Fitting Semi-Parametric Generalized log-Gamma Regression Models
Bootstrap inference for a generalized log-gamma regression
coef.sglg
case-weight scheme
Build the basis matrix and the penalty matrix of cubic B-spline basis.
Deviance Residuals for a Generalized Log-gamma Regression Model
Density distribution function for a generalized log-gamma variable
Density Probability Distribution of a K-th Order Statistic from a Gene...
Tool to calculate the entropy for a generalized log-gamma distribution...
envelope.sglg
Extract Fitted Values
Fitting multiple linear Generalized Log-gamma Regression Models
gnfit
Tool to build the basis matrix and the penalty matrix of natural cubic...
Extract Log-Likehood
Measures of location, scale and shape measures for a generalized log-g...
Measures of location, scale, and shape based on quantile measures for ...
Mean Residual Lifetime Function for a Generalized Gamma Distribution
Random Sampling of K-th Order Statistics from a Generalized Log-gamma ...
Cumulative distribution function for a generalized log-gamma variable
Cumulative Probability Distribution of a K-th Order Statistic from a G...
Plotting a natural cubic splines or P-splines.
Plot simultaneously the Kaplan-Meier and parametric estimators of the ...
Quantile function for a generalized log-gamma distribution
Quantile Residuals for a Generalized Log-gamma Regression Model
Extract Model Residuals
response scheme
Random number generation for a generalized log-gamma distribution
Fitting semi-parametric generalized log-gamma regression models
shape
smoothp
Fitting semi-parametric generalized log-gamma regression models under ...
summary.sglg
Fitting linear generalized log-gamma regression models under the prese...
Survival, Hazard, and Cumulative Hazard functions for a Generalized Ga...
###################### # # # Extreme value case # # # ################...
vcov.sglg
Set of tools to fit a linear multiple or semi-parametric regression models with the possibility of non-informative random right or left 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 likelihood, penalized likelihood and bootstrap methods. Lastly, some numerical and graphical devices for diagnostic of the fitted models are offered.