README.md

In dajmcdon/spasm: Implements the Laplace-Gaussian filter for sparse, additive, state-space models

spasm

Testing...

The goal of spasm is to implement the first order Laplace Gaussian filter (as described in Koyama et al) for estimating state space models whose observation equation has a mean function which is a spline in the unobserved state: [ \begin{align} y_t &= Z(x_t) + \epsilon_t\ x_{t+1} &= T x_t + \eta_t, \end{align} ] where $Z(x)$ is a sparse spline estimated via the group lasso.

Installation

You can install spasm from github with:

# install.packages("devtools")
devtools::install_github("dajmcdon/spasm")

Example

This is a basic example which shows you how to solve a common problem:

someData = generateSPASM(100,3,4)
SPAMfit = spam(someData$y, someData$x)
filt = lgf1(someData$y, SPAMfit, someData$Tt,
  solve(someData$HHt), someData$GGt, rep(0,3), diag(1,3))
matplot(someData$x, ty='l', col=1, lty=1)
matlines(filt$xtt, col=2, lty=3)

dajmcdon/spasm documentation built on May 6, 2019, 1:31 a.m.