Package: dlm 1.1-6.1

dlm: Bayesian and Likelihood Analysis of Dynamic Linear Models

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

Authors:Giovanni Petris [aut, cre], Wally Gilks [ctb]

dlm_1.1-6.1.tar.gz
dlm_1.1-6.1.zip(r-4.5)dlm_1.1-6.1.zip(r-4.4)dlm_1.1-6.1.zip(r-4.3)
dlm_1.1-6.1.tgz(r-4.4-x86_64)dlm_1.1-6.1.tgz(r-4.4-arm64)dlm_1.1-6.1.tgz(r-4.3-x86_64)dlm_1.1-6.1.tgz(r-4.3-arm64)
dlm_1.1-6.1.tar.gz(r-4.5-noble)dlm_1.1-6.1.tar.gz(r-4.4-noble)
dlm_1.1-6.1.tgz(r-4.4-emscripten)dlm_1.1-6.1.tgz(r-4.3-emscripten)
dlm.pdf |dlm.html✨
dlm/json (API)
NEWS

# Install 'dlm' in R:
install.packages('dlm', repos = c('https://gpetris.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
Datasets:
  • NelPlo - Nelson-Plosser macroeconomic time series
  • USecon - US macroeconomic time series

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

7.74 score 8 stars 11 packages 498 scripts 4.2k downloads 11 mentions 51 exports 0 dependencies

Last updated 2 months agofrom:64c187ac71. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 21 2024
R-4.5-win-x86_64OKNov 21 2024
R-4.5-linux-x86_64OKNov 21 2024
R-4.4-win-x86_64OKNov 21 2024
R-4.4-mac-x86_64OKNov 21 2024
R-4.4-mac-aarch64OKNov 21 2024
R-4.3-win-x86_64OKNov 21 2024
R-4.3-mac-x86_64OKNov 21 2024
R-4.3-mac-aarch64OKNov 21 2024

Exports:%+%armsARtransParsas.dlmbdiagC0C0<-convex.boundsdlmdlmBSampledlmFilterdlmForecastdlmGibbsDIGdlmLLdlmMLEdlmModARMAdlmModPolydlmModRegdlmModSeasdlmModTrigdlmRandomdlmSmoothdlmSumdlmSvd2vardropFirstergMeanFFFF<-GGGG<-is.dlmJFFJFF<-JGGJGG<-JVJV<-JWJW<-m0m0<-mcmcMeanmcmcMeansmcmcSDrwishartVV<-WW<-XX<-

Dependencies:

dlm: MLE and Bayesian analysis of Dynamic Linear Models

Rendered fromdlm.Rnwusingutils::Sweaveon Nov 21 2024.

Last update: 2014-09-16
Started: 2014-09-16

Readme and manuals

Help Manual

Help pageTopics
Function to perform Adaptive Rejection Metropolis Samplingarms
Function to parametrize a stationary AR processARtransPars
Build a block diagonal matrixbdiag
Find the boundaries of a convex setconvex.bounds
dlm objectsas.dlm dlm is.dlm
Draw from the posterior distribution of the state vectorsdlmBSample
DLM filteringdlmFilter
Prediction and simulation of future observationsdlmForecast
Gibbs sampling for d-inverse-gamma modeldlmGibbsDIG
Log likelihood evaluation for a state space modeldlmLL
Parameter estimation by maximum likelihooddlmMLE
Create a DLM representation of an ARMA processdlmModARMA
Create an n-th order polynomial DLMdlmModPoly
Create a DLM representation of a regression modeldlmModReg
Create a DLM for seasonal factorsdlmModSeas
Create Fourier representation of a periodic DLM componentdlmModTrig
Random DLMdlmRandom
DLM smoothingdlmSmooth dlmSmooth.default dlmSmooth.dlmFiltered
Outer sum of Dynamic Linear Models%+% dlmSum
Compute a nonnegative definite matrix from its Singular Value DecompositiondlmSvd2var
Drop the first element of a vector or matrixdropFirst
Components of a dlm objectC0 C0.dlm C0<- C0<-.dlm FF FF.dlm FF<- FF<-.dlm GG GG.dlm GG<- GG<-.dlm JFF JFF.dlm JFF<- JFF<-.dlm JGG JGG.dlm JGG<- JGG<-.dlm JV JV.dlm JV<- JV<-.dlm JW JW.dlm JW<- JW<-.dlm m0 m0.dlm m0<- m0<-.dlm V V.dlm V<- V<-.dlm W W.dlm W<- W<-.dlm X X.dlm X<- X<-.dlm
Utility functions for MCMC output analysisergMean mcmcMean mcmcMeans mcmcSD
Nelson-Plosser macroeconomic time seriesNelPlo
One-step forecast errorsresiduals.dlmFiltered
Random Wishart matrixrwishart
US macroeconomic time seriesUSecon