DBI, RSQLite, rqdatatable, igraph, DiagrammeR, knitr,
rmarkdown, testthat

A piped query generator based on Edgar F. Codd's relational
algebra, and on production experience using 'SQL' and 'dplyr' at big data
scale.  The design represents an attempt to make 'SQL' more teachable by
denoting composition by a sequential pipeline notation instead of nested
queries or functions.   The implementation delivers reliable high
performance data processing on large data systems such as 'Spark',
databases, and 'data.table'. Package features include: data processing trees
or pipelines as observable objects (able to report both columns
produced and columns used), optimized 'SQL' generation as an explicit
user visible table modeling step, plus explicit query reasoning and checking.
wrapr (>= 1.8.2), utils, stats, methods,

wrapr (>= 1.8.3), utils, stats, methods,
DBI, RSQLite, rqdatatable, igraph, DiagrammeR, knitr,
rmarkdown, RUnit

DBI, RSQLite, rqdatatable, igraph, DiagrammeR, knitr,
rmarkdown, RUnit, yaml

DBI, RSQLite, rqdatatable (>= 1.2.5), igraph, DiagrammeR,
knitr, rmarkdown, RUnit, yaml

DBI, RSQLite, rqdatatable (>= 1.2.6), igraph, DiagrammeR,
knitr, rmarkdown, RUnit, yaml

DBI, RSQLite, rqdatatable (>= 1.2.7), igraph, DiagrammeR,
knitr, rmarkdown, RUnit, yaml

DBI, RSQLite, rqdatatable (>= 1.2.8), igraph, DiagrammeR,
knitr, rmarkdown, yaml, tinytest

DBI, RSQLite, rqdatatable (>= 1.2.9), igraph, DiagrammeR,
knitr, rmarkdown, yaml, tinytest

DBI, RSQLite, rqdatatable (>= 1.3.0), igraph, DiagrammeR,
knitr, rmarkdown, yaml, tinytest

DBI, RSQLite, rqdatatable (>= 1.3.0), igraph, knitr,
rmarkdown, yaml, tinytest

Statistical Methods for the Randomized Response Technique
Graeme Blair <graeme.blair@columbia.edu>, Yang-Yang Zhou <yz3@princeton.edu>, Kosuke Imai <kimai@princeton.edu>
Enables researchers to conduct multivariate statistical analyses of survey data with randomized response technique items from several designs, including mirrored question, forced question, and unrelated question. This includes regression with the randomized response as the outcome and logistic regression with the randomized response item as a predictor. In addition, tools for conducting power analysis for designing randomized response items are included. The package implements methods described in Blair, Imai, and Zhou (2015) ''Design and Analysis of the Randomized Response Technique,'' Working paper available at <http://graemeblair.com/papers/randresp.pdf>.