A modern R package for design, measurement, and analysis, with special strength in effect sizes, confidence intervals, size planning, reliability and agreement, mediation analysis, equivalence testing, meta-analysis, experimental and quasi-experimental designs, repeated measures, and model comparison-based inference. DMAR (pronounced “Dee-Mar”) is heavily methodological in nature, drawing on the psychometric and statistical traditions, and is aligned with the methodological and applied research program and interests of the author. Many aspects of the package traces to the author's methodological work and collaborations, including sample size planning via accuracy in parameter estimation (AIPE; Kelley & Maxwell, 2003; Kelley & Rausch, 2006; Maxwell, Kelley, & Rausch, 2008), the definition and communication of effect sizes (Kelley & Preacher, 2012; Preacher & Kelley, 2011), and the model comparison perspective of Maxwell, Delaney, and Kelley (2027). It aims to be methodologically sound and particularly well-suited to research in which the independent or dependent variables involve the person, across psychology, sociology, education, behavioral economics, management, marketing, and information systems.
Ken Kelley [email protected]
The package makes accessible to researchers a variety of methods
that are easy to use, including from sample estimates or results
reported in published articles: effect size estimation, confidence
intervals for effect sizes, sample size planning, multivariate
methods, factor analysis, and certain latent variable models.
Particular strengths include sample size planning under several
complementary frameworks: accuracy in parameter estimation (AIPE),
power analysis, minimum risk, and equivalence. Most exported
functions return a tidy data.frame with a term
column and a numeric value column (some carry additional
typed columns per term), and ggplot2 is used for the plotting
functions. A few functions return the shape their task calls for
instead: model fits such as mlmr return a richer list-like
object with coef / vcov / confint methods,
descriptives returns a list of summary tables, and a small
number of scalar utilities such as skewness and
kurtosis return a bare numeric. The interface is consistent,
modern, and opinionated, and is designed for clarity and
reproducibility.
DMAR builds heavily on the MBESS package (Kelley, 2007a, 2007b), which has been on CRAN for more than two decades. MBESS was originally framed for the behavioral, educational, and social sciences, but its use has grown well beyond that scope; DMAR is quite general, though especially well-aligned with human-centered research.
Function families. A user-facing tour:
smd, smd_c, smd_trimmed,
eta_squared, eta_squared_partial,
eta_squared_generalized, omega_squared,
omega_squared_partial, cohen_f,
cles, cliff_delta, vargha_delaney_A,
proportion_of_superiority,
probability_of_superiority_paired, lin_ccc.
ci_smd,
ci_smd_c, ci_R2, ci_R, ci_rc,
ci_src, ci_eta_squared (and partial /
generalized variants), ci_omega_squared,
ci_pvaf, ci_snr, ci_srsnr,
ci_mahalanobis, ci_eigenvalue, ci_cv,
ci_sm, ci_reg_coef, ci_r,
ci_rmsea.
mlmr (univariate
full information maximum likelihood (FIML), lm-like), mlmr_mv (multivariate FIML).
ancova, anova_within_two_way,
mixed_anova, manova_split_plot,
simple_effects_AB, contrast_test,
pairwise_within, mauchly_test,
obrien_test.
reliability,
reliability_alpha, reliability_omega (with a
model implied or observed total-variance denominator, and a
reliability_omega_categorical for ordered items),
reliability_kr20, reliability_H,
cohen_kappa, fleiss_kappa,
krippendorff_alpha, gwet_ac,
limits_of_agreement.
mediate (the simple mediation model with
bootstrap, Monte Carlo, and Sobel intervals),
mediation_mbco (likelihood ratio tests of arbitrary
mediation effects by model-based constrained optimization, with
multiple groups and moderated mediation probing), and
plot_mediation_mbco (conditional effect curves with
confidence bands).
cfa_1,
cov_sem, covmat_from_cfa,
compare_cov_structures.
ss_aipe_smd,
ss_aipe_R2, ss_aipe_reg_coef,
ss_aipe_partial_r, ss_aipe_omega_squared,
ss_aipe_icc, ss_aipe_cv, ss_aipe_pcm,
ss_aipe_rmsea, the cluster-randomized planners
ss_aipe_crd_*, plus their Monte Carlo sensitivity
companions ss_aipe_*_sensitivity.
ss_power_smd,
ss_power_R2, ss_power_r,
ss_power_reg_coef, ss_power_sem,
ss_power_c, ss_power_c_ancova,
ss_power_contrast, ss_power_factorial_anova,
ss_power_split_plot_anova, ss_power_mixed_effects,
ss_power_one_way_anova, ss_power_pcm,
ss_power_rm_anova, ss_power_sc.
cv_t, cv_z,
cv_smm, cv_scheffe, cv_tukey_hsd,
cv_dunnett, ci_dunnett, ci_tukey_kramer,
ci_scheffe, welch_t, summary_t_test,
correlations_test, power_fisher_exact,
randomization_test_paired,
equivalence_smd, equivalence_r, power_equivalence_md.
convert_R2_f /
convert_f_R2, convert_R2_lambda /
convert_lambda_R2, convert_delta_lambda /
convert_lambda_delta, convert_r_Z /
convert_Z_r, convert_cor_cov.
plot_smd, plot_ci,
plot_R2, plot_trajectories,
plot_trajectories_fitted.
icc, icc_lmer,
variance_components_mls, design_effect (Kish design
effect), ss_aipe_crd_*.
bessel_errors (Bessel's 1818 grouped
distribution of Bradley's astronomical observation errors),
diagnosis_agreement (Cohen's 1968 weighted kappa
illustration), drinks_trial (Smith, Meyers, and Delaney's 1998
Community Reinforcement Approach drinking trial),
holzinger_swineford (the 1939 factor analysis study),
prime_time_achievement
(the Indiana Prime Time third grade achievement evaluation),
pygmalion
(Rosenthal and Jacobson's 1968 teacher-expectancy data),
teacher_expectancy (Raudenbush's 1984 meta-analysis of
18 teacher-expectancy experiments), and test_market
(Bryant and Bruvold's 1980 controlled test-market experiment
for ANCOVA with a random covariate).
Feedback. Bug reports, feature requests, and suggestions for new methods are welcomed by email to Ken Kelley [email protected] (please put “DMAR” in the subject line). See https://kenkelley.org for Ken Kelley's web site, https://kenkelley.org/publications/ for related publications, and https://github.com/yelleKneK/DMAR for the project's GitHub page.
Kelley, K. (2007a). Confidence intervals for standardized effect sizes: Theory, application, and implementation. Journal of Statistical Software, 20(8), 1--24. tools:::Rd_expr_doi("10.18637/jss.v020.i08")
Kelley, K. (2007b). Methods for the behavioral, educational, and social sciences: An R package. Behavior Research Methods, 39(4), 979--984. tools:::Rd_expr_doi("10.3758/BF03192993")
Kelley, K., & Maxwell, S. E. (2003). Sample size for multiple regression: Obtaining regression coefficients that are accurate, not simply significant. Psychological Methods, 8(3), 305--321. tools:::Rd_expr_doi("10.1037/1082-989X.8.3.305")
Kelley, K., & Preacher, K. J. (2012). On effect size. Psychological Methods, 17(2), 137--152. tools:::Rd_expr_doi("10.1037/a0028086")
Kelley, K., & Rausch, J. R. (2006). Sample size planning for the standardized mean difference: Accuracy in parameter estimation via narrow confidence intervals. Psychological Methods, 11(4), 363--385. tools:::Rd_expr_doi("10.1037/1082-989X.11.4.363")
Maxwell, S. E., Delaney, H. D., & Kelley, K. (2027). Designing experiments and analyzing data: A model comparison perspective (4th ed.). Routledge.
Maxwell, S. E., Kelley, K., & Rausch, J. R. (2008). Sample size planning for statistical power and accuracy in parameter estimation. Annual Review of Psychology, 59, 537--563. tools:::Rd_expr_doi("10.1146/annurev.psych.59.103006.093735")
Preacher, K. J., & Kelley, K. (2011). Effect size measures for mediation models: Quantitative strategies for communicating indirect effects. Psychological Methods, 16(2), 93--115. tools:::Rd_expr_doi("10.1037/a0022658")
Useful links: