MARSS (Multivariate Autoregressive State Space Models). Over the last several years, this package has been developed by Eli Holmes, Eric Ward, and Mark Scheuerell (FE, NWFSC). This package is designed to take multiple time series and input, and allow the user to test series of hypotheses about the underlying structure (numbers of subpopulations, trends, etc), and effects of any covariates. Software is open source, and the R package is available on CRAN. Further applications of these models, with an emphasis on salmon, are currently in peer-review.
Holmes, E.E., Ward, E.J., and Wills, K. 2012. MARSS: multivariate autoregressive state-space models for analyzing time series data. R Journal, 4(1): 11-19. http://journal.r-project.org/archive/2012-1/RJournal\_2012-1.pdf
Holmes EE, Ward EJ, Scheuerell MD. 2012. Analysis of multivariate time-series using the MARSS package version 3.0.
Ward, E.J., H. Chirrakal, M. González-Suárez, D. Aurioles-Gamboa, E.E. Holmes, L. Gerber. 2010. Applying Multivariate-state-space Models to Detect Spatial clustering of California sea lions in the Gulf of California, Mexico. Journal of Applied Ecology, 47:47-56. doi: 10.1111/j.1365-2664.2009.01745.x
Hinrichsen, R. A. and E. E. Holmes. 2009. Using multivariate state-space models to study spatial structure and dynamics. In Spatial Ecology (editors Robert Stephen Cantrell, Chris Cosner, Shigui Ruan). CRC/Chapman Hall.
Online course: Fish 507: Applied Time Series Analysis in Fisheries and Environmental Sciences. https://catalyst.uw.edu/workspace/fish203/35553/ Instructors: Eric Ward, Eli Holmes, Mark Scheuerell