Facilitates scalable spatiotemporally varying coefficient
    modelling with Bayesian kernelized tensor regression.
    The important features of this package are:
    (a) Enabling local temporal and spatial modeling of the relationship between
    the response variable and covariates.
    (b) Implementing the model described by Lei et al. (2023) <doi:10.48550/arXiv.2109.00046>.
    (c) Using a Bayesian Markov Chain Monte Carlo (MCMC) algorithm to sample from the posterior
    distribution of the model parameters.
    (d) Employing a tensor decomposition to reduce the number of estimated parameters.
    (e) Accelerating tensor operations and enabling graphics processing unit (GPU) acceleration
    with the 'torch' package.

Performs prediction of a response function from simulated response values, allowing black-box optimization of functions estimated with some error. Includes a simple user interface for such applications, as well as more specialized functions designed to be called by the Migraine software (Rousset and Leblois, 2012 <doi:10.1093/molbev/MSR262>; Leblois et al., 2014 <doi:10.1093/molbev/msu212>; and see URL). The latter functions are used for prediction of likelihood surfaces and implied likelihood ratio confidence intervals, and for exploration of predictor space of the surface. Prediction of the response is based on ordinary Kriging (with residual error) of the input. Estimation of smoothing parameters is performed by generalized cross-validation.

Geographically referenced data and statistics of nighttime lights from NASA Black Marble <https://blackmarble.gsfc.nasa.gov/>.

Can be used to read and write a fwf with an accompanying 'Blaise' datamodel.
    Blaise is the software suite built by Statistics Netherlands (CBS). It is essentially a 
    way to write and collect surveys and perform statistical analysis on the data. It stores its data in 
    fixed width format with an accompanying metadata file, this is the Blaise format. The package automatically 
    interprets this metadata and reads the file into an R dataframe.
    When supplying a datamodel for writing, the dataframe will be automatically converted 
    to that format and checked for compatibility.
    Supports dataframes, tibbles and LaF objects.
    For more information about 'Blaise', see <https://blaise.com/products/general-information>.