list

Package: list
Version: 8.0
Date: 2015-5-19
Title: Statistical Methods for the Item Count Technique and List
Experiment
Author: Graeme Blair [aut, cre], Kosuke Imai [aut, cre], Bethany Park [ctb],
Alexander Coppock [ctb]
Maintainer: Graeme Blair <graeme.blair@columbia.edu>
Depends: R (>= 3.2.0), utils, sandwich (>= 2.3-3)
Imports: VGAM (>= 0.9-8), magic (>= 1.5-6), gamlss.dist (>= 4.3-4),
MASS (>= 7.3-40), quadprog (>= 1.5-5), corpcor (>= 1.6.7),
mvtnorm (>= 1.0-2), coda (>= 0.17-1)
Suggests: testthat (>= 0.9.1), knitr (>= 1.10.5)
VignetteBuilder: knitr
Description: Allows researchers to conduct multivariate statistical analyses of survey data with list experiments. This survey methodology is also known as the item count technique or the unmatched count technique and is an alternative to the commonly used randomized response method. The package implements the methods developed by Imai (2011), Blair and Imai (2012), Blair, Imai, and Lyall (2013), Imai, Park, and Greene (2014), and Aronow, Coppock, Crawford, and Green (2015). This includes a Bayesian MCMC implementation of regression for the standard and multiple sensitive item list experiment designs and a random effects setup, a Bayesian MCMC hierarchical regression model with up to three hierarchical groups, the combined list experiment and endorsement experiment regression model, a joint model of the list experiment that enables the analysis of the list experiment as a predictor in outcome regression models, and a method for combining list experiments with direct questions. In addition, the package implements the statistical test that is designed to detect certain failures of list experiments, and a placebo test for the list experiment using data from direct questions.
LazyLoad: yes
LazyData: yes
License: GPL (>= 2)
NeedsCompilation: yes
Packaged: 2015-05-19 15:57:24 UTC; gblair
Repository: CRAN
Date/Publication: 2015-05-20 00:05:16

Install log

* installing to library '/home/ddbj/local/lib64/R/library'
* installing *source* package 'list' ...
** package 'list' successfully unpacked and MD5 sums checked
** libs
gcc -I/home/ddbj/local/lib64/R/include -DNDEBUG  -I/usr/local/include    -fpic  -g -O2  -c combine.c -o combine.o
gcc -I/home/ddbj/local/lib64/R/include -DNDEBUG  -I/usr/local/include    -fpic  -g -O2  -c models.c -o models.o
gcc -I/home/ddbj/local/lib64/R/include -DNDEBUG  -I/usr/local/include    -fpic  -g -O2  -c poisbinom.c -o poisbinom.o
gcc -I/home/ddbj/local/lib64/R/include -DNDEBUG  -I/usr/local/include    -fpic  -g -O2  -c rand.c -o rand.o
gcc -I/home/ddbj/local/lib64/R/include -DNDEBUG  -I/usr/local/include    -fpic  -g -O2  -c subroutines.c -o subroutines.o
gcc -I/home/ddbj/local/lib64/R/include -DNDEBUG  -I/usr/local/include    -fpic  -g -O2  -c vector.c -o vector.o
gcc -shared -L/home/ddbj/local/lib64/R/lib -L/usr/local/lib64 -o list.so combine.o models.o poisbinom.o rand.o subroutines.o vector.o -L/home/ddbj/local/lib64/R/lib -lRlapack -L/home/ddbj/local/lib64/R/lib -lRblas -lgfortran -lm -lquadmath -L/home/ddbj/local/lib64/R/lib -lR
installing to /home/ddbj/local/lib64/R/library/list/libs
** R
** data
*** moving datasets to lazyload DB
** inst
** preparing package for lazy loading
** help
*** installing help indices
  converting help for package 'list'
    finding HTML links ... done
    affirm                                  html  
    combinedListDirect                      html  
    combinedListExps                        html  
    comp.listEndorse                        html  
    ict.test                                html  
    ictreg                                  html  
    ictreg.joint                            html  
    ictregBayes                             html  
    ictregBayesHier                         html  
    mexico                                  html  
    mis                                     html  
    multi                                   html  
    plot.predict.ictreg                     html  
    predict.ictreg                          html  
    predict.ictreg.joint                    html  
    predict.ictregBayes                     html  
    predict.ictregBayesHier                 html  
    race                                    html  
    summary.ictreg                          html  
** building package indices
** installing vignettes
** testing if installed package can be loaded
* DONE (list)
Making 'packages.html' ... done