Package: fGarch
Version: 290.76
Date: 1997 - 2008
Title: Rmetrics - Autoregressive Conditional Heteroskedastic Modelling
Author: Diethelm Wuertz, Yohan Chalabi, Michal Miklovic
Depends: R (>= 2.6.0), stats, graphics, methods, timeDate, timeSeries,
fBasics
Maintainer: Rmetrics Core Team <Rmetrics-core@r-project.org>
Description: Environment for teaching "Financial Engineering and
Computational Finance"
NOTE: SEVERAL PARTS ARE STILL PRELIMINARY AND MAY BE CHANGED IN THE
FUTURE. THIS TYPICALLY INCLUDES FUNCTION AND ARGUMENT NAMES, AS
WELL AS DEFAULTS FOR ARGUMENTS AND RETURN VALUES.
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
URL: http://www.rmetrics.org
Packaged: Wed Jan 28 13:30:51 2009; yankee

analyze and model heteroskedastic behavior in financial time
Imports: fastICA, Matrix, graphics, methods, stats, utils

Packaged: 2017-11-16 17:28:34 UTC; Tobias Setz

Description: Analyze and model heteroskedastic behavior in financial time series.
Imports: fBasics, timeDate, timeSeries, fastICA, Matrix, graphics,

, person("Martin","Maechler", role="aut", email="maechler@stat.math.ethz.ch", comment = c(ORCID = "0000-0002-8685-9910"))
, person("Chris", "Boudt", role = "ctb")
, person("Pierre", "Chausse", role = "ctb")
, person("Michal", "Miklovac", role = "ctb")

Imports: fBasics, timeDate, timeSeries, fastICA, Matrix (>= 1.5-0),

cvar (>= 0.5), graphics, methods, stats, utils

Title: Fitting Hidden Markov Models to Financial Data
person("Lennart", "Oelschl\U00E4ger", email = "lennart.oelschlaeger@uni-bielefeld.de", role = c("aut", "cre")),
person("Timo", "Adam", email = "ta59@st-andrews.ac.uk", role = "aut"))
Description: Fitting (hierarchical) hidden Markov models
to daily share prices provided by <https://finance.yahoo.com/>.
See <https://github.com/loelschlaeger/fHMM#readme> for documentation and examples.

person("Timo", "Adam", email = "ta59@st-andrews.ac.uk", role = "aut"),
person("Michels", "Rouven", email = "r.michels@uni-bielefeld.de", role = "aut"))

Description: Fitting (hierarchical) hidden Markov models to financial data
via maximum likelihood estimation. See Oelschläger, L. and Adam, T.
"Detecting bearish and bullish markets in financial time series using
hierarchical hidden Markov models" (2021, Statistical Modelling) for a
Suggests: parallel, doSNOW, rmarkdown, knitr, testthat (>= 3.0.0),