Inference methods for partially-observed Markov processes

Package for estimating periodic autoregressive models. Datasets: monthly ozone and Fraser riverflow. Plots: periodic versions of boxplot, auto/partial correlations, moving-average expansion.

Regulation, decomposition and analysis of space-time series. The pastecs library is a PNEC-Art4 and IFREMER (Benoit Beliaeff <Benoit.Beliaeff@ifremer.fr>) initiative to bring PASSTEC 2000 (http://www.obs-vlfr.fr/~enseigne/anado/passtec/passtec.htm) functionalities to R.

This package facilitates analysis of paleontological sequences of trait values from an evolving lineage. Functions are provided to fit, using maximum likelihood, evolutionary models including unbiased random walks, directional evolution, stasis, Ornstein-Uhlenbeck, punctuated change, and evolutionary models in which traits track some measured covariate.

This package performs maximum entropy density based dependent data bootstrap. An algorithm is provided to create a population of time series (ensemble) without assuming stationarity. The reference paper (Vinod, H.D., 2004) explains how the algorithm satisfies the ergodic theorem and the central limit theorem.

The package implements several time series filters useful
for smoothing and extracting trend and cyclical components of a time
series. The routines are commonly used in economics and finance,
however they should also be interest to other areas. Currently,
Christiano-Fitzgerald, Baxter-King, Hodrick-Prescott, Butterworth,
and trigonometric regression filters are included in the package.

R functions for multivariate autoregressive analysis

Time Series Analysis including break detection, spectral analysis, KZ Fourier Transforms.

The its package contains an S4 class for handling irregular time series

Stochastic fractal and deterministic chaotic time series analysis.