Package: MDM
Type: Package
Title: Multinomial Diversity Model
Version: 1.3
Date: 2013-06-28
Author: Glenn De'ath <g.death@aims.gov.au>; Code for mdm was adapted from multinom in the nnet package (Brian Ripley <ripley@stats.ox.ac.uk> and Bill Venables <Bill.Venables@csiro.au>)
Maintainer: Glenn De'ath <g.death@aims.gov.au>
Depends: R (>= 2.9.0), nnet
Description: The multinomial diversity model is a toolbox for relating diversity to complex predictors. It is based on (1) Shannon diversity; (2) the multinomial logit model, and (3) the link between Shannon diversity and the log-likelihood of the MLM.
License: GPL-2 | GPL-3
Packaged: 2013-06-30 17:27:55 UTC; root
Repository: CRAN
Date/Publication: 2013-07-10 11:12:01
NeedsCompilation: no
Package: HydeNet
Type: Package
Title: Hybrid Bayesian Networks Using R and JAGS
Version: 0.10.3
Date: 2015-01-18
Author: Jarrod E. Dalton <daltonj@ccf.org> and Benjamin Nutter <benjamin.nutter@gmail.com>
Maintainer: Benjamin Nutter <benjamin.nutter@gmail.com>
Description: Facilities for easy implementation of hybrid Bayesian networks
using R. Bayesian networks are directed acyclic graphs representing joint
probability distributions, where each node represents a random variable and
each edge represents conditionality. The full joint distribution is
therefore factorized as a product of conditional densities, where each node
is assumed to be independent of its non-descendents given information on its
parent nodes. Since exact, closed-form algorithms are computationally
burdensome for inference within hybrid networks that contain a combination
of continuous and discrete nodes, particle-based approximation techniques
like Markov Chain Monte Carlo are popular. We provide a user-friendly
interface to constructing these networks and running inference using the 'rjags' package.
Econometric analyses (maximum expected utility under competing policies,
value of information) involving decision and utility nodes are also
supported.
License: MIT + file LICENSE
Depends: R (>= 3.0.0), nnet, rjags
Imports: ArgumentCheck, DiagrammeR (>= 0.8), plyr, dplyr, graph, gRbase, magrittr, pixiedust (>= 0.6.1), stats, stringr, utils
Suggests: knitr, survival, testthat
VignetteBuilder: knitr
SystemRequirements: JAGS (http://mcmc-jags.sourceforge.net)
LazyLoad: yes
LazyData: true
URL: https://github.com/nutterb/HydeNet,
BugReports: https://github.com/nutterb/HydeNet/issues
NeedsCompilation: no
Packaged: 2016-02-05 13:36:55 UTC; Nutter
Repository: CRAN
Date/Publication: 2016-02-05 19:41:00
Package: LOGICOIL
Type: Package
Version: 0.99.0
Date: 2014-04-12
Title: LOGICOIL: multi-state prediction of coiled-coil oligomeric
state.
Author: Thomas L. Vincent <tlfvincent@gmail.com>, Peter J. Green and Derek N. Woolfson <D.N.Woolfson@bristol.ac.uk>
Maintainer: Thomas L. Vincent <tlfvincent@gmail.com>
Depends: R (>= 2.12), nnet
LazyData: true
ZipData: no
License: GPL (>= 2)
Description: This package contains the functions necessary to run the LOGICOIL algorithm. LOGICOIL can be used to differentiate between antiparallel dimers, parallel dimers, trimers and higher-order coiled-coil sequence. By covering >90 percent of the known coiled-coil structures, LOGICOIL is a net improvement compared with other existing methods, which achieve a predictive coverage of around 31 percent of this population. As such, LOGICOIL is particularly useful for researchers looking to characterize novel coiled-coil sequences or studying coiled-coil containing protein assemblies. It may also be used to assist in the structural characterization of synthetic coiled-coil sequences.
Repository: CRAN
URL: http://coiledcoils.chm.bris.ac.uk/LOGICOIL
Packaged: 2014-04-13 21:36:51 UTC; ThomasVincent
NeedsCompilation: no
Date/Publication: 2014-04-14 00:04:00
Package: CBPS
Version: 0.11
Date: 2016-05-12
Title: Covariate Balancing Propensity Score
Author: Christian Fong <christianfong@stanford.edu>, Marc Ratkovic <ratkovic@princeton.edu>, Chad Hazlett <chazlett@ucla.edu>, Xiaolin Yang <xiaoliny@princeton.edu>, Kosuke Imai <kimai@princeton.edu>
Maintainer: Christian Fong <christianfong@stanford.edu>
Depends: R (>= 2.14), MASS, MatchIt, nnet, numDeriv
Imports:
Description: Implements the covariate balancing propensity score (CBPS) proposed by Imai and Ratkovic (2014) <DOI:10.1111/rssb.12027>. The propensity score is estimated such that it maximizes the resulting covariate balance as well as the prediction of treatment assignment. The method, therefore, avoids an iteration between model fitting and balance checking. The package also implements several extensions of the CBPS beyond the cross-sectional, binary treatment setting. The current version implements the CBPS for longitudinal settings so that it can be used in conjunction with marginal structural models from Imai and Ratkovic (2015) <DOI:10.1080/01621459.2014.956872>, treatments with three- and four-valued treatment variables, continuous-valued treatments from Fong, Hazlett, and Imai (2015) <http://imai.princeton.edu/research/files/CBGPS.pdf>, and the situation with multiple distinct binary treatments administered simultaneously. In the future it will be extended to other settings including the generalization of experimental and instrumental variable estimates. Recently add the optimal CBPS which chooses the optimal balancing function and results in doubly robust and efficient estimator for the treatment effect.
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
Packaged: 2016-05-12 16:04:12 UTC; Christian
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2016-05-13 00:44:24
providing 
.