Enable users to evaluate long-term trends using a Generalized Additive Modeling (GAM) approach. The model development includes selecting a GAM structure to describe nonlinear seasonally-varying changes over time, incorporation of hydrologic variability via either a river flow or salinity, the use of an intervention to deal with method or laboratory changes suspected to impact data values, and representation of left- and interval-censored data. The approach has been applied to water quality data in the Chesapeake Bay, a major estuary on the east coast of the United States to provide insights to a range of management- and research-focused questions. Methodology described in Murphy (2019) <doi:10.1016/j.envsoft.2019.03.027>.
Package details |
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Author | Rebecca Murphy, Elgin Perry, Jennifer Keisman, Jon Harcum, Erik W Leppo |
Maintainer | Erik W Leppo <Erik.Leppo@tetratech.com> |
License | GPL-3 |
Version | 2.0.12 |
URL | https://github.com/tetratech/baytrends https://tetratech.github.io/baytrends/ |
Package repository | View on GitHub |
Installation |
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