R: Scalable Bayesian Inference for Dynamic Generalized Linear Models

# Scalable Bayesian Inference for Dynamic Generalized Linear Models

## Documentation for package ‘SDGLM’ version 0.4.0

- DESCRIPTION file

## Help Pages

compute_metrics | Posterior-Predictive Metrics for Sca-MCMC Fit
compute_mutation_rate | Compute Scalable Mutation-Rate Vector
dglm_likelihood | Calculate Log-Likelihood for DGLM
generate_temperature | Generate Geometric Inverse-Temperature Ladder Constructs a geometric sequence of temperatures (inverse temperatures) for parallel-tempering MCMC.
geoTemp | Generate Geometric Temperature Ladder for Parallel Tempering
hamming_distance | Normalized Hamming Distance
mutRate | Scalable Mutation-Rate Strategies for Sca-MCMC
print.SDGLM | Print method for SDGLM objects
print.summary.SDGLM | Print method for summary.SDGLM
rinvwishart | Generate Random Samples from the Inverse Wishart Distribution
sca_mcmc | Scalable MCMC for Dynamic GLMs
sca_mcmc1 | Alternative Sca-MCMC Implementation for Variable Selection
simGamma | Simulate Gamma Dynamic GLM
simPareto | Simulate Pareto-type Dynamic GLM
simPoisBin | Simulate Poisson-Binomial Dynamic GLM
summary.SDGLM | Summary method for SDGLM objects