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.
|Author||Rebecca Murphy, Elgin Perry, Jennifer Keisman, Jon Harcum, Erik W Leppo|
|Maintainer||Erik Leppo <[email protected]>|
|Package repository||View on GitHub|
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