or UBRE/AIC. Also GAMMs. Includes a gam() function.
Imports: methods, stats, graphics, Matrix

Suggests: splines, parallel, survival, MASS

Title: Mixed GAM Computation Vehicle with GCV/AIC/REML Smoothness
Description: GAMs, GAMMs and other generalized ridge regression with
multiple smoothing parameter estimation by GCV, REML or UBRE/AIC.
Includes a gam() function, a wide variety of smoothers, JAGS
support and distributions beyond the exponential family.

Title: Mixed GAM Computation Vehicle with Automatic Smoothness
Description: Generalized additive (mixed) models, some of their extensions and
other generalized ridge regression with multiple smoothing
parameter estimation by (REstricted) Marginal Likelihood,
Generalized Cross Validation and similar. Includes a gam()
function, a wide variety of smoothers, JAGS support and
distributions beyond the exponential family.

parameter estimation by (Restricted) Marginal Likelihood,

Generalized Cross Validation and similar, or using iterated
nested Laplace approximation for fully Bayesian inference. See
Includes a gam() function, a wide variety of smoothers, 'JAGS'
Imports: methods, stats, graphics, Matrix, splines, utils

Title: Marginal Analysis of Misclassified Longitudinal Ordinal Data
person("Yuliang", "Xu", email = "y455xu@uwaterloo.ca", role = c("aut","cre")),
person("Zhijian", "Chen", email = " ", role = "aut"),
person("Shuo Shuo", "Liu", email = "shuoshuo.liu@psu.edu", role = "aut"),
person("Grace", "Yi", email = "yyi@uwaterloo.ca", role = "aut"))
Description: Three estimating equation methods are provided in this package for marginal analysis of longitudinal ordinal data with misclassified responses and covariates.
The naive analysis which is solely based on the observed data without adjustment may lead to bias.
The corrected generalized estimating equations (GEE2) method which is unbiased requires the misclassification parameters to be known beforehand.
The corrected generalized estimating equations (GEE2) with validation subsample method estimates the misclassification parameters based on a given
validation set. This package is an implementation