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dlm (version 1.1-5)

Bayesian and Likelihood Analysis of Dynamic Linear Models

Description

Provides routines for 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

1.1-5

License

GPL (>= 2)

Maintainer

pcp by Giovanni Petris

Last Published

June 13th, 2018

Functions in dlm (1.1-5)

Draw from the posterior distribution of the state vectors

Random Wishart matrix

Create an n-th order polynomial DLM

Outer sum of Dynamic Linear Models

Drop the first element of a vector or matrix

Compute a nonnegative definite matrix from its Singular Value Decomposition

Utility functions for MCMC output analysis

residuals.dlmFiltered

One-step forecast errors

Nelson-Plosser macroeconomic time series

Components of a dlm object

Find the boundaries of a convex set

Function to parametrize a stationary AR process

US macroeconomic time series

Function to perform Adaptive Rejection Metropolis Sampling

Create Fourier representation of a periodic DLM component

Log likelihood evaluation for a state space model

Create a DLM representation of an ARMA process

Parameter estimation by maximum likelihood

Build a block diagonal matrix

Create a DLM for seasonal factors

Create a DLM representation of a regression model

Prediction and simulation of future observations

Gibbs sampling for d-inverse-gamma model