Package: dMod
Type: Package
Title: Dynamic Modeling and Parameter Estimation in ODE Models
Version: 0.3.1
Date: 2016-05-12
Author: Daniel Kaschek
Maintainer: Daniel Kaschek <daniel.kaschek@physik.uni-freiburg.de>
Description: The framework provides functions to generate ODEs of reaction
networks, parameter transformations, observation functions, residual functions,
etc. The framework follows the paradigm that derivative information should be
used for optimization whenever possible. Therefore, all major functions produce
and can handle expressions for symbolic derivatives.
License: GPL (>= 2)
Depends: cOde, trust, ggplot2, stringr
Suggests: MASS, rPython, deSolve, rootSolve, pander, knitr, rmarkdown
RoxygenNote: 5.0.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2016-05-18 14:28:57 UTC; kaschek
Repository: CRAN
Date/Publication: 2016-05-18 17:26:06
Package: glmm
Type: Package
Title: Generalized Linear Mixed Models via Monte Carlo Likelihood
Approximation
Version: 1.0.4
Date: 2015-07-24
Author: Christina Knudson
Maintainer: Christina Knudson <christina@stat.umn.edu>
Description: Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information.
License: GPL-2
Depends: R (>= 3.1.1), trust, mvtnorm, Matrix
Imports: stats
ByteCompile: TRUE
NeedsCompilation: yes
VignetteBuilder: knitr
Suggests: knitr
Packaged: 2015-07-24 16:08:36 UTC; christina
Repository: CRAN
Date/Publication: 2015-07-24 19:05:33
Package: iWeigReg
Type: Package
Title: Improved methods for causal inference and missing data problems
Version: 1.0
Date: 2013-02-20
Author: Zhiqiang Tan and Heng Shu
Maintainer: Zhiqiang Tan <ztan@stat.rutgers.edu>
URL: http://www.stat.rutgers.edu/~ztan
Description: Improved methods based on inverse probability weighting
and outcome regression for causal inference and missing data
problems
Depends: R (>= 2.9.1), MASS (>= 7.2-1), trust
License: GPL (>= 2)
Packaged: 2013-02-25 06:47:17 UTC; ripley
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-02-25 07:47:37
Package: aster
Version: 0.8-31
Date: 2015-07-16
Title: Aster Models
Author: Charles J. Geyer <charlie@stat.umn.edu>.
Maintainer: Charles J. Geyer <charlie@stat.umn.edu>
Depends: R (>= 2.10.0), trust
Imports: stats
ByteCompile: TRUE
Description: Aster models are exponential family regression models for life
history analysis. They are like generalized linear models except that
elements of the response vector can have different families (e. g.,
some Bernoulli, some Poisson, some zero-truncated Poisson, some normal)
and can be dependent, the dependence indicated by a graphical structure.
Discrete time survival analysis, zero-inflated Poisson regression, and
generalized linear models that are exponential family (e. g., logistic
regression and Poisson regression with log link) are special cases.
Main use is for data in which there is survival over discrete time periods
and there is additional data about what happens conditional on survival
(e. g., number of offspring). Uses the exponential family canonical
parameterization (aster transform of usual parameterization).
License: MIT + file LICENSE
URL: http://www.stat.umn.edu/geyer/aster/
NeedsCompilation: yes
Packaged: 2015-07-17 15:15:42 UTC; geyer
Repository: CRAN
Date/Publication: 2015-07-17 19:58:13
providing 
.