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dlm (version 1.1-2)

Bayesian and Likelihood Analysis of Dynamic Linear Models

Description

Maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Normal linear State Space models, also known as Dynamic Linear Models

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Version

Install

install.packages('dlm')

Monthly Downloads

4,117

Version

1.1-2

License

GPL (>= 2)

Maintainer

pcp by Giovanni Petris

Last Published

October 5th, 2010

Functions in dlm (1.1-2)

rwishart

Random Wishart matrix

dlmModPoly

Create an n-th order polynomial DLM

arms

Function to perform Adaptive Rejection Metropolis Sampling

USecon

US macroeconomic time series

dlmSmooth

DLM smoothing

mcmc

Utility functions for MCMC output analysis

dropFirst

Drop the first element of a vector or matrix

dlmModTrig

Create Fourier representation of a periodic DLM component

dlmBSample

Draw from the posterior distribution of the state vectors

NelPlo

Nelson-Plosser macroeconomic time series

residuals.dlmFiltered

One-step forecast errors

dlmFilter

DLM filtering

dlmGibbsDIG

Gibbs sampling for d-inverse-gamma model

dlmSvd2var

Compute a nonnegative definite matrix from its Singular Value Decomposition

dlmRandom

Random DLM

dlmMLE

Parameter estimation by maximum likelihood

dlmLL

Log likelihood evaluation for a state space model

dlmModARMA

Create a DLM representation of an ARMA process

dlm

dlm objects

dlmSum

Outer sum of Dynamic Linear Models

ARtransPars

Function to parametrize a stationary AR process

Components of a dlm object

convex.bounds

Find the boundaries of a convex set

dlmModSeas

Create a DLM for seasonal factors

dlmModReg

Create a DLM representation of a regression model

dlmForecast

Prediction and simulation of future observations

bdiag

Build a block diagonal matrix