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dlm (version 1.1-6)

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

4,121

Version

1.1-6

License

GPL (>= 2)

Maintainer

pcp by Giovanni Petris

Last Published

November 28th, 2022

Functions in dlm (1.1-6)

dlmModARMA

Create a DLM representation of an ARMA process
dlmLL

Log likelihood evaluation for a state space model
dlmModTrig

Create Fourier representation of a periodic DLM component
dlmModPoly

Create an n-th order polynomial DLM
dlmRandom

Random DLM
dlmMLE

Parameter estimation by maximum likelihood
dlmGibbsDIG

Gibbs sampling for d-inverse-gamma model
dlmForecast

Prediction and simulation of future observations
dlmModReg

Create a DLM representation of a regression model
rwishart

Random Wishart matrix
dlmModSeas

Create a DLM for seasonal factors
dropFirst

Drop the first element of a vector or matrix
dlmSvd2var

Compute a nonnegative definite matrix from its Singular Value Decomposition
residuals.dlmFiltered

One-step forecast errors
dlmSmooth

DLM smoothing
mcmc

Utility functions for MCMC output analysis
dlmSum

Outer sum of Dynamic Linear Models
dlmFilter

DLM filtering
convex.bounds

Find the boundaries of a convex set
FF

Components of a dlm object
NelPlo

Nelson-Plosser macroeconomic time series
bdiag

Build a block diagonal matrix
arms

Function to perform Adaptive Rejection Metropolis Sampling
dlm

dlm objects
USecon

US macroeconomic time series
ARtransPars

Function to parametrize a stationary AR process
dlmBSample

Draw from the posterior distribution of the state vectors