Bayesian and Likelihood Analysis of Dynamic Linear Models
Function to perform Adaptive Rejection Metropolis Sampling
Function to parametrize a stationary AR process
Build a block diagonal matrix
Find the boundaries of a convex set
dlm objects
Draw from the posterior distribution of the state vectors
DLM filtering
Prediction and simulation of future observations
Gibbs sampling for d-inverse-gamma model
Log likelihood evaluation for a state space model
Parameter estimation by maximum likelihood
Create a DLM representation of an ARMA process
Create an n-th order polynomial DLM
Create a DLM representation of a regression model
Create a DLM for seasonal factors
Create Fourier representation of a periodic DLM component
Random DLM
DLM smoothing
Outer sum of Dynamic Linear Models
Compute a nonnegative definite matrix from its Singular Value Decompos...
Drop the first element of a vector or matrix
Components of a dlm object
Utility functions for MCMC output analysis
One-step forecast errors
Random Wishart matrix
Provides routines for Maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Normal linear State Space models, also known as Dynamic Linear Models.