R Development Page
Contributed R Packages
Below is a list of all packages provided by project Time Series Utilities and Analysis.
Important note for package
binaries: R-Forge provides these binaries only for
the most recent version of R, but not for older
versions. In order to successfully install the
packages provided on R-Forge, you have to switch
to the most recent version of R or, alternatively,
install from the package sources (.tar.gz).
EvalEst | Dynamic Systems Estimation - Extensions
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Provides functions for evaluating (time series) model
estimation methods. These facilitate Monte Carlo experiments of repeated
simulations and estimations. Also provides methods for
looking at the distribution of the results from these experiments,
including model roots (which are an equivalence class invariant). |
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Version: 2024.2-1 |
Last change: 2024-02-18 15:54:29+01 |
Rev.: 234 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get EvalEst 2024.2-1 from CRAN |
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R install command:
install.packages("EvalEst", repos="http://R-Forge.R-project.org") |
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Xtratsfa | Time Series Factor Analysis Extra Functions
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Extra Functions for tsfa package. |
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Version: 2014.4-1 |
Last change: 2019-09-23 00:51:05+02 |
Rev.: 225 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current |
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R install command:
install.packages("Xtratsfa", repos="http://R-Forge.R-project.org") |
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curve | Dynamic Systems Estimation - Curvature Extensions
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Curvature extensions for dse. |
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Version: 2014.2-1 |
Last change: 2019-09-23 00:51:05+02 |
Rev.: 225 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current |
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R install command:
install.packages("curve", repos="http://R-Forge.R-project.org") |
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dse | Dynamic Systems Estimation (Time Series Package)
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Tools for multivariate, linear, time-invariant,
time series models. This includes ARMA and state-space representations,
and methods for converting between them. It also includes simulation
methods and several estimation functions. The package has functions
for looking at model roots, stability, and forecasts at different
horizons. The ARMA model representation is general, so that VAR, VARX,
ARIMA, ARMAX, ARIMAX can all be considered to be special cases. Kalman
filter and smoother estimates can be obtained from the state space
model, and state-space model reduction techniques are implemented.
An introduction and Users Guide is available in a vignette. |
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Version: 2020.2-1 |
Last change: 2020-03-02 01:48:08+01 |
Rev.: 226 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get dse 2020.2-1 from CRAN |
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R install command:
install.packages("dse", repos="http://R-Forge.R-project.org") |
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dse1 | DSE (unbundled transition package).
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This package is only to aid transition to the unbundled dse
package. It has no functions, but simply requires package EvalEst. |
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Version: 2011.11-3 |
Last change: 2019-09-23 00:51:05+02 |
Rev.: 225 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current |
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R install command:
install.packages("dse1", repos="http://R-Forge.R-project.org") |
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dse2 | DSE (unbundled transition package).
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This package is only to aid transition to the unbundled dse
package. It has no functions, but simply requires package EvalEst. |
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Version: 2013.9-1 |
Last change: 2019-09-23 00:51:05+02 |
Rev.: 225 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current |
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R install command:
install.packages("dse2", repos="http://R-Forge.R-project.org") |
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monitor | Dynamic Systems Estimation - Monitoring Extensions
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Multivariate Time Series - monitoring extensions |
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Version: 2012.4-1 |
Last change: 2019-09-23 00:51:05+02 |
Rev.: 225 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Failed to build |
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R install command:
install.packages("monitor", repos="http://R-Forge.R-project.org") |
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tfMiscDevel | Time Frame Extensions (in Development)
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Extensions and additional tframe utilities. |
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Version: 2016.7-1 |
Last change: 2019-09-23 00:51:05+02 |
Rev.: 225 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current |
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R install command:
install.packages("tfMiscDevel", repos="http://R-Forge.R-project.org") |
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tfplot | Time Frame User Utilities
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Utilities for simple manipulation and quick
plotting of time series data. These utilities use the tframe package
which provides a programming kernel for time series. Extensions to
tframe provided in tframePlus can also be used. See the Guide
vignette for examples. |
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Version: 2015.12-1 |
Last change: 2019-09-23 00:51:05+02 |
Rev.: 225 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get tfplot 2021.6-1 from CRAN |
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R install command:
install.packages("tfplot", repos="http://R-Forge.R-project.org") |
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tframe | Time Frame Coding Kernel
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A kernel of functions for programming
time series methods in a way that is relatively independently of the
representation of time. Also provides plotting, time windowing,
and some
other utility functions which are specifically intended for time series.
See the Guide distributed as a vignette, or ?tframe.Intro for more
details. (User utilities are in package tfplot.) |
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Version: 2019.6-1 |
Last change: 2019-09-23 00:51:05+02 |
Rev.: 225 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get tframe 2015.12-1.1 from CRAN |
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R install command:
install.packages("tframe", repos="http://R-Forge.R-project.org") |
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tframePlus | Time Frame Coding Kernel Extensions
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Extensions and additional tframe utilities. |
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Version: 2024.2-1 |
Last change: 2024-02-18 15:53:10+01 |
Rev.: 233 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get tframePlus 2024.2-1 from CRAN |
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R install command:
install.packages("tframePlus", repos="http://R-Forge.R-project.org") |
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tsfa | Time Series Factor Analysis
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Extraction of Factors from Multivariate Time Series. See ?00tsfa-Intro for more details. |
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Version: 2021.1-3 |
Last change: 2021-01-10 04:06:44+01 |
Rev.: 228 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get tsfa 2021.1-3 from CRAN |
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R install command:
install.packages("tsfa", repos="http://R-Forge.R-project.org") |
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Build status codes
0 - Current: the package is available for download. The corresponding package passed checks on the Linux and Windows platform without ERRORs.
1 - Scheduled for build: the package has been recognized by the build system and provided in the staging area.
2 - Building: the package has been sent to the build machines. It will be built and checked using the latest patched version of R. Note that it is included in a batch of several packages. Thus, this process will take some time to finish.
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