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The objective of this issue is to highlight the work necessary to change the underlying structure of the GMWM package so that it is more flexible for future extensions.
Principally, there are three key abstractions that must occur:
The numerical optimization library must be broken away from base R's optim function and abstracted. To achieve this, there are three routes to consider:
Integration with RcppMLPACK, the present work on this would mandate a more complex install process than one would hope
Creation of RcppOptim that is arma focused and borrows heavily upon the tenets of optimization library found in MLPACK.
Switch to RcppEigen to take advantage of RcppNumerical
Move from a function-based interface to a class-based interface potentially inspired by policy-based design
Required classes:
TS Object: SARIMA, AR1, GM, RW, WN, QN, DR
WV Decomp, Filters
GMWM Routine
Classes would be exposed to R using RcppR6 modules.
Creation of a virtual package named gmwm that links individual method packages and the underlying computational core gmwmcore
The text was updated successfully, but these errors were encountered:
The objective of this issue is to highlight the work necessary to change the underlying structure of the GMWM package so that it is more flexible for future extensions.
Principally, there are three key abstractions that must occur:
optim
function and abstracted. To achieve this, there are three routes to consider:RcppMLPACK
, the present work on this would mandate a more complex install process than one would hopeRcppOptim
that isarma
focused and borrows heavily upon the tenets of optimization library found in MLPACK.RcppEigen
to take advantage ofRcppNumerical
RcppR6
modules.virtual
package namedgmwm
that links individual method packages and the underlying computational coregmwmcore
The text was updated successfully, but these errors were encountered: