Package: mclogit
Type: Package
Title: Mixed Conditional Logit
Version: 0.3-1
Date: 2014-10-13
Author: Martin Elff
Maintainer: Martin Elff <elff@gmx.com>
Description: This packages provides a function to estimate parameters for
the conditional logit model (also with multinomial counts), and for the
mixed conditional logit model, or conditional logit with random effects
(random intercepts only, no random slopes yet).
The current implementation of random effects is limited to
the PQL technique, which requires large cluster sizes.
License: GPL-2
Depends: stats, Matrix
Enhances: memisc
LazyLoad: Yes
Packaged: 2014-10-13 21:17:58 UTC; elff
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-10-14 05:27:20
Package: mdhglm
Type: Package
Title: Multivariate Double Hierarchical Generalized Linear Models
Version: 1.4
Date: 2016-06-28
Author: Youngjo Lee, Marek Molas, Maengseok Noh
Maintainer: Maengseok Noh <msnoh@pknu.ac.kr>
Description: Allows various models for multivariate response variables where each response is assumed to follow double hierarchical generalized linear models. In double hierarchical generalized linear models, the mean, dispersion parameters for variance of random effects, and residual variance can be further modeled as random-effect models.
Depends: R (>= 3.2.0), methods, Matrix, numDeriv, boot, mvtnorm
License: Unlimited
NeedsCompilation: no
Packaged: 2016-06-28 06:47:37 UTC; msnoh
Repository: CRAN
Date/Publication: 2016-06-28 08:56:42
Package: metafor
Version: 1.9-8
Date: 2015-09-28
Title: Meta-Analysis Package for R
Authors@R: person("Wolfgang", "Viechtbauer", email = "wvb@metafor-project.org", role = c("aut","cre"))
Depends: R (>= 3.2.2), Matrix
Imports: stats, utils, graphics, grDevices, methods
Suggests: lme4, numDeriv, minqa, nloptr, dfoptim, ucminf, CompQuadForm, mvtnorm, Formula, R.rsp, testthat, BiasedUrn, survival, Epi, igraph, multcomp, plyr
Description: A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted.
License: GPL (>= 2)
ByteCompile: TRUE
LazyData: TRUE
Encoding: UTF-8
VignetteBuilder: R.rsp
URL: http://www.metafor-project.org
NeedsCompilation: no
Packaged: 2015-09-28 15:08:19 UTC; Wolfgang
Author: Wolfgang Viechtbauer [aut, cre]
Maintainer: Wolfgang Viechtbauer <wvb@metafor-project.org>
Repository: CRAN
Date/Publication: 2015-09-28 17:40:48
Package: mht
Type: Package
Title: Multiple Hypothesis Testing for Variable Selection in
High-Dimensional Linear Models
Version: 3.1.2
Author: Florian Rohart
Maintainer: Florian Rohart <florian.rohart@gmail.com>
Description: Multiple Hypothesis Testing For Variable Selection in high dimensional linear models. This package performs variable selection with multiple hypothesis testing, either for ordered variable selection or non-ordered variable selection. In both cases, a sequential procedure is performed. It starts to test the null hypothesis "no variable is relevant"; if this hypothesis is rejected, it then tests "only the first variable is relevant", and so on until the null hypothesis is accepted.
License: GPL-3
Depends: glmnet, Matrix
Packaged: 2015-03-21 23:15:11 UTC; florian
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2015-03-23 07:33:03
Package: mi
Type: Package
Title: Missing Data Imputation and Model Checking
Version: 1.0
Date: 2015-04-16
Authors@R: c(person("Andrew", "Gelman", email = "gelman@stat.columbia.edu", role = "ctb"),
person("Jennifer", "Hill", email = "jennifer.hill@nyu.edu", role = "ctb"),
person("Yu-Sung", "Su", email = "suyusung@tsinghua.edu.cn", role = c("aut")),
person("Masanao", "Yajima", email = "my2167@columbia.edu", role = "ctb"),
person("Maria", "Pittau", email = "grazia@stat.columbia.edu", role = "ctb"),
person("Ben", "Goodrich", email = "benjamin.goodrich@columbia.edu", role = c("cre", "aut")),
person("Yajuan", "Si", email = "sophie2012@gmail.com", role = "ctb"),
person("Jon", "Kropko", email = "jkropko@gmail.com", role = "aut"))
Description: The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.
VignetteBuilder: knitr
Depends: R (>= 3.0.0), methods, Matrix, stats4
Imports: arm (>= 1.4-11)
Suggests: betareg, lattice, knitr, MASS, nnet, parallel, sn, survival, truncnorm, foreign
URL: http://www.stat.columbia.edu/~gelman/
License: GPL (>= 2)
LazyLoad: yes
Packaged: 2015-04-16 14:03:10 UTC; goodrich
Author: Andrew Gelman [ctb],
Jennifer Hill [ctb],
Yu-Sung Su [aut],
Masanao Yajima [ctb],
Maria Pittau [ctb],
Ben Goodrich [cre, aut],
Yajuan Si [ctb],
Jon Kropko [aut]
Maintainer: Ben Goodrich <benjamin.goodrich@columbia.edu>
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-04-16 19:53:48
Package: lassoscore
Title: High-Dimensional Inference with the Penalized Score Test
Description: Use the lasso regression method to perform approximate inference
in high dimensions, by penalizing the effects of nuisance parameters.
Version: 0.6
Author: Arie Voorman <arie.voorman@gmail.com>
Maintainer: Arie Voorman <arie.voorman@gmail.com>
Depends: R (>= 2.10), glasso, glmnet, Matrix
Suggests: covTest, lars
License: GPL (>= 2)
LazyData: true
Packaged: 2014-10-28 04:56:25 UTC; arie
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-10-28 08:10:02
Package: lfe
Version: 2.5-1998
Date: 2016-04-18
Title: Linear Group Fixed Effects
Author: Simen Gaure, Ragnar Frisch Centre for Economic Research
Maintainer: Simen Gaure <Simen.Gaure@frisch.uio.no>
Copyright: 2011-2016, Simen Gaure
Depends: R (>= 2.15.2), Matrix (>= 1.1-2)
Imports: Formula, xtable, compiler, utils, methods, sandwich
Suggests: knitr, igraph, plm, R2Cuba, numDeriv
VignetteBuilder: knitr
ByteCompile: yes
Description: Transforms away factors with many levels prior to doing an OLS.
Useful for estimating linear models with multiple group fixed effects, and for
estimating linear models which uses factors with many levels as pure control
variables. Includes support for instrumental variables, conditional F statistics
for weak instruments, robust and multi-way clustered standard errors, as well as
limited mobility bias correction.
License: Artistic-2.0
Classification/JEL: C13, C23, C60
Classification/MSC: 62J05, 65F10, 65F50
RoxygenNote: 5.0.1
NeedsCompilation: yes
Packaged: 2016-04-18 09:20:05 UTC; sgaure
Repository: CRAN
Date/Publication: 2016-04-19 00:34:26
providing 
.