Package: mcll
Type: Package
Title: Monte Carlo Local Likelihood Estimation
Version: 1.2
Date: 2014-3-1
Author: Minjeong Jeon, Cari Kaufman, and Sophia Rabe-Hesketh
Maintainer: Minjeong Jeon<jeon.117@osu.edu>
Depends: R (>= 2.13.0), statmod, locfit
Description: Maximum likelihood estimation using a Monte Carlo local likelihood (MCLL) method
License: GPL (>= 2)
LazyLoad: yes
Packaged: 2014-03-01 18:56:19 UTC; jeon
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-03-01 20:25:56
Package: lmeNB
Type: Package
Title: Compute the Personalized Activity Index Based on a Negative
Binomial Model
Version: 1.3
Date: 2015-01-14
Author: Yinshan Zhao and Yumi Kondo (with contributions from Steven G. Johnson, Rudolf Schuerer and Brian Gough on the integration subroutines)
Maintainer: Yumi Kondo <y.kondo@stat.ubc.ca>
Description: The functions in this package implement the safety monitoring procedures proposed in the paper titled "Detection of unusual increases in MRI lesion counts in individual multiple sclerosis patients" by Zhao, Y., Li, D.K.B., Petkau, A.J., Riddehough, A., Traboulsee, A., published in Journal of the American Statistical Association in 2013. The procedure first models longitudinally collected count variables with a negative binomial mixed-effect regression model. To account for the correlation among repeated measures from the same patient, the model has subject-specific random intercept, which can be modelled with a gamma or log-normal distributions. One can also choose the semi-parametric option which does not assume any distribution for the random effect. These mixed-effect models could be useful beyond the application of the safety monitoring. The maximum likelihood methods are used to estimate the unknown fixed effect parameters of the model. Based on the fitted model, the personalized activity index is computed for each patient. Lastly, this package is companion to R package lmeNBBayes, which contains the functions to compute the Personalized Activity Index in Bayesian framework.
License: GPL (>= 2)
Copyright: The portion of numerical integration is based on Cubature,
Copyright (c) 2005-2013 Steven G. Johnson
(http://ab-initio.mit.edu/wiki/index.php/Cubature), which in
turn is based on HIntLib (also distributed under * the GNU GPL,
v2 or later), copyright (c) 2002-2005 Rudolf Schuerer and GNU
GSL (also distributed under the GNU GPL, v2 or later),
copyright (c) 1996-2000 Brian
Gough.(http://www.gnu.org/software/gsl/).
LazyData: true
Depends: numDeriv, statmod, lmeNBBayes
Packaged: 2015-02-02 20:00:39 UTC; yumikondo
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2015-02-02 22:40:23
Package: mixcat
Type: Package
Title: Mixed effects cumulative link and logistic regression models
Version: 1.0-3
Date: 2011-12-22
Authors@R: c(person("Georgios", "Papageorgiou", role = c("aut", "cre"),
email = "gpapageo@gmail.com"), person("John", "Hinde", role =
"aut", email = "john.hinde@nuigalway.ie"))
Author: Georgios Papageorgiou and John Hinde
Maintainer: Georgios Papageorgiou <gpapageo@gmail.com>
Depends: R (>= 2.8.1), statmod
LazyLoad: yes
Description: Mixed effects cumulative and baseline logit link models
for the analysis of ordinal or nominal responses, with
non-parametric distribution for the random effects
License: GPL (>= 2)
Packaged: 2012-11-13 14:41:16 UTC; gpapageo
Repository: CRAN
Date/Publication: 2012-11-13 15:32:08
Package: nlreg
Version: 1.2-2
Date: 2014-03-31
Title: Higher Order Inference for Nonlinear Heteroscedastic Models
Author: S original by Alessandra R. Brazzale <alessandra.brazzale@unipd.it>
and Ruggero Bellio <ruggero.bellio@uniud.it>. R port by Alessandra R.
Brazzale <alessandra.brazzale@unipd.it>, following earlier work by
Douglas Bates.
Maintainer: Alessandra R. Brazzale <alessandra.brazzale@unipd.it>
URL: http://www.r-project.org
Description: Likelihood inference based on higher order approximations
for nonlinear models with possibly non constant variance
Depends: R (>= 3.0.0), statmod, survival
Suggests: boot, cond, csampling, marg
License: GPL (>= 2) | file LICENCE
LazyLoad: yes
LazyData: yes
Packaged: 2014-04-03 08:32:56 UTC; brazzale
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-04-03 14:00:52
Package: marg
Version: 1.2-2
Date: 2014-03-31
Title: Approximate marginal inference for regression-scale models
Author: S original by Alessandra R. Brazzale <alessandra.brazzale@unipd.it>.
R port by Alessandra R. Brazzale <alessandra.brazzale@unipd.it>, following
earlier work by Douglas Bates.
Maintainer: Alessandra R. Brazzale <alessandra.brazzale@unipd.it>
Depends: R (>= 3.0.0), statmod, survival
Suggests: boot, cond, csampling, nlreg
Description: Likelihood inference based on higher order approximations
for linear nonnormal regression models
License: GPL (>= 2) | file LICENCE
URL: http://www.r-project.org
LazyLoad: yes
LazyData: yes
Packaged: 2014-04-03 08:30:00 UTC; brazzale
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-04-03 14:00:49
Package: MVR
Type: Package
Title: Mean-Variance Regularization
Version: 1.30.3
Date: 2016-01-28
Authors@R: c(person("Jean-Eudes", "Dazard",
role = c("aut", "cre"),
email = "jxd101@case.edu"),
person("Hua", "Xu",
role = "ctb",
email = "huaxu77@gmail.com"),
person("Alberto", "Santana",
role = "ctb",
email = "ahs4@case.edu"))
Author: Jean-Eudes Dazard [aut, cre], Hua Xu [ctb], Alberto Santana [ctb]
Maintainer: Jean-Eudes Dazard <jxd101@case.edu>
Description: This is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data. It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm). Among those are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. Key features include:
(i) Normalization and/or variance stabilization of the data,
(ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow),
(iii) Generation of diverse diagnostic plots,
(iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment.
Depends: R (>= 3.0.2), parallel, statmod
Imports: graphics, grDevices, methods, stats
URL: https://github.com/jedazard/MVR
Repository: CRAN
License: GPL (>= 3) | file LICENSE
LazyLoad: yes
LazyData: yes
Archs: i386, x64
Packaged: 2016-01-28 17:50:04 UTC; jxd101
NeedsCompilation: yes
Date/Publication: 2016-01-28 20:57:12
Package: PCS
Type: Package
Title: Calculate the probability of correct selection (PCS)
Version: 1.2
Date: 2013-08-16
Depends: statmod, multtest
Author: Jason Wilson
Maintainer: Jason Wilson <jason.wilson@biola.edu>
Description: Given k populations (can be in thousands), what is the probability that a given subset of size t contains the true top t populations? This package finds this probability and offers three tuning parameters (G, d, L) to relax the definition.
License: GPL-3
LazyLoad: yes
Packaged: 2013-08-16 19:23:44 UTC; jasonw
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-08-16 23:45:14
Package: GHQp
Type: Package
Title: Gauss Hermite Quadrature with pruning.
Version: 1.0
Date: 2014-03-04
Author: Freddy Hernandez Barajas
Maintainer: Freddy Hernandez Barajas <fhernanb@gmail.com>
Description: The GHQ function can be used to obtain the quadrature points and weights to approximate an integral in two or more dimensions. This function uses the pruning approach to eliminate that points that do not contribute to the approximation of the integral and increases computational cost. The advantage to conducting this elimination of points is the decrease in the number of times that the function of interest is evaluated. This advantage is crucial in mixed models in which we must address several integrations within an iterative process to obtain model parameters.
License: GPL (>= 2)
Depends: R (>= 2.10), statmod
Suggests: scatterplot3d
Packaged: 2014-04-09 14:36:24 UTC; user
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-04-09 16:46:24
Package: JointModel
Title: Semiparametric Joint Models for Longitudinal and Counting
Processes
Version: 1.0
Date: 2016-06-01
Author: Sehee Kim <seheek@umich.edu>
Maintainer: Sehee Kim <seheek@umich.edu>
Description: Joint fit of a semiparametric regression model for longitudinal responses and a semiparametric transformation model for time-to-event data.
Depends: R (>= 3.2.0), lme4, survival, splines, statmod
License: GPL-3
LazyData: TRUE
NeedsCompilation: no
Repository: CRAN
Packaged: 2016-06-08 18:35:05 UTC; seheek
Date/Publication: 2016-06-09 05:49:19
Package: csampling
Version: 1.2-2
Date: 2014-03-31
Title: Functions for Conditional Simulation in Regression-Scale Models
Author: S original by Alessandra R. Brazzale <alessandra.brazzale@unipd.it>.
R port by Alessandra R. Brazzale <alessandra.brazzale@unipd.it>.
Maintainer: Alessandra R. Brazzale <alessandra.brazzale@unipd.it>
Depends: R (>= 3.0.0), marg, statmod, survival
Description: Monte Carlo conditional inference for the parameters of a
linear nonnormal regression model
License: GPL (>= 2) | file LICENCE
URL: http://www.r-project.org
LazyLoad: yes
LazyData: yes
Packaged: 2014-04-03 08:28:05 UTC; brazzale
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-04-03 14:00:47
providing 
.