Depends: R (>= 2.10.0), numDeriv, MASS, Rsolnp

Description: 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>.

Description: Functions for semiparametric regression analysis,
Ruppert, D., Wand, M.P. and Carroll, R.J. (2003).
Semiparametric Regression. Cambridge University Press.
Packaged: Mon Apr  4 11:58:49 2005; wand

Packaged: Thu Nov 30 12:20:01 2006; wand

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

X-CRAN-Comment: Orphaned on 2013-03-23 as the maintainer is

Author: Giampiero Marra and Rosalba Radice
Title: Semiparametric Bivariate Probit Modelling
Description: Routine for bivariate probit modelling with linear
predictors specified using parametric and nonparametric
Packaged: 2011-01-05 15:53:38 UTC; giampiero

Author: Giampiero Marra <giampiero@stats.ucl.ac.uk> and Rosalba Radice
Packaged: 2011-03-01 10:41:56 UTC; giampiero

Packaged: 2011-03-17 16:04:37 UTC; giampiero

Packaged: 2011-04-01 10:34:06 UTC; giampiero

Packaged: 2011-04-12 15:37:19 UTC; giampiero

Packaged: 2011-05-03 10:51:59 UTC; giampiero

Packaged: 2011-05-09 16:10:11 UTC; giampiero

Packaged: 2011-05-16 16:14:45 UTC; giampiero

Description: Routine for bivariate probit modelling with semiparametric
predictors, including linear and nonlinear effects, in the
presence of correlated error equations, endogeneity or sample
Packaged: 2011-10-07 08:41:13 UTC; giampiero

Packaged: 2011-09-27 08:19:12 UTC; giampiero

Packaged: 2011-04-21 14:01:24 UTC; giampiero

<rosalba.radice@lshtm.ac.uk> with contributions from Georgios
Description: 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.
Depends: R (>= 2.14.0), MASS, VGAM, magic, statmod, mgcv, trust,
Packaged: 2012-01-10 12:55:12 UTC; giampiero