| bbinompdf | Probability density for a hierarchical prior setup for the elements of the adjacency matrix based on the beta binomial distribution |
| betapdf | The four-parameter Beta probability density function |
| beta_priors | Set prior specifications for the slope parameters |
| beta_sampler | An R6 class for sampling slope parameters |
| covid | Covid incidences data |
| logdetAinvUpdate | Efficient update of the log-determinant and the matrix inverse |
| logdetPaceBarry | Pace and Barry's log determinant approximation |
| normalgamma | A Markov Chain Monte Carlo (MCMC) sampler for a linear panel model |
| plot.estimateW | Graphical summary of the estimated adjacency matrix Omega |
| plot.sim_dgp | Graphical summary of a generated spatial weight matrix |
| rho_priors | Specify prior for the spatial autoregressive parameter and sampling settings |
| rho_sampler | An R6 class for sampling the spatial autoregressive parameter rho |
| sar | A Markov Chain Monte Carlo (MCMC) sampler for the panel spatial autoregressive model (SAR) with exogenous spatial weight matrix. |
| sarw | A Markov Chain Monte Carlo (MCMC) sampler for the panel spatial autoregressive model (SAR) with unknown spatial weight matrix |
| sdm | A Markov Chain Monte Carlo (MCMC) sampler for the panel spatial Durbin model (SDM) with exogenous spatial weight matrix. |
| sdmw | A Markov Chain Monte Carlo (MCMC) sampler for the panel spatial Durbin model (SDM) with unknown spatial weight matrix |
| sigma_priors | Set prior specification for the error variance using an inverse Gamma distribution |
| sigma_sampler | An R6 class for sampling for sampling sigma^2 |
| sim_dgp | Simulating from a data generating process |
| slxw | A Markov Chain Monte Carlo (MCMC) sampler for the panel spatial SLX model with unknown spatial weight matrix |
| W_priors | Set prior specifications for the spatial weight matrix |
| W_sampler | An R6 class for sampling the elements of W |