# Approximate Bayesian Inference via Sparse Grid Quadrature Evaluation (BISQuE) for Hierarchical Models

## Documentation for package ‘bisque’ version 1.0.2

- DESCRIPTION file.
- Package NEWS.

## Help Pages

createLocScaleGrid | Create a centered and scaled sparse integration grid
dmix | Evaluate a mixture density
emix | Compute expectations via weighted mixtures
furseals | Data from a capture-recapture study of fur seal pups
itx | Named inverse transformation functions
jac.exp | Jacobian for exponential transform
jac.invlogit | Jacobian for logit transform
jac.log | Jacobian for log transform
jac.logit | Jacobian for logit transform
kCompute | Use sparse grid quadrature techniques to integrate (unnormalized) densities
logjac | Wrapper to abstractly evaluate log-Jacobian functions for transforms
mergePars | Merge pre-computed components of f(theta1 | theta2, X)
sFit | Fit a spatially mean-zero spatial Gaussian process model
sKrig | Draw posterior predictive samples from a spatial Gaussian process model
tx | Named transformation functions
wBuild | Derive parameters for building integration grids
wMix | Construct a weighted mixture object