Package: DMAR 1.0.1

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.

Authors:Ken Kelley [aut, cre]