Package: SBAGM 0.1.0

SBAGM: Search Best ARIMA, GARCH, and MS-GARCH Model

Get the most appropriate autoregressive integrated moving average, generalized auto-regressive conditional heteroscedasticity and Markov switching GARCH model. For method details see Haas M, Mittnik S, Paolella MS (2004). <doi:10.1093/jjfinec/nbh020>, Bollerslev T (1986). <doi:10.1016/0304-4076(86)90063-1>.

Authors:Rajeev Ranjan Kumar [aut, cre], Girish Kumar Jha [aut, ths, ctb], Dwijesh C. Mishra [ctb], Neeraj Budhlakoti [ctb]

SBAGM_0.1.0.tar.gz
SBAGM_0.1.0.zip(r-4.7)SBAGM_0.1.0.zip(r-4.6)SBAGM_0.1.0.zip(r-4.5)
SBAGM_0.1.0.tgz(r-4.6-any)SBAGM_0.1.0.tgz(r-4.5-any)
SBAGM_0.1.0.tar.gz(r-4.7-any)SBAGM_0.1.0.tar.gz(r-4.6-any)
SBAGM_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
SBAGM/json (API)

# Install 'SBAGM' in R:
install.packages('SBAGM', repos = c('https://rrk4910.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

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

1.00 score 2 scripts 142 downloads 3 exports 64 dependencies

Last updated from:9c759b4315. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK204
source / vignettesOK204
linux-release-x86_64OK205
macos-release-arm64OK166
macos-oldrel-arm64OK152
windows-develOK147
windows-releaseOK208
windows-oldrelOK172
wasm-releaseOK114

Exports:appgarchappmsgarchARIMAAIC

Dependencies:chronclicodacodetoolscolorspacecpp11digestDistributionUtilsexpmfanplotfarverFNNforecastfracdifffuturefuture.applyGeneralizedHyperbolicgenericsggplot2globalsgluegtableisobandkernlabKernSmoothkslabelinglatticelifecyclelistenvlmtestmagrittrMASSMatrixmclustmgcvMSGARCHmulticoolmvtnormnlmenloptrnnetnumDerivparallellypracmaR6RColorBrewerRcppRcppArmadillorlangRsolnprugarchS7scalesSkewHyperbolicspdtimeDatetruncnormurcavctrsviridisLitewithrxtszoo