fpca_gauss_optimization: Internal main optimization for fpca_gauss
In julia-wrobel/registr: Curve Registration for Exponential Family Functional Data

Main optimization function for fpca_gauss. If npc_varExplained is specified, the function simply returns a list with elements npc (chosen number of FPCs), evalues (estimated variances of the first 'npc' FPCs) and evalues_sum (sum of the estimated variances of the first 20 FPCs, as approximation of the overall variance).

fpca_gauss_optimization(
  npc,
  npc_varExplained = NULL,
  Kt,
  maxiter,
  print.iter,
  seed,
  periodic,
  error_thresh,
  verbose,
  Y,
  rows,
  I,
  knots,
  Theta_phi,
  alpha_coefs
)

`npc`	The number of functional principal components (FPCs) has to be specified either directly as `npc` or based on their explained share of variance. In the latter case, `npc_varExplained` has to be set to a share between 0 and 1.
`npc_varExplained`	The number of functional principal components (FPCs) has to be specified either directly as `npc` or based on their explained share of variance. In the latter case, `npc_varExplained` has to be set to a share between 0 and 1.
`Kt`	Number of B-spline basis functions used to estimate mean functions and functional principal components. Default is 8. If `npc_varExplained` is used, `Kt` is set to 20.
`maxiter`	Maximum number of iterations to perform for EM algorithm. Default is 50.
`print.iter`	Prints current error and iteration
`seed`	Set seed for reproducibility. Defaults to 1988.
`periodic`	If TRUE, uses periodic b-spline basis functions. Default is FALSE.
`error_thresh`	Error threshold to end iterations. Defaults to 0.0001.
`verbose`	Can be set to integers between 0 and 4 to control the level of detail of the printed diagnostic messages. Higher numbers lead to more detailed messages. Defaults to 1.
`Y, rows, I, knots, Theta_phi, alpha_coefs`	Internal objects created in `fpca_gauss`.

list with elements t_vec, Theta_phi_mean, alpha_coefs, efunctions, evalues, evalues_sum, scores, subject_coef, fittedVals, sigma2. See documentation of fpca_gauss for details.