Fit Bayesian Dynamic Generalized Additive Models to sets of time series. Users can build dynamic nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2022) <doi:10.1111/2041-210X.13974>.
Package source: | mvgam_1.1.3.tar.gz |
Windows binaries: | r-devel: mvgam_1.1.3.zip, r-release: mvgam_1.1.3.zip, r-oldrel: mvgam_1.1.3.zip |
macOS binaries: | r-release (arm64): mvgam_1.1.3.tgz, r-oldrel (arm64): mvgam_1.1.3.tgz, r-release (x86_64): mvgam_1.1.3.tgz, r-oldrel (x86_64): mvgam_1.1.3.tgz |
Old sources: | mvgam archive |
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