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Long time I have not praised #Rstats {targets} so here we are: complex project with 20ish mins to generate from row data.

We still have lot of adjustment on multiple places and also disagreements. One _targets.yaml, and one TAR_PROJECT config/logic allow us to switch between multiple targets stores and compare multiple versions at various steps while being efficient and rigorous.

(I am not even using gittargets!)

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The paper introduces R algorithms that use data-driven, varying-size buffers to adaptively delimit background regions for SDM/ENM, with the aim of addressing spatially uneven sampling / survey bias: https://journals.ku.edu/jbi/article/view/25045 #Rstats #GISChat #GIS #biogeography #macroecology

Maps with spatially adaptive buffers around differently surveyed regions
journals.ku.eduSpatially adaptive background delimitation strategies for species distribution models with unevenly surveyed occurrences | Biodiversity Informatics
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