Package: mdsdt
Version: 1.2
Date: 2016-03-11
Title: Functions for Analysis of Data with General Recognition Theory
Author: Robert X.D. Hawkins <rxdh@stanford.edu>, Joe Houpt
<joseph.houpt@wright.edu>, Noah Silbert <noahpoah@gmail.com>, Leslie
Blaha <Leslie.Blaha@wpafb.af.mil>, Thomas D. Wickens
<twickens@socrates.berkeley.edu>
Maintainer: Robert X.D. Hawkins <rxdh@stanford.edu>
Depends: R (>= 1.8.0), ellipse, mnormt, polycor
Description: Tools associated with General
Recognition Theory (Townsend & Ashby, 1986), including Gaussian model fitting of 2x2 and more
general designs, associated plotting and model comparison tools,
and tests of marginal response invariance and report independence.
License: GPL (>= 2)
Collate: 'grt-data.R' 'grt_base.R'
NeedsCompilation: no
Packaged: 2016-03-12 08:23:53 UTC; rxdh
Repository: CRAN
Date/Publication: 2016-03-12 09:57:29
Package: DiceOptim
Version: 1.5
Title: Kriging-Based Optimization for Computer Experiments
Date: 2015-03-10
Author: D. Ginsbourger, V. Picheny, O. Roustant, with contributions by
C. Chevalier, S. Marmin, and T. Wagner
Maintainer: D. Ginsbourger <david.ginsbourger@stat.unibe.ch>
Description: Expected Improvement. EGO algorithm. Multipoint EI and parallelized versions of EGO. Criteria and algorithms for Noisy Kriging-based Optimization , including AEI, AKG, EQI, and EI with plugin.
Depends: DiceKriging (>= 1.2), rgenoud, MASS, lhs, mnormt
License: GPL-2 | GPL-3
URL: http://dice.emse.fr/
Packaged: 2015-03-10 16:49:26 UTC; ginsbourger
Repository: CRAN
Date/Publication: 2015-03-10 18:32:47
NeedsCompilation: no
Package: PLordprob
Type: Package
Title: Multivariate Ordered Probit Model via Pairwise Likelihood
Version: 1.0
Date: 2014-09-29
Author: Euloge Clovis Kenne Pagui [aut,cre], Antonio Canale [aut], Alan Genz [ctb], Adelchi Azzalini [ctb]
Maintainer: Euloge Clovis Kenne Pagui <kenne@stat.unipd.it>
Depends: mnormt
Description: Multivariate ordered probit model, i.e. the extension of the scalar ordered probit model where the observed variables have dimension greater than one. Estimation of the parameters is done via maximization of the pairwise likelihood, a special case of the composite likehood obtained as product of bivariate marginal distributions. The package uses the Fortran 77 subroutine SADMVN by Alan Genz, with minor adaptations made by Adelchi Azzalini in his "mvnormt" package for evaluating the two-dimensional Gaussian integrals involved in the pairwise log-likelihood. Optimization of the latter objective function is performed via quasi-Newton box-constrained optimization algorithm, as implemented in nlminb.
License: GPL-2
Packaged: 2014-10-07 14:29:04 UTC; kenne
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-10-07 18:28:10
Package: StratSel
Type: Package
Title: Strategic Selection Estimator
Version: 1.2
Author: Lucas Leemann
Maintainer: Lucas Leemann <lleemann@gmail.com>
Description: Provides functions to estimate a strategic selection estimator. A strategic selection estimator is an agent error model in which the two random components are not assumed to be orthogonal. In addition this package provides generic functions to print and plot objects of its class as well as the necessary functions to create tables for LaTeX. There is also a function to create dyadic data sets.
License: GPL (>= 2)
Depends: R (>= 3.2.0), MASS, memisc, Formula, mnormt, pbivnorm
LazyData: TRUE
NeedsCompilation: no
Packaged: 2016-05-09 16:18:30 UTC; lleemann
Repository: CRAN
Date/Publication: 2016-05-10 10:30:29
Package: Rgbp
Version: 1.1.1
Date: 2015-08-03
Title: Hierarchical Modeling and Frequency Method Checking on
Overdispersed Gaussian, Poisson, and Binomial Data
Author: Joseph Kelly, Hyungsuk Tak, and Carl Morris
Maintainer: Joseph Kelly <josephkelly@post.harvard.edu>
Depends: R (>= 2.2.0), sn (>= 0.4-18), mnormt (>= 1.5-1)
Description: We utilize approximate Bayesian machinery to fit two-level conjugate hierarchical models on overdispersed Gaussian, Poisson, and Binomial data and evaluates whether the resulting approximate Bayesian interval estimates for random effects meet the nominal confidence levels via frequency coverage evaluation. The data that Rgbp assumes comprise observed sufficient statistic for each random effect, such as an average or a proportion of each group, without population-level data. The approximate Bayesian tool equipped with the adjustment for density maximization produces approximate point and interval estimates for model parameters including second-level variance component, regression coefficients, and random effect. For the Binomial data, the package provides an option to produce posterior samples of all the model parameters via the acceptance-rejection method. The package provides a quick way to evaluate coverage rates of the resultant Bayesian interval estimates for random effects via a parametric bootstrapping, which we call frequency method checking.
License: GPL-2
BugReports: https://github.com/jyklly/Rgbp/issues
NeedsCompilation: no
Packaged: 2016-01-12 00:10:48 UTC; hyungsuktak
Repository: CRAN
Date/Publication: 2016-01-13 18:15:21
Package: flexCWM
Type: Package
Title: Flexible Cluster-Weighted Modeling
Version: 1.5
Date: 2015-03-25
Author: Mazza A., Punzo A., Ingrassia S.
Maintainer: Angelo Mazza <a.mazza@unict.it>
Description: Allows for maximum likelihood fitting of cluster-weighted models, a class of mixtures of regression models with random covariates.
License: GPL-2
LazyLoad: yes
Depends: R (>= 3.0.0)
Imports:
parallel,numDeriv,mnormt,mclust,ellipse,mixture,Flury,adehabitat,MASS,statmod
NeedsCompilation: no
Packaged: 2015-03-25 12:38:01 UTC; Angelo
Repository: CRAN
Date/Publication: 2015-03-25 16:07:42
Package: FAMT
Type: Package
Title: Factor Analysis for Multiple Testing (FAMT) : simultaneous tests
under dependence in high-dimensional data
Version: 2.5
Date: 2013-09-30
Author: David Causeur, Chloe Friguet, Magalie Houee-Bigot, Maela Kloareg
Maintainer: David Causeur <David.Causeur@agrocampus-ouest.fr>
Depends: mnormt, impute
Description: The method proposed in this package takes into account the impact of dependence on the multiple testing procedures for high-throughput data as proposed by Friguet et al. (2009). The common information shared by all the variables is modeled by a factor analysis structure. The number of factors considered in the model is chosen to reduce the false discoveries variance in multiple tests. The model parameters are estimated thanks to an EM algorithm. Adjusted tests statistics are derived, as well as the associated p-values. The proportion of true null hypotheses (an important parameter when controlling the false discovery rate) is also estimated from the FAMT model. Graphics are proposed to interpret and describe the factors.
LazyLoad: yes
License: GPL (>= 2)
URL: http://famt.free.fr/
Packaged: 2014-01-02 14:02:18 UTC; ripley
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-01-02 15:15:13
providing 
.