Returns both the filtered values and smoothed values for the state-space model.
Ksmooth0(num, y, A, mu0, Sigma0, Phi, cQ, cR)state smoothers
smoother mean square error
initial mean smoother
initial smoother covariance
initial value of the J matrix
the J matrices
one-step-ahead prediction of the state
mean square prediction error
filter value of the state
mean square filter error
the negative of the log likelihood
last value of the gain
number of observations
data matrix, vector or time series
time-invariant observation matrix
initial state mean vector
initial state covariance matrix
state transition matrix
Cholesky-type decomposition of state error covariance matrix Q -- see details below
Cholesky-type decomposition of observation error covariance matrix R -- see details below
D.S. Stoffer
cQ and cR are the Cholesky-type decompositions of Q and R. In particular, Q = t(cQ)%*%cQ and R = t(cR)%*%cR is all that is required (assuming Q and R are valid covariance matrices).
You can find demonstrations of astsa capabilities at FUN WITH ASTSA.
The most recent version of the package can be found at https://github.com/nickpoison/astsa/.
In addition, the News and ChangeLog files are at https://github.com/nickpoison/astsa/blob/master/NEWS.md.
The webpages for the texts are https://www.stat.pitt.edu/stoffer/tsa4/ and https://www.stat.pitt.edu/stoffer/tsda/.