# Package for implementing regression with spatial correlation

**URL:** <https://discourse.julialang.org/t/package-for-implementing-regression-with-spatial-correlation/111893>\
**Category:** General Usage\
**Tags:** package, regression, geospatial\
**Created:** [March 20, 2024, 5:16pm UTC](https://discourse.julialang.org/t/package-for-implementing-regression-with-spatial-correlation/111893 "2024-03-20T17:16:51Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![Mattriks](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mattriks/32/351_2.png) [@Mattriks](https://discourse.julialang.org/u/Mattriks)\
**Post date:** [March 24, 2024, 11:03pm UTC](https://discourse.julialang.org/t/package-for-implementing-regression-with-spatial-correlation/111893/2 "2024-03-24T23:03:59Z")

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I have a work-in-progress: [MeasurementErrorModels.jl](https://github.com/Mattriks/MeasurementErrorModels.jl). It allows lag-covariance matrices for both the predictor noise and response noise. I’ve added a [github gist](https://gist.github.com/Mattriks/c297542525abbd1a4b5ec7b41370d2fe) which illustrates a spatial model example. I’m currently working on adding models that allow both noisy and noiseless predictors, and Fisher information derived confidence intervals. Currently I assume that the noise _covariances_ are known, but I’m interested in implementing models where e.g. only the _correlation_ structure is known too.

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