R-side code to implement an R editor and IDE in Komodo IDE
  with the SciViews-K extension.

In practice, it is difficult to determine the number of decomposition modes, K, for Variational Mode Decomposition (VMD). To overcome this issue, this study offers Spearman Variational Mode Decomposition (SVMD), a method that uses the Spearman correlation coefficient to calculate the ideal mode number. Unlike the Pearson correlation coefficient, which only returns a perfect value when X and Y are linearly connected, the Spearman correlation can be calculated without knowing the probability distributions of X and Y. The Spearman correlation coefficient, also called Spearman's rank correlation coefficient, is a subset of a wider correlation coefficient. As VMD decomposes a signal, the Spearman correlation coefficient between the reconstructed and original sequences rises as the mode number K increases. Once the signal has been fully decomposed, subsequent increases in K cause the correlation to gradually level off. When the correlation reaches a specific level, VMD is said to have adequately decomposed the signal. Numerous experiments revealed that a threshold of 0.997 produces the best denoising effect, so the threshold is set at 0.997. This package has been developed using concept of Yang et al. (2021)<doi:10.1016/j.aej.2021.01.055>.

Functions required for the 'SciViews::R' dialect or for general
  use: manage a temporary environment attached to the search path, define
  synonyms for R functions using aka(), test if 'Aqua', 'Mac', 'Win' ... Show
  progress bar, etc.

Implements the SVM-Maj algorithm to train data with support vector machine
  <doi:10.1007/s11634-008-0020-9>.
  This algorithm uses two efficient updates, one for linear kernel and one 
  for the nonlinear kernel.

Computes the entire regularization path for the two-class svm classifier
		with essentially the same cost as a single SVM fit.

Determines networks of significant synchronization between the discrete states of nodes; see Tumminello et al <doi:10.1371/journal.pone.0017994>.