DESCRIPTION

Package: voila
Type: Package
Title: Variational Inference for Langevin Equations
Version: 0.1.0
Author: Constantino Antonio Garcia Martinez
Maintainer: Constantino Antonio Garcia Martinez <constantinoantonio.garcia@usc.es>
Description: Non-parametric estimation of Langevin equations (also called  Stochastic Differential Equations or SDE) from a densely-observed time series. estimate the drift and diffusion terms by modelling them as gaussian processes  (GPs). To cope with the computational complexity that calculating the posterior distribution of the GPs requires, the GPs are approximated using a small set of function points, the inducing variables. These inducing variables are the result of evaluating the drift and diffusion terms at some strategically located  pseudo-inputs. The pseudo-inputs and the approximate posterior distributions  are learnt using variational inference.
License: GPL (>= 3)
LazyLoad: yes
Depends: R (>= 2.10), yuima (>= 1.6.4), Rcpp(>= 0.12.6), methods
Imports: KernSmooth, doParallel, foreach, zoo
LinkingTo: Rcpp, RcppArmadillo
RcppModules: VOILA_KERNELS
RoxygenNote: 6.0.1
Suggests: knitr,
    rmarkdown,
    plot3D,
    mvtnorm
VignetteBuilder: knitr
LazyData: true