Bayesian Modeling of Spatio-Temporal Data with R
Bayesian regression model fitting for areal and areal spatio-temporal ...
Cauchy prior simulation example.
Model choice criteria calculation for univariate normal model for both...
Model fitting and validation for spatio-temporal data from moving sens...
Calculates and plots the variogram cloud and an estimated variogram.
N(theta, sigma2): Using different methods.
Bayesian regression model fitting for point referenced spatial data. C...
Bayesian regression model fitting for point referenced spatio-temporal...
Calculate DIC function. Has two arguments: (1) log full likelihood at ...
Calculates the four validation statistics: RMSE, MAE, CRPS and coverag...
Calculate WAIC function. Has the sole argument component wise likeliho...
Prints and returns the estimates of the coefficients
Draws a time series (ribbon) plot by combining fitted and predicted va...
Extract fitted values from bmstdr objects.
This function is used to delete values outside the state of New York
Obtains parameter estimates from MCMC samples
Obtains suitable validation summary statistics from MCMC samples obtai...
Returns a vector of row numbers for validation.
Calculate the hit and false alarm rates
Is it a bmstdr model fitted object?
Banerjee, Carlin and Gelfand (2015) Mat'ern covariance function
Banerjee et al Mat'ern covariance function
Observed against predicted plot
Grid search method for choosing phi Calculates the validation statisti...
Calculates the validation statistics using the spatial model with a gi...
Plot method for bmstdr objects.
Provides basic information regarding the fitted model.
Extract residuals from a bmstdr fitted object.
Provides basic summaries of model fitting.
Prints the terms
Fits, validates and compares a number of Bayesian models for spatial and space time point referenced and areal unit data. Model fitting is done using several packages: 'rstan', 'INLA', 'spBayes', 'spTimer', 'spTDyn', 'CARBayes' and 'CARBayesST'. Model comparison is performed using the DIC and WAIC, and K-fold cross-validation where the user is free to select their own subset of data rows for validation. Sahu (2022) <doi:10.1201/9780429318443> describes the methods in detail.