Showing 3 of total 3 results (show query)
nicholasjclark
mvgam:Multivariate (Dynamic) Generalized Additive Models
Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build 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 (2023) <doi:10.1111/2041-210X.13974>.
Maintained by Nicholas J Clark. Last updated 1 days ago.
bayesian-statisticsdynamic-factor-modelsecological-modellingforecastinggaussian-processgeneralised-additive-modelsgeneralized-additive-modelsjoint-species-distribution-modellingmultilevel-modelsmultivariate-timeseriesstantime-series-analysistimeseriesvector-autoregressionvectorautoregressioncpp
10.0 match 139 stars 9.85 score 117 scripts
luisgruber
bayesianVARs:MCMC Estimation of Bayesian Vectorautoregressions
Efficient Markov Chain Monte Carlo (MCMC) algorithms for the fully Bayesian estimation of vectorautoregressions (VARs) featuring stochastic volatility (SV). Implements state-of-the-art shrinkage priors following Gruber & Kastner (2023) <doi:10.48550/arXiv.2206.04902>. Efficient equation-per-equation estimation following Kastner & Huber (2020) <doi:10.1002/for.2680> and Carrerio et al. (2021) <doi:10.1016/j.jeconom.2021.11.010>.
Maintained by Luis Gruber. Last updated 4 months ago.
bayesiantime-seriesvectorautoregressionopenblascpp
18.1 match 9 stars 5.43 score 9 scripts
icasas
tvReg:Time-Varying Coefficient for Single and Multi-Equation Regressions
Fitting time-varying coefficient models for single and multi-equation regressions, using kernel smoothing techniques.
Maintained by Isabel Casas. Last updated 2 years ago.
autoregressivenonparametricregressionsurevectorautoregressive
10.0 match 19 stars 6.25 score 62 scripts