		*predictForFresa(...,newdata,type,...) -> 
			"newdata"  and "type" arguments renamed "testdata" and "predictType" respectively
		*rankInverseNormalDataFrame(varList, dataframe,..)
			"varList" and "dataframe" arguments renamed "variableList" and "data" respectively
		*ReclassificationFRESA.Model(...,dataframe,...)
			"dataframe" argument renamed "data"
		*reportEquivalentVariables(...,dataframe,...)
			"dataframe" argument renamed "data"
		*residualForNeRIs(...,newdata,...)
			"newdata" argument renamed "testData"
		*timeSerieAnalysis(...,dataframe,...)
			"dataframe" argument renamed "data"
		*uniRankVar(...,dataframe,...,FitType,..)
			"dataframe" and "FitType" arguments renamed "data" and "type" respectively
		*univariateRankVariables(...,dataframe,...,FitType,..)
			"dataframe" and "FitType" arguments renamed "data" and "type" respectively
		*updateModel(...,dataframe,...)
			"dataframe" argument renamed "data"
		*updateNeRImodel(...,dataframe,...)
			"dataframe" argument renamed "data"
			
		
		


    




