Functions for fitting multi-state semi-Markov models to longitudinal data. A parametric maximum likelihood estimation method adapted to deal with Exponential, Weibull and Exponentiated Weibull distributions is considered. Right-censoring can be taken into account and both constant and time-varying covariates can be included using a Cox proportional model. Reference: A. Krol and P. Saint-Pierre (2015) 	<doi:10.18637/jss.v066.i06>.

Functions for semiparametric regression analysis,
to complement the book:
Ruppert, D., Wand, M.P. and Carroll, R.J. (2003).
Semiparametric Regression. Cambridge University Press.

Functions for semiparametric regression analysis, to
complement the book: Ruppert, D., Wand, M.P. and Carroll, R.J.
(2003). Semiparametric Regression. Cambridge University Press.

Orphaned on 2013-03-23 as the maintainer is
unresponsive.

Routine for bivariate probit modelling with linear
predictors specified using parametric and nonparametric
components.

Giampiero Marra <giampiero@stats.ucl.ac.uk> and Rosalba Radice
<rosalba.radice@lshtm.ac.uk>

Routine for bivariate probit modelling with semiparametric
predictors, including linear and nonlinear effects, in the
presence of correlated error equations, endogeneity or sample
selection.

Giampiero Marra <giampiero@stats.ucl.ac.uk> and Rosalba Radice
<rosalba.radice@lshtm.ac.uk> with contributions from Georgios
Papageorgiou <gpapageo@gmail.com>
Routine for fitting bivariate probit models with
semiparametric predictors (including linear, nonlinear and
nonparametric random effects) in the presence of correlated
error equations, endogeneity or sample selection.
R (>= 2.14.0), MASS, VGAM, magic, statmod, mgcv, trust,
mvtnorm

Routine for fitting bivariate probit models with semiparametric predictors (including linear and nonlinear effects) in the presence of correlated error equations, endogeneity or sample selection. Bivariate copula models are also supported.
MASS, magic, polycor, VineCopula, survey, trust, matrixStats,
Matrix

MASS, magic, polycor, VineCopula, VGAM, survey, trust,
matrixStats, Matrix, sn