Package: tsDyn
Type: Package
Title: Nonlinear Time Series Models with Regime Switching
Version: 0.9-44
Date: 2015-05-22
Authors@R: c(person("Antonio Fabio", "Di Narzo", role = "aut"),
person("Jose Luis", "Aznarte", role = "ctb"),
person("Matthieu", "Stigler", role = c("aut","cre"), email="Matthieu.Stigler@gmail.com"))
Imports: mnormt, mgcv, nnet, tseriesChaos, tseries, utils, vars, urca, forecast, MASS, Matrix, foreach, methods
Suggests: sm, scatterplot3d, rgl, FinTS
Maintainer: Matthieu Stigler <Matthieu.Stigler@gmail.com>
Description: Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006).
License: GPL (>= 2)
URL: http://github.com/MatthieuStigler/tsDyn/wiki
MailingList: tsdyn@googlegroups.com
NeedsCompilation: yes
Packaged: 2016-05-22 20:02:05 UTC; matifou
Author: Antonio Fabio Di Narzo [aut],
Jose Luis Aznarte [ctb],
Matthieu Stigler [aut, cre]
Repository: CRAN
Date/Publication: 2016-05-22 22:56:44
Package: tsModel
Title: Time Series Modeling for Air Pollution and Health
Depends: R (>= 3.0.0)
Imports: splines, stats
Suggests: gam
Date: 2013-06-24
Version: 0.6
Author: Roger D. Peng <rpeng@jhsph.edu>, with contributions from Aidan
McDermott
Maintainer: Roger D. Peng <rpeng@jhsph.edu>
Description: Tools for specifying time series regression models
License: GPL (>= 2)
Packaged: 2013-06-24 19:20:01 UTC; rdpeng
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-06-24 23:00:11
Package: tsPI
Title: Improved Prediction Intervals for ARIMA Processes and Structural
Time Series
Version: 1.0.1
Date: 2016-03-17
Author: Jouni Helske
Maintainer: Jouni Helske <jouni.helske@jyu.fi>
Imports: KFAS
Suggests: testthat
Description: Prediction intervals for ARIMA and structural time series
models using importance sampling approach with uninformative priors for model
parameters, leading to more accurate coverage probabilities in frequentist
sense. Instead of sampling the future observations and hidden states of the
state space representation of the model, only model parameters are sampled,
and the method is based solving the equations corresponding to the conditional
coverage probability of the prediction intervals. This makes method relatively
fast compared to for example MCMC methods, and standard errors of prediction
limits can also be computed straightforwardly.
License: GPL-3
NeedsCompilation: yes
Encoding: UTF-8
BugReports: https://github.com/helske/tsPI/issues
RoxygenNote: 5.0.1
Packaged: 2016-03-17 06:59:17 UTC; jovetale
Repository: CRAN
Date/Publication: 2016-03-17 13:14:01
Package: tsallisqexp
Type: Package
Title: Tsallis q-Exp Distribution
Version: 0.9-2
Author: Cosma Shalizi [aut] (original R code), Christophe Dutang [cre] (R code packaging)
Maintainer: Christophe Dutang <christophe.dutang@ensimag.fr>
Description: Tsallis distribution also known as the q-exponential family distribution. Provide distribution d, p, q, r functions, fitting and testing functions. Project initiated by Paul Higbie and based on Cosma Shalizi's code.
Depends: R (>= 3.0.0)
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2015-07-20 15:20:18 UTC; dutangc
Repository: CRAN
Date/Publication: 2015-07-20 18:20:01
Package: tsbridge
Type: Package
Title: Calculate normalising constants for Bayesian time series models.
Version: 1.1
Date: 2013-10-07
Author: Guy J. Abel and Jackie S. T. Wong
Maintainer: Guy J. Abel <g.j.abel@gmail.com>
Depends: R (>= 2.15.0)
Imports: mvtnorm, tsbugs
Description: The tsbridge package contains a collection of R functions that can be used to estimate normalising constants using the bridge sampler of Meng and Wong (1996). The functions can be applied to calculate posterior model probabilities for a variety of time series Bayesian models, where parameters are estimated using BUGS, and models themselves are created using the tsbugs package.
License: GPL-2
URL: http://gjabel.wordpress.com/category/r/tsbridge/
LazyLoad: yes
LazyData: yes
Packaged: 2014-05-23 08:04:46 UTC; gabel
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-05-23 10:27:25
Package: tsbugs
Type: Package
Title: Create time series BUGS models.
Version: 1.2
Author: Guy J. Abel
Maintainer: "Guy J. Abel" <g.j.abel@gmail.com>
Depends: R (>= 2.15.2)
Suggests: R2OpenBUGS, fanplot
Description: The tsbugs package contains a collection of R functions
that can be used to create time series BUGS models of various
order. Included are function to create BUGS with non-constant
variance such stochastic volatility models and random variance
shift models.
License: GPL-2
LazyLoad: yes
LazyData: yes
Packaged: 2013-02-25 13:08:29 UTC; gabel
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-02-25 15:02:00
Package: tsc
Type: Package
Title: Likelihood-ratio Tests for Two-Sample Comparisons
Version: 1.0-3
Date: 2014-11-20
Author: Yang Zhao, Albert Vexler, Alan Hutson
Maintainer: Yang Zhao <yzhao37@buffalo.edu>
Description: Performs the two-sample comparisons using the following exact test procedures: the exact likelihood-ratio test (LRT) for equality of two normal populations proposed in Zhang et al. (2012); the combined test based on the LRT and Shapiro-Wilk test for normality via the Bonferroni correction technique; the newly proposed density-based empirical likelihood (DBEL) ratio test. To calculate p-values of the DBEL procedures, three procedures are used: (a) the traditional Monte Carlo (MC) method implemented in C++, (b) a new interpolation method based on regression techniques to operate with tabulated critical values of the test statistic; (c) a Bayesian type method that uses the tabulated critical values as the prior information and MC generated DBEL-test-statistic's values as data.
License: GPL (>= 2)
LazyData: yes
Packaged: 2015-01-07 15:48:07 UTC; starvictoria
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2015-01-07 17:51:11
Package: tscount
Type: Package
Title: Analysis of Count Time Series
Version: 1.3.0
Date: 2016-05-13
Authors@R: c(
person("Tobias", "Liboschik", role=c("aut", "cre"), email="liboschik@statistik.tu-dortmund.de"),
person("Roland", "Fried", role=c("aut"), email="fried@statistik.tu-dortmund.de"),
person("Konstantinos", "Fokianos", role=c("aut"), email="fokianos@ucy.ac.cy"),
person("Philipp", "Probst", role=c("aut"), email="philipp.probst@tu-dortmund.de"),
person("Jonathan", "Rathjens", role=c("ctb"), email="jonathan.rathjens@tu-dortmund.de"))
Description: Likelihood-based methods for model fitting and assessment, prediction and intervention analysis of count time series following generalized linear models are provided. Models with the identity and with the logarithmic link function are allowed. The conditional distribution can be Poisson or Negative Binomial.
Imports: parallel, ltsa
Suggests: Matrix, xtable, gamlss.data, surveillance, gamlss, VGAM, acp, glarma, gamlss.util, KFAS, gcmr
License: GPL-2 | GPL-3
URL: http://tscount.r-forge.r-project.org
ByteCompile: true
NeedsCompilation: no
LazyData: true
Encoding: UTF-8
Packaged: 2016-05-13 16:50:30 UTC; liboschik
Author: Tobias Liboschik [aut, cre],
Roland Fried [aut],
Konstantinos Fokianos [aut],
Philipp Probst [aut],
Jonathan Rathjens [ctb]
Maintainer: Tobias Liboschik <liboschik@statistik.tu-dortmund.de>
Repository: CRAN
Date/Publication: 2016-05-13 19:30:24
Package: tseriesChaos
Title: Analysis of nonlinear time series
Date: 2013-04-29
Version: 0.1-13
Author: Antonio, Fabio Di Narzo
Depends: R (>= 2.2.0), deSolve
Suggests: scatterplot3d
LazyData: yes
LazyLoad: yes
Description: Routines for the analysis of nonlinear time series. This
work is largely inspired by the TISEAN project, by Rainer
Hegger, Holger Kantz and Thomas Schreiber:
http://www.mpipks-dresden.mpg.de/~tisean/
Maintainer: Antonio Fabio Di Narzo <antonio.fabio@gmail.com>
License: GPL-2
Packaged: 2013-04-29 13:48:58 UTC; dinarzo
Repository: CRAN
Date/Publication: 2013-04-29 16:08:47
NeedsCompilation: yes
Package: tseriesEntropy
Title: Entropy Based Analysis and Tests for Time Series
Date: 2015-07-02
Version: 0.5-12
Author: Simone Giannerini
Depends: R (>= 2.14.0)
Imports: cubature, methods, parallel, stats, graphics
Suggests:
Description: Implements an Entropy measure of dependence based on the Bhattacharya-Hellinger-Matusita distance. Can be used as a (nonlinear) autocorrelation/crosscorrelation function for continuous and categorical time series. The package includes tests for serial dependence and nonlinearity based on it. Some routines have a parallel version that can be used in a multicore/cluster environment. The package makes use of S4 classes.
Maintainer: Simone Giannerini <simone.giannerini@unibo.it>
License: GPL (>= 2)
NeedsCompilation: yes
Packaged: 2015-07-03 15:19:31 UTC; Simone
Repository: CRAN
Date/Publication: 2015-07-04 09:52:03
providing 
.