Title: Semiparametric Sample Selection Modelling with Continuous
Description: Routine for fitting continuous response sample selection
models with semiparametric predictors, including linear and
Depends: R (>= 2.14.0), magic, mgcv, trust, mvtnorm
Packaged: 2012-04-25 08:41:56 UTC; giampiero

Author: Giampiero Marra <giampiero.marra@ucl.ac.uk>, Rosalba Radice
<r.radice@bbk.ac.uk> and Malgorzata Wojtys <m.wojtys@ucl.ac.uk>
Description: Routine for fitting continuous response copula sample
selection models with semiparametric predictors, including
Depends: R (>= 2.14.0), magic, mgcv, trust, mvtnorm, copula
Packaged: 2013-02-26 10:37:55 UTC; giampiero

Author: Giampiero Marra, Rosalba Radice and Malgorzata Wojtys
Description: Routine for fitting continuous response copula sample selection models with semiparametric predictors, including linear and nonlinear effects.
Depends: R (>= 2.15.1), copula, mgcv, mvtnorm
Packaged: 2014-08-27 10:53:40 UTC; giampiero

Author: Giampiero Marra, Rosalba Radice, Malgorzata Wojtys and Karol Wyszynski
Title: Semiparametric Sample Selection Modelling with Continuous or
Description: Routine for fitting continuous or discrete response copula sample selection models with semiparametric predictors, including linear and nonlinear effects.
Depends: R (>= 3.1.1), copula, mgcv, mvtnorm, gamlss.dist

Imports: magic, trust, VGAM, Matrix, graphics, grDevices, stats, utils

Title: Semi-Parametric Sample Selection Modelling with Continuous or
Description: Routine for fitting continuous or discrete response copula sample selection models with semi-parametric predictors, including linear and nonlinear effects.
Imports: magic, trust, VGAM, Matrix, graphics, grDevices, stats, utils,

Packaged: 2017-05-17 17:32:29 UTC; Giampiero

Title: Safe Semi-Supervised Learning Tools
Maintainer: Mark Vere Culp <mvculp@mail.wvu.edu>
Description: Implements several safe graph-based semi-supervised learning algorithms. The first algorithm is the Semi-Supervised Semi-Parametric Model (S4PM) and the fast Anchor Graph version of this approach.  For additional technical details, refer to Culp and Ryan (2013) <http://jmlr.org/papers/v14/culp13a.html>, Ryan and Culp (2015) <http://www.jmlr.org/papers/v16/ryan15a.html> and the package vignette. The underlying fitting routines are executed in C++. All tuning parameter estimation is optimized using K-fold Cross-Validation.