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dlm (version 1.1-6.1)

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

3,964

Version

1.1-6.1

License

GPL (>= 2)

Maintainer

pcp by Giovanni Petris

Last Published

September 21st, 2024

Functions in dlm (1.1-6.1)

dlmGibbsDIG

Gibbs sampling for d-inverse-gamma model
dlmModARMA

Create a DLM representation of an ARMA process
dlmLL

Log likelihood evaluation for a state space model
rwishart

Random Wishart matrix
dlmMLE

Parameter estimation by maximum likelihood
dropFirst

Drop the first element of a vector or matrix
dlmModPoly

Create an n-th order polynomial DLM
dlmModSeas

Create a DLM for seasonal factors
mcmc

Utility functions for MCMC output analysis
residuals.dlmFiltered

One-step forecast errors
dlmSvd2var

Compute a nonnegative definite matrix from its Singular Value Decomposition
dlmRandom

Random DLM
dlmSmooth

DLM smoothing
dlmSum

Outer sum of Dynamic Linear Models
dlmFilter

DLM filtering
ARtransPars

Function to parametrize a stationary AR process
USecon

US macroeconomic time series
dlmBSample

Draw from the posterior distribution of the state vectors
FF

Components of a dlm object
NelPlo

Nelson-Plosser macroeconomic time series
dlmModTrig

Create Fourier representation of a periodic DLM component
dlmModReg

Create a DLM representation of a regression model
dlm

dlm objects
convex.bounds

Find the boundaries of a convex set
dlmForecast

Prediction and simulation of future observations
bdiag

Build a block diagonal matrix
arms

Function to perform Adaptive Rejection Metropolis Sampling