initialize-method: Initialize an instance of a fitSpectra S4 class.
In asmitapoddar/BayesSentinel: Bayes Classification of Spetrometric Data

Initialization method of the fitSpectra class.

Initialization method of the predictClass class.

Initialization method of the simulateSpectra class.

## S4 method for signature 'fitSpectra'
initialize(.Object, m = list(0),
  modelname = "full", spectra = "diag", time = "diag",
  kerneltypeSpectra = "exponential", kerneltypeTime = "exponential",
  h = 10, s = 0.01, lambdaS = 0.3, lambdaT = 0.3,
  validation = FALSE, listLambdaS = seq(from = 0.1, to = 0.3, by =
  0.1), listLambdaT = seq(from = 0.1, to = 0.3, by = 0.1),
  model = "gaussian")

## S4 method for signature 'predictClass'
initialize(.Object, m = list(0),
  fittedCov = list(0), lambdaS = 0.3, lambdaT = 0.3,
  model = "gaussian", validation = FALSE, listLambdaS = seq(from =
  0.1, to = 10, by = 0.1), listLambdaT = seq(from = 0.1, to = 10, by =
  0.1))

## S4 method for signature 'simulateSpectra'
initialize(.Object, nbPixel = 10000,
  nbCluster = 15, nbSpectrum = 10, nbSampling = 33,
  sigma = rexp(nbSpectrum), times = c(0, 10, 20, 30, 40, 50, 60, 70,
  80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220,
  230, 240, 250, 260, 270, 280, 290, 300, 310, 321), width = 50,
  simulationType = "gaussian", modelname = "parsimonious",
  kernelSpectra = "gaussian", kernelTime = "gaussian")

`.Object`	object of class fitSpectra
`m`	spectroscopic data
`modelname`	name of model to be used for calculating the covariance matrix. Available models are "full", "parsimonious". Default is "full".
`spectra`	type of spectra. Available models are "diag", "unknown" and "kernel". Default is "diag".
`time`	type of time. Available models are "diag", "unknown" and "kernel". Default is "diag".
`kerneltypeSpectra`	kernel to be used for covariance matrix of spectra Available kernels are "epanechnikov", "gaussian", "exponential", "uniform", "quadratic", "circular", "triangular", "rational quadratic", "inverse multiquadratic". Default is "exponential".
`kerneltypeTime`	kernel to be used for covariance matrix of time Available kernels are "epanechnikov", "gaussian", "exponential", "uniform", "quadratic", "circular", "triangular", "rational quadratic", "inverse multiquadratic". Default is "exponential".
`h`	used for kernel calculation
`s`	regularisation paramater for flip flop algorithm
`lambdaS`	regularisation for spectra for flip flop algorithm
`lambdaT`	regularisation for spectra for flip flop algorithm
`validation`	to optimize lambda or not
`listLambdaS`	list of lambdaS used in prediction in case validation is TRUE
`listLambdaT`	list of lambdaT used in prediction in case validation is TRUE
`model`	use in prediction in case of validation is TRUE
`fittedCov`	fitted covariance matrix for the data
`nbPixel`	number of pixels belonging to class k
`nbCluster`	number of cluster
`nbSpectrum`	number of spectra
`nbSampling`	number of sampling
`sigma`	a vector of size nbSpectrum giving the variance level of the spectrum
`times`	time intervals of the simulation
`width`	the width of the kernel to use for "gaussian" simulation. Default is 50.
`simulationType`	type of simulation. Available options are "gaussian" and "tstudent". Default is "gaussian".
`kernelSpectra`	type of kernel to be used to simulate spectra. Available options are "diag", ""epanechnikov", "gaussian", "exponential", "uniform", "quadratic" , "circular", "triangular", "rational quadratic", "inverse multiquadratic". Default is "gaussian".
`kernelTime`	type of kernel to be used for simulating time. Available options are "diag", ""epanechnikov", "gaussian", "exponential", "uniform", "quadratic", "circular", "triangular", "rational quadratic", "inverse multiquadratic". Default is "gaussian".
`.Object`	object of class predictClass
`m`	spectroscopic data
`lambdaS`	parameter for regularisation of spectra
`lambdaT`	parameter for regularisation of time
`model`	type of model to be used for prediction of labels Available models are "gaussian", "tstudent". Default is "gaussian".
`validation`	logical to optimize the lambda.
`predicted_labels`	predicted class labels
`accuracy`	accracy of prediction
`.Object`	object of class simulateSpectra
`modelname`	type of model to be used to build covariance matrix. Available options are "full" and "parsimonious". Default is "full".
`nbSampling`	number of time intervals of the simulation
`gamma`	degrees of freedom used for simulating "tstudent" distribution of data. Default is 3.
`labels`	class labels of the data
`result`	return a list of simulated data