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jacolien
itsadug:Interpreting Time Series and Autocorrelated Data Using GAMMs
GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999) as implemented in the R package 'mgcv' (Wood, S.N., 2006; 2011) is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. As time course measures are sensitive to autocorrelation problems, GAMMs implements methods to reduce the autocorrelation problems. This package includes functions for the evaluation of GAMM models (e.g., model comparisons, determining regions of significance, inspection of autocorrelational structure in residuals) and interpreting of GAMMs (e.g., visualization of complex interactions, and contrasts).
Maintained by Jacolien van Rij. Last updated 3 years ago.
1 stars 6.45 score 576 scripts 2 dependents
schuch666
eva3dm:Evaluation of 3D Meteorological and Air Quality Models
Provides tools for post-process, evaluate and visualize results from 3d Meteorological and Air Quality models against point observations (i.e. surface stations) and grid (i.e. satellite) observations.
Maintained by Daniel Schuch. Last updated 7 days ago.
air-quality-modelair-quality-model-evaluationatmosatmosphereatmospheric-chemistryatmospheric-modellingatmospheric-modelsatmospheric-scienceevaluationmodel-evaluationmodel-evaluation-metricswrf-chem
4 stars 4.76 score 3 scripts