DMAR - Design, Measurement, and Analysis in R (DMAR)
Methods for design, measurement, and analysis, with the
aim of being user friendly yet methodologically sound. 'DMAR'
(pronounced "Dee-Mar") implements many advanced and nonstandard
methods and makes them available for straightforward use, with
interfaces, defaults, and documentation that are consistent
across the package and grounded in the methodological
literature, in support of sound and reproducible results. The
package emphasizes effect size estimation with confidence
intervals; sample size planning through accuracy in parameter
estimation (AIPE) and power analysis (including composite power
for designs whose conclusions require several results to hold
at once), with minimum risk, sequential, and equivalence
frameworks; reliability, agreement, and measurement more
broadly, from coefficient omega with confidence intervals to
measurement invariance; factor analysis and structural equation
modeling, in which constructs, latent variables measured by
multiple indicators, are modeled directly, with confirmatory
factor analysis, convergent and discriminant validity, and
sample size planning for structural equation models; mediation
analysis, from the simple mediation model with bootstrap
intervals to likelihood ratio tests of arbitrary indirect
effects by model-based constrained optimization (MBCO), with
multiple groups and the probing of moderated mediation;
equivalence and noninferiority testing; meta-analysis; repeated
measures, multivariate, ANOVA, and ANCOVA designs; and
inference grounded in model comparison throughout. Measurement
is approached from a psychometric perspective, and although
many of the methods grew up in human-centered research, they
apply broadly across the empirical sciences. Much of what is
implemented traces to the author's methodological work,
interests, and collaborations. 'DMAR' is a more modern, more
general, and greatly expanded reimagining of the 'MBESS'
package (Kelley, 2007a, <doi:10.18637/jss.v020.i08>; 2007b,
<doi:10.3758/BF03192993>), which has been on CRAN for more than
two decades and remains available there in stable form. Most
functions accept either raw data or the summary statistics
typically reported in published articles, so an analysis can be
reproduced from a paper without the original data, which is
useful both for extending a published analysis and for
meta-analytic work. The estimation, inference, and planning
functions return one consistently formatted data frame per
function that composes with the broader R ecosystem, and
confidence intervals are reported alongside effect sizes
throughout, as best practice recommends. Researchers who have
data and a question but who are not R experts will find the
package approachable, while methodologists gain access to
advanced and nonstandard methods, including tables of critical
values not available elsewhere.
