Showing 3 of total 3 results (show query)
pbs-assess
sdmTMB:Spatial and Spatiotemporal SPDE-Based GLMMs with 'TMB'
Implements spatial and spatiotemporal GLMMs (Generalized Linear Mixed Effect Models) using 'TMB', 'fmesher', and the SPDE (Stochastic Partial Differential Equation) Gaussian Markov random field approximation to Gaussian random fields. One common application is for spatially explicit species distribution models (SDMs). See Anderson et al. (2024) <doi:10.1101/2022.03.24.485545>.
Maintained by Sean C. Anderson. Last updated 17 hours ago.
ecologyglmmspatial-analysisspecies-distribution-modellingtmbcpp
205 stars 11.01 score 848 scripts 1 dependents
lcbc-uio
galamm:Generalized Additive Latent and Mixed Models
Estimates generalized additive latent and mixed models using maximum marginal likelihood, as defined in Sorensen et al. (2023) <doi:10.1007/s11336-023-09910-z>, which is an extension of Rabe-Hesketh and Skrondal (2004)'s unifying framework for multilevel latent variable modeling <doi:10.1007/BF02295939>. Efficient computation is done using sparse matrix methods, Laplace approximation, and automatic differentiation. The framework includes generalized multilevel models with heteroscedastic residuals, mixed response types, factor loadings, smoothing splines, crossed random effects, and combinations thereof. Syntax for model formulation is close to 'lme4' (Bates et al. (2015) <doi:10.18637/jss.v067.i01>) and 'PLmixed' (Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>).
Maintained by Øystein Sørensen. Last updated 6 months ago.
generalized-additive-modelshierarchical-modelsitem-response-theorylatent-variable-modelsstructural-equation-modelscpp
29 stars 7.33 score 41 scripts
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