Added high performance PLS Cox backends:
big_pls_cox_fast() for exact PLS Cox fits on both dense matrices and bigmemory::big.matrix objects.
big_pls_cox_gd() for gradient based optimisation of Cox partial likelihood in the latent PLS space.
big_pls_cox_gd() now supports several optimisation schemes via the method argument:
"gd" for a basic fixed step gradient descent,
"bb" for a Barzilai Borwein step size,"nesterov" for Nesterov style acceleration,"bfgs" for a quasi Newton update.All optimisers share the same PLS scores and differ only in how the Cox coefficients are updated.
big_pls_cox() and big_pls_cox_gd().New prediction helpers:
predict.big_pls_cox_fast() and predict.big_pls_cox_gd() now handle dense matrices, big.matrix inputs and in-sample prediction.
type = "components" returns the PLS scores for the requested components.Arguments comps and coef allow partial use of components and user supplied Cox coefficients.
Added simple diagnostic accessors for gradient based fits, including iteration counts, log-likelihood trajectory, gradient norms and step sizes.
See the "Release highlights" section of the README for a condensed overview of these changes.
bigmemory matrices together with benchmarking utilities.big_pls_cox() and big_pls_cox_gd().big_pls_cox() and exposed survival model
objects for downstream predictions.cv.big_pls_cox() and cv.big_pls_cox_gd()
mirroring the plsRcox criteria, including the recommended survivalROC
iAUC metric by default.cv.coxgpls() to accept big.matrix predictors without coercion
errors.inst/benchmarks comparing
big_pls_cox() against plsRcox::plsRcox() on in-memory and file-backed
matrices.bigmemory.DESCRIPTION.big_pls_cox() and big_pls_cox_gd() stability checks.big_pls_cox() numerical stability and added support for additional
convergence diagnostics in the gradient-descent solver.bigmemory file-backed matrices.micro.censure and simulated Cox examples.Any scripts or data that you put into this service are public.
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