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 (2022) <doi:10.1111/2041-210X.13974>.
Last updated 3 hours ago
bayesian-statisticsdynamic-factor-modelsecological-modellingforecastinggaussian-processgeneralised-additive-modelsgeneralized-additive-modelsjoint-species-distribution-modellingmultilevel-modelsmultivariate-timeseriesstantime-series-analysistimeseriesvector-autoregressionvectorautoregression
9.54 score 114 stars 98 scripts 321 downloadsMRFcov - Markov Random Fields with Additional Covariates
Approximate node interaction parameters of Markov Random Fields graphical networks. Models can incorporate additional covariates, allowing users to estimate how interactions between nodes in the graph are predicted to change across covariate gradients. The general methods implemented in this package are described in Clark et al. (2018) <doi:10.1002/ecy.2221>.
Last updated 8 months ago
conditional-random-fieldsgraphical-modelsmachine-learningmarkov-random-fieldmultivariate-analysismultivariate-statisticsnetwork-analysisnetworks
6.00 score 24 stars 28 scripts 325 downloads