<code>Gu</code> builds the basis matrix and penalty matrix to approximate a smooth function using
natural cubic splines based on the Gu basis form.

Set of tools to fit a linear multiple or semi-parametric regression
models with the possibility of non-informative random right-censoring.
Under this setup, the localization parameter of the response variable distribution is modeled by using linear multiple regression
or semi-parametric functions, whose non-parametric components may be approximated
by natural cubic spline or P-splines. The supported distribution for the model error is a generalized log-gamma distribution which includes
the generalized extreme value and standard normal distributions as important special cases. Inference is based on penalized likelihood and bootstrap methods.
Also, some numerical and graphical devices for diagnostic of the fitted models are offered.

Carlos Cardozo Delgado

sglg

Fitting Semi-Parametric Generalized log-Gamma Regression Models

Gu function

<dl><dt>t</dt>
<dd>the covariate.</dd>
<dt>knot</dt>
<dd>a integer value that represent the number of knots of the natural cubic spline.</dd></dl>

Arguments

Carlos Alberto Cardozo Delgado &lt;cardozorpackages@gmail.com&gt;

Author

Tool to build the basis matrix and the penalty matrix of natural cubic splines. — Gu

<dl>

<dt>t</dt>
<dd>the covariate.</dd>


<dt>knot</dt>
<dd>a integer value that represent the number of knots of the natural cubic spline.</dd>

</dl>

Gu: Tool to build the basis matrix and the penalty matrix of natural cubic splines.

Description

Usage

Value

Arguments

Author

References

Examples