Mark van der Loo <mark.vanderloo@gmail.com>
Modify Data Using Externally Defined Modification Rules
c(person("Mark", "van der Loo",role=c("cre","aut"),email="mark.vanderloo@gmail.com")
, person("Edwin", "de Jonge", role="aut"))
Data cleaning scripts typically contain a lot of 'if this change that'
type of statements. Such statements are typically condensed expert knowledge.
With this package, such 'data modifying rules' are taken out of the code and
become in stead parameters to the work flow. This allows one to maintain, document,
and reason about data modification rules separately from the workflow.
yaml, validate (>= 0.1.5), settings, utils
Mark van der Loo [cre, aut],
Edwin de Jonge [aut]

Data cleaning scripts typically contain a lot of 'if this change that'
type of statements. Such statements are typically condensed expert knowledge.
With this package, such 'data modifying rules' are taken out of the code and
become in stead parameters to the work flow. This allows one to maintain, document,
and reason about data modification rules as separate entities.

'dplyr_verbs.R' 'guard.R' 'modifier.R' 'modify.R' 'validate.R'

person(given = "Edwin",
family = "de Jonge",
role = c("aut", "cre"),
email = "edwindjonge@gmail.com",
comment = c(ORCID = "0000-0002-6580-4718"))
Apply modification rules from R package 'dcmodify' to the database,
prescribing and documenting deterministic data cleaning steps on records in a database.
The rules are translated into SQL statements using R package 'dbplyr'.
dplyr, dbplyr, DBI, dcmodify (>= 0.1.9), methods, validate

dplyr, dbplyr, DBI, dcmodify (>= 0.1.9), validate, methods

c(
person(given = "Edwin",
family = "de Jonge",
role = c("aut", "cre"),
email = "edwindjonge@gmail.com",
comment = c(ORCID = "0000-0002-6580-4718")),
person(given="Wytze", family = "Gelderloos", role=c("ctb"), email="")
)
testthat (>= 3.0.0), RSQLite, covr, knitr, rmarkdown
Edwin de Jonge [aut, cre] (<https://orcid.org/0000-0002-6580-4718>),
Wytze Gelderloos [ctb]

Attribute profile estimation using Diagnostic Classification
Models and MCMC
c( person("Diane", family="Losardo", email='dlosardo@amplify.com",
role = c("aut", "cre", "cph")), person("Margi", family="Dubal",
email='margidubal@gmail.com", role = c("aut", "cre", "cph")))
Analysis of dichotomous response data to obtain attribute profile
estimates for respondents using Diagnostic Classification Model (DCM) and
Markov Chain Monte Carlo (MCMC) method. The estimation procedure uses a
loglinear cognitive diagnostic modeling (LDCM) framework that allows for
the estimation of a host of DCMs such as NIDO, NIDA, NC-RUM etc.
ggplot2, KFAS, plyr, reshape2, tableplot, methods