CovIterSolvers.jl

CovIterSolvers.jl provides a high-performance pipeline for covariance smoothing and functional principal component analysis using Krylov subspace methods.

API Reference

The following pages list all the functions and types available in the package.

• CovIterSolvers.AbstractBlockGP
• CovIterSolvers.AbstractBlockTensor
• CovIterSolvers.AbstractEstimateMethod
• CovIterSolvers.AdjointBlockOuter
• CovIterSolvers.AdjointCovFwdTensor
• CovIterSolvers.BSplineMethod
• CovIterSolvers.BlockDiagonal
• CovIterSolvers.BlockOuter
• CovIterSolvers.BrownianBridge
• CovIterSolvers.BrownianMotion
• CovIterSolvers.CovFwdTensor
• CovIterSolvers.CustomGP
• CovIterSolvers.CustomKernel
• CovIterSolvers.GaussianKernel
• CovIterSolvers.IntegratedBM
• CovIterSolvers.LaplacianKernel
• CovIterSolvers.MaternKernel
• CovIterSolvers.OrnsteinUhlenbeck
• CovIterSolvers.RBFKernelMethod
• BlockArrays.blocksizes
• CovIterSolvers.:⊙
• CovIterSolvers.block_outer
• CovIterSolvers.compute_kernel
• CovIterSolvers.conj_lanczos
• CovIterSolvers.covariancekernel
• CovIterSolvers.eval_covariance
• CovIterSolvers.eval_fwd
• CovIterSolvers.fpca
• CovIterSolvers.loc_grid
• CovIterSolvers.mean_fwd
• CovIterSolvers.rand_block_diag
• CovIterSolvers.sample_gp
• CovIterSolvers.undef_block_diag
• CovIterSolvers.zero_block_diag