c(person("Catherine", "Schramm", email = "cath.schramm@gmail.com", role = c("aut", "cre")), person("Aurelie", "Labbe", role = "ctb"), person("Celia M. T.", "Greenwood", role = "ctb"))
To fit the kernel semi-parametric model and its extensions. It allows multiple kernels and unlimited interactions in the same model. Coefficients are estimated by maximizing a penalized log-likelihood; penalization terms and hyperparameters are estimated by minimizing leave-one-out error. It includes predictions with confidence/prediction intervals, statistical tests for the significance of each kernel, a procedure for variable selection and graphical tools for diagnostics and interpretation of covariate effects. Currently it is implemented for continuous dependent variables.
Catherine Schramm [aut, cre],
Aurelie Labbe [ctb],
Celia M. T. Greenwood [ctb]

To fit the kernel semi-parametric model and its extensions. It allows multiple kernels and unlimited interactions in the same model. Coefficients are estimated by maximizing a penalized log-likelihood; penalization terms and hyperparameters are estimated by minimizing leave-one-out error. It includes predictions with confidence/prediction intervals, statistical tests for the significance of each kernel, a procedure for variable selection and graphical tools for diagnostics and interpretation of covariate effects. Currently it is implemented for continuous dependent variables. The package is based on the paper of Liu et al. (2007), <doi:10.1111/j.1541-0420.2007.00799.x>.

Implements the Lilliefors-corrected Kolmogorov-Smirnoff test for use
in goodness-of-fit tests, suitable when population parameters are unknown and
must be estimated by sample statistics. P-values are estimated by simulation.
Can be used with a variety of continuous distributions, including normal,
lognormal, univariate mixtures of normals, uniform, loguniform, exponential,
gamma, and Weibull distributions. Functions to generate random numbers and
calculate density, distribution, and quantile functions are provided for use
with the log uniform and mixture distributions.