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miceadds : Some Additional Multiple Imputation Functions, Especially for 'mice'

Package: miceadds
Type: Package
Title: Some Additional Multiple Imputation Functions, Especially for
'mice'
Version: 1.8-0
Date: 2016-05-31
Author:
Alexander Robitzsch [aut, cre], Simon Grund [aut],
Thorsten Henke [aut]
Maintainer:
Alexander Robitzsch <robitzsch@ipn.uni-kiel.de>
Description:
Contains some auxiliary functions for multiple
imputation which complements existing functionality
in R.
In addition to some utility functions, main features
include plausible value imputation, multilevel
imputation functions, imputation using partial least
squares (PLS) for high dimensional predictors, nested
multiple imputation, and two-way imputation.
Depends: R (>= 2.15.0), mice
Imports: car, foreign, graphics, grouped, lme4, MASS, MCMCglmm,
methods, mitools, multiwayvcov, mvtnorm, Rcpp, sirt, sjmisc,
stats, TAM (>= 1.99), utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: Amelia, BIFIEsurvey, haven, Hmisc, inline, jomo, MBESS,
mitml, pan, pls, Zelig
License: GPL (>= 2)
NeedsCompilation: yes
Packaged: 2016-05-31 18:46:25 UTC; sunpn563
Repository: CRAN
Date/Publication: 2016-05-31 23:15:35

● Data Source: CranContrib
● 0 images, 68 functions, 7 datasets
● Reverse Depends: 0

micompr : Multivariate Independent Comparison of Observations

Package: micompr
Title: Multivariate Independent Comparison of Observations
Version: 1.0.0
Date: 2016-05-06
Authors@R: person("Nuno", "Fachada", email = "faken@fakenmc.com",
role = c("aut", "cre"))
Maintainer: Nuno Fachada <faken@fakenmc.com>
Description: A procedure for comparing multivariate samples associated with
different groups. It uses principal component analysis to convert
multivariate observations into a set of linearly uncorrelated statistical
measures, which are then compared using a number of statistical methods. The
procedure is independent of the distributional properties of samples and
automatically selects features that best explain their differences, avoiding
manual selection of specific points or summary statistics. It is appropriate
for comparing samples of time series, images, spectrometric measures or
similar multivariate observations.
Depends: R (>= 3.2.0)
Imports: utils, graphics, methods, stats
Suggests: biotools, MVN, testthat (>= 0.8), knitr, deseasonalize
License: MIT + file LICENSE
URL: http://github.com/fakenmc/micompr
BugReports: https://github.com/fakenmc/micompr/issues
LazyData: true
VignetteBuilder: knitr
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2016-05-08 15:50:00 UTC; nuno
Author: Nuno Fachada [aut, cre]
Repository: CRAN
Date/Publication: 2016-05-08 18:27:56

● Data Source: CranContrib
● 0 images, 40 functions, 3 datasets
● Reverse Depends: 0

microbats : An Implementation of Bat Algorithm in R

Package: microbats
Type: Package
Title: An Implementation of Bat Algorithm in R
Version: 0.1-1
Date: 2016-02-16
Author: Seong Hyun Hwang with contributions from Rachel Myoung Moon
Maintainer: Seong Hyun Hwang <krshh1412@gmail.com>
Description: A nature-inspired metaheuristic algorithm based on the echolocation behavior of microbats that uses frequency tuning to optimize problems in both continuous and discrete dimensions. This R package makes it easy to implement the standard bat algorithm on any user-supplied function. The algorithm was first developed by Xin-She Yang in 2010 (<DOI:10.1007/978-3-642-12538-6_6>, <DOI:10.1109/CINTI.2014.7028669>).
Depends: R (>= 3.2.1)
License: GPL (>= 2)
URL: https://github.com/stathwang/microbats
LazyData: TRUE
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2016-02-18 02:59:44 UTC; Owner
Repository: CRAN
Date/Publication: 2016-02-18 06:51:33

● Data Source: CranContrib
● 0 images, 1 functions, 0 datasets
● Reverse Depends: 0

microbenchmark : Accurate Timing Functions

Package: microbenchmark
Title: Accurate Timing Functions
Description: Provides infrastructure to accurately measure and compare
the execution time of R expressions.
Authors@R: c( person("Olaf", "Mersmann", role=c("aut", "cre"),
email="olafm@p-value.net"), person("Claudia", "Beleites",
role=c("ctb")), person("Rainer", "Hurling", role=c("ctb")),
person("Ari", "Friedman", role=c("ctb")))
License: BSD_2_clause + file LICENSE
Imports: graphics, ggplot2, stats
Suggests: multcomp
ByteCompile: yes
LazyData: yes
Version: 1.4-2.1
SystemRequirements: GNU Make
NeedsCompilation: yes
Packaged: 2015-11-25 10:59:00 UTC; ligges
Author: Olaf Mersmann [aut, cre],
Claudia Beleites [ctb],
Rainer Hurling [ctb],
Ari Friedman [ctb]
Maintainer: Olaf Mersmann <olafm@p-value.net>
Repository: CRAN
Date/Publication: 2015-11-25 12:02:00

● Data Source: CranContrib
● 0 images, 10 functions, 0 datasets
Reverse Depends: 1

micromapST : Linked Micromap Plots for U. S. States

Package: micromapST
Type: Package
Version: 1.0.5
Date: 2015-01-21
Title: Linked Micromap Plots for U. S. States
Author: Dan Carr <dcarr@gmu.edu> and James B Pearson <jpearson@statnetconsulting.com>,
with contributions from Linda Pickle <linda@statnetconsulting.com>.
Maintainer: Jim Pearson <jpearson@statnetconsulting.com>
Depends: R (>= 3.0.0)
Imports: graphics, RColorBrewer, grDevices, stats
Suggests: R.rsp
VignetteBuilder: R.rsp
Description: This package provides the user with the ability to quickly create Linked
Micromap plots of the 50 U.S. states and District of Columbia (51 areas).
Linked Micromap plots are visualizations of geo-referenced data that link statistical
graphics to an organized series of small maps.
The Help description contains examples of how to use the micromapST function.
This package contains the US and State boundary files and U. S. FIPS code to State conversion tables
used by the micromapST function and several data sets used in the micromapST examples.
Copyrighted 2013, 2014 and 2015 by Carr, Pearson and Pickle.
License: GPL (>= 2)
LazyData: yes
LazyLoad: yes
BuildResaveData: yes
ByteCompile: yes
Packaged: 2015-01-21 23:45:16 UTC; jpearson
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-01-22 01:02:51

● Data Source: CranContrib
● 0 images, 5 functions, 12 datasets
● Reverse Depends: 0

micromap : Linked Micromap Plots

Package: micromap
Version: 1.9.2
Date: 2015-02-06
Title: Linked Micromap Plots
Authors@R: c(person("Quinn", "Payton", role="aut",
email = "Payton.Quinn@epa.gov"),
person("Tony", "Olsen", role="aut",
email = "Olsen.Tony@epa.gov"),
person("Marc", "Weber", role="ctb",
email = "Weber.Marc@epa.gov"),
person("Michael", "McManus", role="ctb",
email = "McManus.Michael@epa.gov"),
person("Tom", "Kincaid", role=c("cre", "ctb"),
email = "Kincaid.Tom@epa.gov"))
Depends: R (>= 2.10), maptools, RColorBrewer, rgdal, sp
Imports: ggplot2, grid
Description: This group of functions simplifies the creation of linked micromap
plots.
License: GPL (>= 2)
URL: http://www.jstatsoft.org/v63/i02/
Packaged: 2015-02-06 16:52:58 UTC; tkincaid
Author: Quinn Payton [aut],
Tony Olsen [aut],
Marc Weber [ctb],
Michael McManus [ctb],
Tom Kincaid [cre, ctb]
Maintainer: Tom Kincaid <Kincaid.Tom@epa.gov>
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-02-06 21:22:27

● Data Source: CranContrib
● Cran Task View: Spatial
● 0 images, 11 functions, 7 datasets
● Reverse Depends: 0

micropan : Microbial Pan-genome Analysis

Package: micropan
Type: Package
Title: Microbial Pan-genome Analysis
Version: 1.0
Date: 2014-09-03
Author: Lars Snipen and Kristian Hovde Liland
Maintainer: Lars Snipen <lars.snipen@nmbu.no>
Description: A collection of functions for computations and visualizations of microbial pan-genomes.
Depends: R (>= 2.15.0), igraph
License: GPL-2
LazyData: FALSE
ZipData: TRUE
Packaged: 2014-09-09 09:30:39 UTC; larssn
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2014-09-09 14:18:33

● Data Source: CranContrib
● 0 images, 40 functions, 1 datasets
● Reverse Depends: 0

microplot : Use R Graphics Files as Microplots (Sparklines) in Tables in LaTeX or HTML Files, and Output Data.frames to Org-Mode

Package: microplot
Type: Package
Title: Use R Graphics Files as Microplots (Sparklines) in Tables in
LaTeX or HTML Files, and Output Data.frames to Org-Mode
Version: 1.0-14
Date: 2016-06-21
Author: Richard M. Heiberger, with contributions from Karen Byron and Nooreen Dabbish.
Maintainer: Richard M. Heiberger <rmh@temple.edu>
Description: Prepare lists of R graphics files to be used as
microplots (sparklines) in tables in either LaTeX or HTML files. For LaTeX use
the Hmisc::latex() function or xtable::xtable() with
Sweave, knitr, rmarkdown, or Emacs org-mode to construct
latex tabular environments which include the graphs. For HTML files use
either Emacs org-mode or the htmlTable::htmlTable()
function to construct an HTML file containing tables which
include the graphs. Examples are shown with lattice
graphics, base graphics, and ggplot2 graphics. Examples
for LaTeX include Sweave (both LaTeX-style and
Noweb-style), knitr, emacs org-mode,
and rmarkdown input files and their pdf
output files. Examples for HTML include org-mode
and Rmd input files and their webarchive
HTML output files. In addition, the as.orgtable function
can display a data.frame in an org-mode document.
Imports: Hmisc
Suggests: HH, lattice, ggplot2, reshape2, grid, latticeExtra, xtable,
markdown, rmarkdown, knitr, htmlTable
SystemRequirements: LaTeX
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2016-06-22 02:47:19 UTC; rmh
Repository: CRAN
Date/Publication: 2016-06-22 06:04:27

● Data Source: CranContrib
● 0 images, 8 functions, 0 datasets
● Reverse Depends: 0

midasr : Mixed Data Sampling Regression

Package: midasr
Title: Mixed Data Sampling Regression
Description: Methods and tools for mixed frequency time series data analysis. Allows estimation, model selection and forecasting for MIDAS regressions.
URL: http://mpiktas.github.io/midasr/
Version: 0.5
Maintainer: Vaidotas Zemlys <zemlys@gmail.com>
Author: Virmantas Kvedaras <virmantas.kvedaras@mif.vu.lt>, Vaidotas Zemlys
<vaidotas.zemlys@mif.vu.lt>
Depends: R (>= 2.11.0), sandwich, optimx
Imports: MASS, numDeriv, Matrix, forecast, stats, graphics, utils
License: GPL-2 | MIT + file LICENCE
BugReports: https://github.com/mpiktas/midasr/issues
Suggests: testthat
NeedsCompilation: no
Packaged: 2015-07-16 06:37:45 UTC; mpiktas
Repository: CRAN
Date/Publication: 2015-07-16 14:24:34

● Data Source: CranContrib
● Cran Task View: Econometrics, TimeSeries
● 0 images, 59 functions, 6 datasets
● Reverse Depends: 0

midastouch : Multiple Imputation by Distance Aided Donor Selection

Package: midastouch
Type: Package
Version: 1.3
Title: Multiple Imputation by Distance Aided Donor Selection
Date: 2016-02-06
Authors@R: c(person("Philipp", "Gaffert", role = c("aut","cre"),
email = "philipp.gaffert@web.de"),
person("Florian", "Meinfelder", role = "aut",
email = "florian.meinfelder@uni-bamberg.de"),
person("Volker", "Bosch", role = "aut",
email = "volker.bosch@gfk.com"))
Maintainer: Philipp Gaffert <philipp.gaffert@web.de>
Depends: R (>= 3.2.0)
Imports: utils
Suggests: mice
Description: Contains the function mice.impute.midastouch(). Technically this function is to be run from within the 'mice' package (van Buuren et al. 2011), type ??mice. It substitutes the method 'pmm' within mice by 'midastouch'. The authors have shown that 'midastouch' is superior to default 'pmm'. Many ideas are based on Siddique / Belin 2008's MIDAS.
License: GPL-2 | GPL-3
LazyLoad: yes
LazyData: yes
URL:
https://www.uni-bamberg.de/fileadmin/uni/fakultaeten/sowi_lehrstuehle/statistik/Personen/Dateien_Florian/properPMM.pdf
NeedsCompilation: no
Packaged: 2016-02-06 17:33:43 UTC; Phil
Author: Philipp Gaffert [aut, cre],
Florian Meinfelder [aut],
Volker Bosch [aut]
Repository: CRAN
Date/Publication: 2016-02-07 09:35:46

● Data Source: CranContrib
● 0 images, 1 functions, 0 datasets
● Reverse Depends: 0