Package: mcIRT
Type: Package
Title: IRT models for multiple choice items (mcIRT)
Version: 0.41
Date: 2014-08-30
Author: Manuel Reif
Maintainer: Manuel Reif <manuel.reif@univie.ac.at>
Description: This package provides functions to estimate two popular IRT-models: The Nominal Response Model (Bock 1972) and the quite recently developed Nested Logit Model (Suh & Bolt 2010). These are two models to examine multiple-choice items and other multicategorial response formats.
Depends: R (>= 2.14)
LinkingTo: Rcpp, RcppArmadillo
Imports: Rcpp (>= 0.8.0)
License: GPL-3
URL: https://github.com/manuelreif/mcIRT
Suggests: testthat
Packaged: 2014-08-30 16:12:50 UTC; manuel
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2014-08-30 19:17:27
Package: mcPAFit
Type: Package
Title: Estimating Preferential Attachment from a Single Network
Snapshot by Markov Chain Monte Carlo
Version: 0.1.3
Date: 2016-05-25
Author: Thong Pham, Paul Sheridan, Hidetoshi Shimodaira
Maintainer: Thong Pham <thongpham@thongpham.net>
Description: A Markov chain Monte Carlo method is provided to estimate the preferential attachment function from a single network snapshot. Conventional methods require the complete information about the appearance order of all nodes and edges in the network. This package incorporates the appearance order into the state space and estimates it together with the preferential attachment function. Auxiliary variables are introduced to facilitate fast Gibbs sampling.
License: GPL-3
Depends: R (>= 2.10.0)
Imports: Rcpp (>= 0.11.3), grDevices, graphics, stats, RColorBrewer, PAFit
LinkingTo: Rcpp
LazyData: True
NeedsCompilation: yes
Packaged: 2016-05-25 13:06:39 UTC; pham
Repository: CRAN
Date/Publication: 2016-05-25 15:52:19
Package: mcbiopi
Title: Matrix Computation Based Identification Of Prime Implicants
Version: 1.1.2
Date: 2012-01-03
Author: Holger Schwender
Maintainer: Holger Schwender <holger.schw@gmx.de>
Description: Computes the prime implicants or a minimal disjunctive normal form for a
logic expression presented by a truth table or a logic tree. Has been particularly
developed for logic expressions resulting from a logic regression analysis, i.e.
logic expressions typically consisting of up to 16 literals, where the prime implicants
are typically composed of a maximum of 4 or 5 literals.
License: LGPL (>= 2)
Packaged: 2012-01-03 20:41:29 UTC; schwender
Repository: CRAN
Date/Publication: 2012-01-04 06:30:46
Package: mcc
Type: Package
Title: Moment Corrected Correlation
Version: 1.0
Date: 2014-07-03
Author: Yi-Hui Zhou
Description: A number of biomedical problems involve performing many hypothesis tests, with an attendant need to apply stringent thresholds. Often the data take the form of a series of predictor vectors, each of which must be compared with a single response vector, perhaps with nuisance covariates. Parametric tests of association are often used, but can result in inaccurate type I error at the extreme thresholds, even for large sample sizes. Furthermore, standard two-sided testing can reduce power compared to the doubled p-value, due to asymmetry in the null distribution. Exact (permutation) testing approaches are attractive, but can be computationally intensive and cumbersome. MCC is an approximation to exact association testing of two vectors that is accurate and fast enough for standard use in high-throughput settings, and can easily provide standard two-sided or doubled p-values.
Maintainer: Yi-Hui Zhou<yihui_zhou@ncsu.edu>
License: GPL (>= 2)
LazyLoad: yes
Packaged: 2014-07-03 15:54:51 UTC; yzhou19
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-07-03 20:59:30
Package: mcclust
Type: Package
Title: Process an MCMC Sample of Clusterings
Version: 1.0
Date: 2009-05-22
Author: Arno Fritsch
Depends: R (>= 2.10), lpSolve
Maintainer: Arno Fritsch <arno.fritsch@tu-dortmund.de>
Description: Implements methods for processing a sample of (hard)
clusterings, e.g. the MCMC output of a Bayesian clustering
model. Among them are methods that find a single best
clustering to represent the sample, which are based on the
posterior similarity matrix or a relabelling algorithm.
License: GPL (>= 2)
LazyLoad: yes
Packaged: 2012-07-23 10:10:08 UTC; ripley
Repository: CRAN
Date/Publication: 2012-07-23 10:35:32
Package: mcga
Type: Package
Title: Machine Coded Genetic Algorithms for Real-Valued Optimization
Problems
Version: 3.0.1
Date: 2016-05-12
Author: Mehmet Hakan Satman
Maintainer: Mehmet Hakan Satman <mhsatman@istanbul.edu.tr>
Description: Machine coded genetic algorithm (MCGA) is a fast tool for
real-valued optimization problems. It uses the byte
representation of variables rather than real-values. It
performs the classical crossover operations (uniform) on these
byte representations. Mutation operator is also similar to
classical mutation operator, which is to say, it changes a
randomly selected byte value of a chromosome by +1 or -1 with
probability 1/2. In MCGAs there is no need for
encoding-decoding process and the classical operators are
directly applicable on real-values. It is fast and can handle a
wide range of a search space with high precision. Using a
256-unary alphabet is the main disadvantage of this algorithm
but a moderate size population is convenient for many problems.
Package also includes multi_mcga function for multi objective
optimization problems. This function sorts the chromosomes
using their ranks calculated from the non-dominated sorting
algorithm.
License: GPL (>= 2)
Depends: GA
Imports: Rcpp (>= 0.11.4)
LinkingTo: Rcpp
NeedsCompilation: yes
LazyLoad: yes
Repository: CRAN
Date/Publication: 2016-05-12 16:24:50
Packaged: 2016-05-12 13:28:39 UTC; hako
RoxygenNote: 5.0.1
Package: mcgibbsit
Title: Warnes and Raftery's MCGibbsit MCMC diagnostic
Version: 1.1.0
Date: 2012-10-23
Author: Gregory R. Warnes <greg@warnes.net>, Robert Burrows
Depends: coda
Description:
'mcgibbsit' provides an implementation of Warnes & Raftery's
MCGibbsit run-length diagnostic for a set of (not-necessarily
independent) MCMC samplers. It combines the estimate error-bounding
approach of the Raftery and Lewis MCMC run length diagnostic with
the between verses within chain approach of the Gelman and
Rubin MCMC convergence diagnostic.
Maintainer: Gregory R. Warnes <greg@warnes.net>
License: GPL
Packaged: 2013-10-23 23:56:39 UTC; warnes
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-10-24 08:40:58
Package: mcglm
Type: Package
Title: Multivariate Covariance Generalized Linear Models
Version: 0.3.0
Date: 2016-06-06
Author: Wagner Hugo Bonat [aut, cre],
Walmes Marques Zeviani [ctb],
Fernando de Pol Mayer [ctb]
Maintainer: Wagner Hugo Bonat <wbonat@ufpr.br>
Authors@R: c(person(c("Wagner","Hugo"), "Bonat", role = c("aut", "cre"),
email = "wbonat@ufpr.br"),
person(c("Walmes","Marques"), "Zeviani", role = "ctb"),
person(c("Fernando", "de Pol"), "Mayer", role = "ctb"))
Description: Fitting multivariate covariance generalized linear
models (McGLMs) to data. McGLMs is a general framework for non-normal
multivariate data analysis, designed to handle multivariate response
variables, along with a wide range of temporal and spatial correlation
structures defined in terms of a covariance link function combined
with a matrix linear predictor involving known matrices.
The models take non-normality into account in the conventional way
by means of a variance function, and the mean structure is modelled
by means of a link function and a linear predictor.
The models are fitted using an efficient Newton scoring algorithm
based on quasi-likelihood and Pearson estimating functions, using
only second-moment assumptions. This provides a unified approach to
a wide variety of different types of response variables and covariance
structures, including multivariate extensions of repeated measures,
time series, longitudinal, spatial and spatio-temporal structures.
The package offers a user-friendly interface for fitting McGLMs
similar to the glm() R function.
Depends: R (>= 3.2.1)
Suggests: testthat, plyr, lattice, latticeExtra, knitr, rmarkdown, MASS, mvtnorm, tweedie, devtools
Imports: stats, Matrix, assertthat, graphics
License: GPL-3 | file LICENSE
LazyData: TRUE
URL: https://github.com/wbonat/mcglm
BugReports: https://github.com/wbonat/mcglm/issues
Encoding: UTF-8
VignetteBuilder: knitr
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2016-06-09 14:10:09 UTC; fernando
Repository: CRAN
Date/Publication: 2016-06-09 20:23:56
providing 
.