Package dse provides tools for multivariate, linear, time-invariant, time series models. It 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 User's Guide is available in a vignette.

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

Continuous Time Autoregressive Models and the Kalman Filter

Chronological objects which can handle dates and times

functions and datasets for bootstrapping from the book "Bootstrap Methods and Their Applications" by A. C. Davison and D. V. Hinkley (1997, CUP).

TSdbi provides a common interface to time series databases. The objective is to define a standard interface so users can retrieve time series data from various sources with a simple, common, set of commands, and so programs can be written to be portable with respect to the data source. The SQL implementations also provide a database table design, so users needing to set up a time series database have a reasonably complete way to do this easily. The interface provides for a variety of options with respect to the representation of time series in R. The interface, and the SQL implementations, also handle vintages of time series data (sometime called editions or realtime data). There is also a (not yet well tested) mechanism to handle multilingual data documentation. Comprehensive examples of all the TS* packages is provided in the vignette Guide.pdf with the TSdata package.

Algorithms for time series analysis from nonlinear dynamical systems theory originally made available by Rainer Hegger, Holger Kantz and Thomas Schreiber at the site http://www.mpipks-dresden.mpg.de/~tisean/ . A related R package (tseriesChaos by Antonio, Fabio Di Narzo) contains rewritten versions of a few of the TISEAN algorithms. The intention of the present package is to use the TISEAN routines from within R with no need of manual importing/exporting. It is in a beta version state, though most of the functions should be usable. Correspondence should be sent to either Marji Lines, lines@dss.uniud.it, or to the current maintainer of the package. This package only contains R interface code. It requires that you have the Tisean-3.0.1 algorithms available on your computer.

Provides methods for estimating frequentist and Bayesian Vector Autoregression (VAR) models and Markov-switching Bayesian VAR (MSBVAR). Functions for reduced form and structural VAR models are also available. Includes methods for the generating posterior inferences for these models, forecasts, impulse responses (using likelihood-based error bands), and forecast error decompositions. Also includes utility functions for plotting forecasts and impulse responses, and generating draws from Wishart and singular multivariate normal densities. Current version includes functionality to build and evaluate models with Markov switching.

GeneNet is a package for analyzing gene expression (time series) data with focus on the inference of gene networks. In particular, GeneNet implements the methods of Schaefer and Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007) for learning large-scale gene association networks (including assignment of putative directions).

The GeneCycle package implements the approaches of Wichert et al. (2004), Ahdesmaki et al. (2005) and Ahdesmaki et al. (2007) for detecting periodically expressed genes from gene expression time series data.