Spatial Surplus Production Model Framework for Northern Shrimp Populations
Accessing OR replacing discretization_method model elements
Accessing OR replacing sspm_boundary model elements
Accessing OR replacing sspm_dataset model elements
Accessing OR replacing sspm_fit model elements
Accessing OR replacing sspm_formula model elements
Cast into a discretization_method object
sspm discretization method class
Extract methods
Fit the GAM part of a sspm model
GAM confidence and prediction intervals
Accessing OR replacing sspm model elements
sspm model class
Create a sspm model object
sspm formula object
Map model formula onto a sspm_dataset object
Pipe operator
Plot sspm objects
Predict with a SPM model
sspm Smoothing functions
Update biomass value from catch adta
Aggregate a dataset or fit data variable based on a boundary
Create a sspm_boundary object
Create a sspm_dataset dataset structure
Discretize a sspm model object
Create lagged columns in a sspm smoothed data slot
Get the list of available discretization methods
Get the list of available smoothing methods
Smooth a variable in a sspm dataset
Split data in test and train sets
Fit an SPM model
sspm boundary structure
sspm dataset structure
sspm discrete boundary structure
sspm fit
sspm: Spatial Surplus Production Model Framework for Northern Shrimp P...
Summarises sspm_fit objects
Perform voronoi tesselation
Perform delaunay triangulation
Implement a GAM-based (Generalized Additive Models) spatial surplus production model (spatial SPM), aimed at modeling northern shrimp population in Atlantic Canada but potentially to any stock in any location. The package is opinionated in its implementation of SPMs as it internally makes the choice to use penalized spatial gams with time lags. However, it also aims to provide options for the user to customize their model. The methods are described in Pedersen et al. (2022, <https://www.dfo-mpo.gc.ca/csas-sccs/Publications/ResDocs-DocRech/2022/2022_062-eng.html>).
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