The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
MARSS: Multivariate Autoregressive State-space Models for Analyzing Time-series Data
Open accessAbstract
MARSS is a package for fitting mul- tivariate autoregressive state-space models to time-series data. The MARSS package imple- ments state-space models in a maximum like- lihood framework. The core functionality of MARSS is based on likelihood maximization us- ing the Kalman filter/smoother, combined with an EM algorithm. To make comparisons with other packages available, parameter estimation is also permitted via direct search routines avail- able in 'optim'. The MARSS package allows data to contain missing values and allows a wide variety of model structures and constraints to be specified (such as fixed or shared parame- ters). In addition to model-fitting, the package provides bootstrap routines for simulating data and generating confidence intervals, and multi- ple options for calculating model selection crite- ria (such as AIC).
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Citations
Cites
Forecasting, Structural Time Series Models and the Kalman Filter
Fildes, R., Harvey, A. C., West, M., & Harrison, J. (1991) · The Journal of the Operational Research Society · doi:10.2307/2583225 · Cited by 4,682
Forecasting, Structural Time Series Models and the Kalman Filter
Harvey, A. C. (1990) · doi:10.1017/cbo9781107049994 · Cited by 4,195
Time Series Analysis by State Space Methods: Second Edition
Durbin, J., & Koopman, S. J. (2012) · doi:10.1093/acprof:oso/9780199641178.001.0001 · Cited by 3,301
Analysis of Financial Time Series
(2006) · Technometrics · doi:10.1198/tech.2006.s405 · Cited by 2,348
Wiley Series in Probability and Statistics
(2007) · Wiley Series in Probability and Statistics · doi:10.1002/9780470186466.scard · Cited by 1,856
AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM
Shumway, R. H., & Stoffer, D. S. (1982) · Journal of Time Series Analysis · doi:10.1111/j.1467-9892.1982.tb00349.x · Cited by 1,470
Analysis of Financial Time Series
Tsay, R. S. (2002) · Wiley series in probability and statistics · doi:10.1002/0471264105 · Cited by 903
Estimation of Growth and Extinction Parameters for Endangered Species
Dennis, B., Munholland, P. L., & Scott, J. M. (1991) · Ecological Monographs · doi:10.2307/1943004 · Cited by 691
Density Dependence in Time Series Observations of Natural Populations: Estimation and Testing
Dennis, B., & Taper, M. L. (1994) · Ecological Monographs · doi:10.2307/2937041 · Cited by 583
ESTIMATING COMMUNITY STABILITY AND ECOLOGICAL INTERACTIONS FROM TIME-SERIES DATA
Ives, A. R., Dennis, B., Cottingham, K. L., & Carpenter, S. R. (2003) · Ecological Monographs · doi:10.1890/0012-9615(2003)073[0301:ecsaei]2.0.co;2 · Cited by 534
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Invasive species triggers a massive loss of ecosystem services through a trophic cascade
Walsh, J. R., Carpenter, S. R., & Vander Zanden, M. J. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1600366113 · Cited by 532
Population Trend of the World’s Monitored Seabirds, 1950-2010
Paleczny, M., Hammill, E., Karpouzi, V., & Pauly, D. (2015) · PLOS ONE · doi:10.1371/journal.pone.0129342 · Cited by 397
Tracking and forecasting ecosystem interactions in real time
Deyle, E. R., May, R. M., Munch, S. B., & Sugihara, G. (2016) · Proceedings of the Royal Society B: Biological Sciences · doi:10.1098/rspb.2015.2258 · Cited by 284
Predator decline leads to decreased stability in a coastal fish community
Britten, G. L., Dowd, M., Minto, C., Ferretti, F., Boero, F., & Lotze, H. K. (2014) · Ecology Letters · doi:10.1111/ele.12354 · Cited by 122
Real time transit demand prediction capturing station interactions and impact of special events
Noursalehi, P., Koutsopoulos, H. N., & Zhao, J. (2018) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.10.023 · Cited by 71
Metadata
- Authors
- Holmes, E., Ward, E., & Wills, K.
- Venue
- The R Journal
- Publisher
- The R Foundation
- Published
- 2012
- Volume
- 4
- Issue
- 1
- Pages
- 11
- ISSN
- 2073-4859 (ISSN-L)
- OpenAlex
- W2319291298
- Cited by
- 328
- References
- 27
- FWCI
- 8.87
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
