DMAR

DMAR (Design, Measurement, and Analysis in R, pronounced “Dee-Mar”) is a package for human-centered research: effect sizes, confidence intervals for effect sizes, sample size planning, reliability and agreement, mediation analysis (from the simple mediation model to likelihood ratio tests of arbitrary indirect effects, with moderated mediation probing), equivalence testing, meta-analysis, experimental and quasi-experimental designs, repeated measures, and model comparison-based inference. It draws on the psychometric and statistical traditions and reflects the methodological and applied research program of its author, Ken Kelley (University of Notre Dame). It aims to be methodologically sound and useful across psychology, sociology, education, management, marketing, and information systems, especially where the independent or dependent variables involve the person.

Installation

# Once on CRAN:
install.packages("DMAR")

# Development version from GitHub:
# install.packages("remotes")
remotes::install_github("yelleKneK/DMAR")

R >= 4.0.0 is required. Most estimation, inference, and planning functions return a tidy data.frame(term, value) that composes naturally with dplyr / ggplot2 pipelines and with broom::tidy() / broom::glance(). Heavier dependencies (e.g., lavaan, lme4, OpenMx) are in Suggests and gated by requireNamespace(); install them only when you need the functions that use them.

Function families

DMAR is organized into a small number of stable function families:

• Effect size estimates. smd(), cles(), proportion_of_superiority() (sometimes called Cohen’s U3), cliff_delta(), vargha_delaney_A(), cohen_f(), eta_squared(), omega_squared(), nnt_from_smd().
• Confidence intervals for effect sizes. ci_smd(), ci_R2(), ci_eta_squared(), ci_omega_squared(), ci_rmsea(), ci_cv(), ci_c(), ci_sc(), ci_c_ancova(), ci_sc_ancova(), ci_reg_coef(), ci_eigenvalue(), ci_mahalanobis().
• Sample size planning under AIPE (accuracy in parameter estimation). The ss_aipe_*() family covers SMD, R², ω², CV, partial / semipartial r, regression coefficients (standardized and unstandardized), ANCOVA contrasts, RMSEA, SEM paths, polynomial change, mixed-effects fixed effects, ICC, reliability, and Cliff’s delta. Every planner has a Monte Carlo *_sensitivity() sibling that quantifies the impact of misspecified planning values.
• Sample size planning under power. ss_power_R2(), ss_power_smd(), ss_power_r(), ss_power_one_way_anova(), ss_power_factorial_anova(), ss_power_rm_anova(), ss_power_split_plot_anova(), ss_power_mixed_effects(), ss_power_pcm(), ss_power_sem(), ss_power_contrast(), power_fisher_exact().
• Minimum risk / sequential estimation. mr_smd(), mr_cv().
• Equivalence testing. equivalence_smd(), equivalence_r(), ss_aipe_equivalence_smd().
• Reliability. reliability_alpha(), reliability_omega(), reliability_omega_categorical(), reliability_omega_h(), reliability_H(), reliability_kr20(), var_alpha(), the umbrella reliability(), and ss_aipe_reliability().
• ANOVA / ANCOVA / contrasts. welch_t(), contrast_test(), summary_t_test(), ancova(), mixed_anova(), manova_split_plot(), anova_within(), anova_within_two_way(), pairwise_within(), simple_effects_AB(), obrien_test(), mauchly_test(), epsilon_corrections(), contrast_adjusted().
• Multiple-comparison procedures. ci_dunnett(), ci_tukey_kramer(), ci_scheffe(), and their critical-value helpers cv_dunnett(), cv_smm(), cv_scheffe(), cv_tukey_hsd(), cv_t(), cv_z().
• Agreement and reliability of raters. icc(), icc_lmer(), cohen_kappa(), fleiss_kappa(), gwet_ac(), krippendorff_alpha(), lin_ccc(), limits_of_agreement().
• Multilevel and multivariate / latent variable methods. cfa_1(), mlmr(), mlmr_mv(), R2_mixed_effects(), R2_mixed_effects_decomposition(), compare_cov_structures(), covmat_from_cfa(), cov_sem(), procrustes_phi().
• Longitudinal designs. ss_aipe_pcm(), ss_power_pcm(), plot_trajectories(), plot_trajectories_fitted(), variance_components_mls().
• Design utilities. design_effect() (Kish’s design effect and DEFT), effects_coding(), helmert_coding(), is_orthogonal_set(), simulate_anova_data(), simulate_ancova_data(), simulate_regression_data().
• Parameterization conversions (convert_*). Invertible maps between equivalent metrics (convert_r_Z / convert_Z_r, convert_R2_f / convert_f_R2, convert_lambda_R2 / convert_R2_lambda, convert_delta_lambda / convert_lambda_delta, convert_cor_cov, convert_t_smd).
• Plots. plot_smd(), plot_ci(), plot_R2(), plot_trajectories(), plot_trajectories_fitted(). All default to showing the CI and the sample size.
• Variance utilities. var_smd(), var_R2(), var_r(), var_partial_r(), var_semipartial_r(), var_cv(), var_omega_squared(), var_alpha(), var_icc(), var_indirect_effect().

Canonical examples

1. Standardized mean difference with a noncentral-t confidence interval

library(DMAR)

# From summary statistics:
smd(mean_1 = 60, mean_2 = 55, s_1 = 10, s_2 = 10, n_1 = 30, n_2 = 30)

# CI on the standardized mean difference (Cohen's d) via the noncentral t:
ci_smd(smd = 0.5, n_1 = 30, n_2 = 30, conf_level = 0.95)

Confidence level: 95%

2. R² with a noncentral-F confidence interval and AIPE sample size

ci_R2(R2 = 0.30, N = 100, p = 4, conf_level = 0.95)

Confidence level: 95%

# What N is needed so the 95% CI on R^2 has full width <= 0.20?
ss_aipe_R2(population_R2 = 0.30, width = 0.20, p = 4)

Confidence level: 95%

3. AIPE planning with a sensitivity check

# Plan N for a half-width of .10 on the SMD at delta = 0.40.
ss_aipe_smd(delta = 0.40, width = 0.20)

Confidence level: 95%

# How does that plan hold up if the truth is actually delta = 0.25?
set.seed(113)
ss_aipe_smd_sensitivity(
  true_delta      = 0.25,
  estimated_delta = 0.40,
  desired_width   = 0.20,
  G               = 200,
  print_iter      = FALSE
)

Confidence level: 95%

4. Welch’s t test with a tidy return

set.seed(113)
x <- rnorm(20, mean = 100, sd = 15)
y <- rnorm(20, mean = 110, sd = 25)
welch_t(x, y, conf_level = 0.95)

Confidence level: 95%

5. Reliability with a confidence interval

# Cronbach's alpha plus the Bonett (2002) CI from a covariance matrix.
S <- matrix(
  c(1.0, 0.6, 0.5, 0.5,
    0.6, 1.0, 0.6, 0.5,
    0.5, 0.6, 1.0, 0.6,
    0.5, 0.5, 0.6, 1.0),
  nrow = 4)
reliability_alpha(S = S, N = 250, ci_method = "bonett")

6. Kish’s design effect in a clustered sample

# 25 schools with attendance ranging from 0 (closed) to 30 (full class),
# ICC = .10. How much clustering information do we actually have?
sizes <- c(0, 0, 1, 1, 1, 2, 3, 5, 8, 10, 12, 15, 18, 20, 22,
           22, 25, 26, 28, 28, 30, 30, 30, 30, 30)
design_effect(cluster_sizes = sizes, icc = 0.10)

Numerical validation

DMAR ships a dedicated numerical-correctness test suite (tests/testthat/test-numerical-correctness.R and companion files) that pins its output against authoritative reference implementations and published values, so results are reproducible and checkable:

The machine-precision rows hold for the model and design classes the test suite pins, including the boundary cases the current tests add. The mixed-effects R-squared functions match r2mlm and the direct Johnson quadratic form across random-intercept, correlated random-slope, split (||) random-slope, multi-slope, and nested two-level models; on split random-slope models performance/insight omits the random-slope variance, so DMAR agrees with r2mlm and the Johnson form there and not with performance. The agreement procedures match irr and irrCAC on the ordinary rating designs and on the boundary cases the tests now cover: the zero-distance case of ratio-metric Krippendorff’s alpha, all-missing units in gwet_ac(), and incomplete category sets in cohen_kappa().

Learn more

• Package website: https://yelleknek.github.io/DMAR/
• Start here: vignette("DMAR") (overview), vignette("dmar_output") (the tidy dmar_tbl output and tidy()/glance()/as_kable() methods), vignette("mbess_to_dmar") (MBESS → DMAR migration).
• Full function reference and topical articles are on the website under Reference and Articles.

Relation to MBESS

DMAR is the modern, more general reimplementation and expansion of the MBESS package (Kelley, 2007a, Journal of Statistical Software, 20(8), 1–24; 2007b, Behavior Research Methods, 39(4), 979–984), which has been on CRAN for about two decades. MBESS was originally framed for the behavioral, educational, and social sciences; its use has grown well beyond that scope, and DMAR reflects how the methods are now applied across disciplines. The notational and programming changes (uniform snake_case, tidy data.frame(term, value) returns, native ggplot2 plotting, broader coverage of within-subjects, multivariate, mixed-effects, and sequential-estimation methods) were too extensive to ship under the MBESS name without breaking compatibility, so the work ships under a new name. MBESS itself remains stable on CRAN; DMAR is the recommended path forward for new users. See NEWS.md for the MBESS → DMAR migration mapping.

Relation to Maxwell, Delaney, and Kelley (MDK, 4th ed.)

Many of the methods in DMAR are discussed in Maxwell, Delaney, and Kelley (2027), Designing Experiments and Analyzing Data: A Model Comparison Perspective (4th ed., Routledge). Functions cite the specific MDK chapter in their @references block where the underlying method is discussed in detail. DMAR is broader than MDK: it covers methods (e.g., minimum-risk sequential estimation, equivalence testing, mediation effect size CIs, design effects for cluster sampling) that are outside the scope of an experimental- design textbook. Conversely, MDK covers conceptual material that falls outside the scope of an R package. The two are complementary.

Related packages

BUCSS (Bias-Corrected Uncertainty Adjusted Sample Size; Anderson, Kelley, and others) provides bias and uncertainty corrected sample size methods for several common designs, with overlap with DMAR’s AIPE family. Ken Kelley is a co-author of BUCSS; the two packages take complementary approaches to the small-sample sample size planning problem and reading them side by side is a good way to see where the AIPE and BUCSS traditions agree, where they diverge, and when each is most informative for a given planning question.

Citation

If you use DMAR in published work, please cite

Kelley, K. (2026). DMAR: Design, Measurement, and Analysis in R. R package version 1.0.0.

and, where appropriate, the original MBESS references for the methodological lineage:

Kelley, K. (2007). Confidence Intervals for Standardized Effect Sizes: Theory, Application, and Implementation. Journal of Statistical Software, 20(8), 1–24. doi:10.18637/jss.v020.i08

Kelley, K. (2007). Methods for the Behavioral, Educational, and Social Sciences: An R package. Behavior Research Methods, 39(4), 979–984. doi:10.3758/BF03192993

citation("DMAR") reproduces the canonical BibTeX entry from R.

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 and https://kenkelley.org/publications/ for related publications.