Maintainer: Mark van der Loo <mark.vanderloo@gmail.com>
Title: Modify Data Using Externally Defined Modification Rules
Authors@R: c(person("Mark", "van der Loo",role=c("cre","aut"),email="mark.vanderloo@gmail.com")
, person("Edwin", "de Jonge", role="aut"))
Description: 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.
Imports: yaml, validate (>= 0.1.5), settings, utils

and reason about data modification rules as separate entities.

Suggests: tinytest, knitr, lumberjack, markdown

Suggests: tinytest, knitr, lumberjack, rmarkdown
Collate: 'dplyr_verbs.R' 'guard.R' 'modifier.R' 'modify.R' 'validate.R'

Description: 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'.
Suggests: testthat (>= 3.0.0), RSQLite, covr
Imports: dplyr, dbplyr, DBI, dcmodify (>= 0.1.9), methods, validate

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

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

Title: Attribute profile estimation using Diagnostic Classification
Author: 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")))
Description: 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.
Imports: ggplot2, KFAS, plyr, reshape2, tableplot, methods