Package: PSM
Type: Package
Title: Non-Linear Mixed-Effects modelling using Stochastic Differential
Equations.
Version: 0.8-10
Date: 2013-09-10
Encoding: latin1
Author: Stig Bousgaard Mortensen <stigbm@gmail.com> and Søren Klim
<soren@klimens.dk>
Maintainer: Stig Bousgaard Mortensen <stigbm@gmail.com>
Depends: MASS, numDeriv, deSolve, ucminf
Description: This package provides functions for estimation of linear
and non-linear mixed-effects models using stochastic
differential equations. Moreover it provides functions for
finding smoothed estimates of model states and for simulation.
The package allows for any multivariate non-linear time-variant
model to be specified, and it also handles multidimensional
input, co-variates, missing observations and specification of
dosage regimen.
License: GPL (>= 2)
URL: http://www.imm.dtu.dk/psm
Packaged: 2013-09-10 17:50:40 UTC; sbm
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2013-09-10 20:16:22
Package: Benchmarking
Type: Package
Title: Benchmark and Frontier Analysis Using DEA and SFA
Version: 0.26
Date: 2015-7-8 ($Date: 2015-07-08 12:32:10 +0200 (on, 08 jul 2015) $)
Author: Peter Bogetoft and Lars Otto
Maintainer: Lars Otto <larsot23@gmail.com>
Depends: lpSolveAPI, ucminf
Imports: methods, stats, graphics, grDevices
Description: Methods for frontier
analysis, Data Envelopment Analysis (DEA), under different
technology assumptions (fdh, vrs, drs, crs, irs, add/frh, and fdh+),
and using different efficiency measures (input based, output based,
hyperbolic graph, additive, super, and directional efficiency). Peers
and slacks are available, partial price information can be included,
and optimal cost, revenue and profit can be calculated. Evaluation of
mergers is also supported. Methods for graphing the technology sets
are also included. There is also support comparative methods based
on Stochastic Frontier Analyses (SFA). In general, the methods can be
used to solve not only standard models, but also many other model
variants. It complements the book, Bogetoft and Otto,
Benchmarking with DEA, SFA, and R, Springer-Verlag, 2011, but can of
course also be used as a stand-alone package.
License: GPL (>= 2)
LazyLoad: yes
NeedsCompilation: no
Packaged: 2015-07-08 14:36:25 UTC; b002961
Repository: CRAN
Date/Publication: 2015-07-08 17:44:18
Package: ordinalCont
Title: Ordinal Regression Analysis for Continuous Scales
Version: 0.4
Authors@R: c(person("Maurizio Manuguerra", role = c("aut", "cre"),
email = "maurizio.manuguerra@mq.edu.au"),
person("Gillian Heller", role = "aut", email = "gillian.heller@mq.edu.au"))
Author: Maurizio Manuguerra [aut, cre],
Gillian Heller [aut]
Maintainer: Maurizio Manuguerra <maurizio.manuguerra@mq.edu.au>
Description: A regression framework for response variables which are continuous self-rating scales such as the Visual Analog Scale (VAS) used in pain assessment, or the Linear Analog Self-Assessment (LASA) scales in quality of life studies. These scales measure subjects' perception of an intangible quantity, and cannot be handled as ratio variables because of their inherent nonlinearity. We treat them as ordinal variables, measured on a continuous scale. A function (the g function, currently the generalized logistic function) connects the scale with an underlying continuous latent variable. The link function is the inverse of the CDF of the assumed underlying distribution of the latent variable. Currently the logit link, which corresponds to a standard logistic distribution, is implemented.
Depends: R (>= 3.1.1), fastGHQuad, boot, ucminf
License: GPL (>= 2)
LazyData: true
NeedsCompilation: no
Packaged: 2015-05-26 05:46:53 UTC; manuguerra
Repository: CRAN
Date/Publication: 2015-05-26 08:24:22
providing 
.