Showing 2 of total 2 results (show query)
usepa
spmodel:Spatial Statistical Modeling and Prediction
Fit, summarize, and predict for a variety of spatial statistical models applied to point-referenced and areal (lattice) data. Parameters are estimated using various methods. Additional modeling features include anisotropy, non-spatial random effects, partition factors, big data approaches, and more. Model-fit statistics are used to summarize, visualize, and compare models. Predictions at unobserved locations are readily obtainable. For additional details, see Dumelle et al. (2023) <doi:10.1371/journal.pone.0282524>.
Maintained by Michael Dumelle. Last updated 21 days ago.
15 stars 7.66 score 112 scripts 3 dependentsusepa
SSN2:Spatial Modeling on Stream Networks
Spatial statistical modeling and prediction for data on stream networks, including models based on in-stream distance (Ver Hoef, J.M. and Peterson, E.E., (2010) <DOI:10.1198/jasa.2009.ap08248>.) Models are created using moving average constructions. Spatial linear models, including explanatory variables, can be fit with (restricted) maximum likelihood. Mapping and other graphical functions are included.
Maintained by Michael Dumelle. Last updated 7 months ago.
19 stars 6.61 score 36 scripts 2 dependents