Moving From MBESS to DMAR: A Migration Guide and a Tour of the New Methods

Ken Kelley

August 2026

library(DMAR)

Why a Reimplementation and Expansion

The MBESS package (Methods for the Behavioral, Educational, and Social Sciences; Kelley, 2007a, Journal of Statistical Software; 2007b, Behavior Research Methods) shipped in 2006 and has been on CRAN ever since. Its uptake outgrew the original framing in two ways. First, the methods it implements are used well beyond the behavioral, educational, and social sciences, including in clinical and translational research, biostatistics, information systems, marketing, organizational science, sociology, education, and the methodological literature itself. Second, the package’s API conventions (dotted argument names, mixed-style returns, a verbose = flag controlling print) reflect an earlier era of R style. DMAR ships under a new name to make the scope expansion explicit and to introduce a uniform, modern API without breaking compatibility with the long-stable MBESS interface.

MBESS itself remains stable on CRAN; researchers with running scripts that depend on MBESS can continue to use it indefinitely. The purpose of this vignette is to help users who want to move forward to DMAR, either for new work or to gradually migrate existing scripts, and to make visible the methods that DMAR adds beyond MBESS’s scope.

Migration Table: The Renames That Matter

The naming convention in DMAR is snake_case throughout. Argument names use underscores rather than dots (conf_level, not conf.level; alpha_level, not alpha.level), and the canonical return is a tidy data.frame with term and value columns. The table below maps the most-used MBESS calls to their DMAR equivalents.

MBESS call	DMAR call	Notes
MBESS::ci.smd(ncp, n.1, n.2, conf.level)	DMAR::ci_smd(ncp, n_1, n_2, conf_level)	Same noncentral t inversion; tidy data.frame return.
MBESS::ci.smd.c(...)	DMAR::ci_smd_c(...)	Glass’s $g$ with control-group SD.
MBESS::ci.R2(R2, N, K, conf.level)	DMAR::ci_R2(R2, N, p, conf_level)	K -> p; same fixed-vs-random predictors switch.
MBESS::ci.reg.coef(...)	DMAR::ci_reg_coef(...)	Same noncentral / central paths.
MBESS::ci.rc(...), MBESS::ci.src(...)	DMAR::ci_rc(...), DMAR::ci_src(...)	Per-coefficient CIs.
MBESS::ci.cv(...)	DMAR::ci_cv(...)	CV with McKay/Vangel CIs.
MBESS::ci.pvaf(...)	DMAR::ci_pvaf(...)	Proportion of variance accounted for.
MBESS::ci.snr(...)	DMAR::ci_snr(...)	Signal-to-noise CI.
MBESS::ci.srsnr(...)	DMAR::ci_srsnr(...)	Square root of signal-to-noise CI.
MBESS::ci.sm(...)	DMAR::ci_sm(...)	Standardized-mean CI; capitalized Mean/SD are now mean/sd.
MBESS::ci.sc(...), MBESS::ci.sc.ancova(...)	DMAR::ci_sc(...), DMAR::ci_sc_ancova(...)	Standardized contrast CIs.
MBESS::ci.c(...), MBESS::ci.c.ancova(...)	DMAR::ci_c(...), DMAR::ci_c_ancova(...)	Unstandardized contrast CIs.
MBESS::ci.rmsea(...)	DMAR::ci_rmsea(...)	Noncentral-$\chi^2$ inversion for RMSEA.
MBESS::ci.cc(...)	DMAR::ci_r(...)	Correlation CI; r and n arguments.
MBESS::conf.limits.nct(...)	DMAR::conf_limits_nct(...)	Noncentral t; t.value -> t_value.
MBESS::conf.limits.ncf(...)	DMAR::conf_limits_ncf(...)	Noncentral $F$.
MBESS::conf.limits.nc.chisq(...)	DMAR::conf_limits_nc_chisq(...)	Noncentral $\chi^2$.
MBESS::ss.aipe.smd(delta, conf.level, width, ...)	DMAR::ss_aipe_smd(delta, conf_level, width, ...)	Same AIPE planner; tidy return.
MBESS::ss.aipe.R2(...)	DMAR::ss_aipe_R2(...)	AIPE planner for $R^2$; K -> p; random.regressors argument retired in favor of random_predictors.
MBESS::ss.aipe.reg.coef(...)	DMAR::ss_aipe_reg_coef(...)	AIPE planner for a regression coefficient.
MBESS::ss.aipe.rmsea(...)	DMAR::ss_aipe_rmsea(...)	AIPE for RMSEA.
MBESS::ss.power.R2(...)	DMAR::ss_power_R2(...)	Power-based planner; alpha.level -> alpha_level.
MBESS::ss.power.reg.coef(...)	DMAR::ss_power_reg_coef(...)	Power for a regression coefficient.
MBESS::cv(mean, sd)	DMAR::cv(mean, sd)	Coefficient of variation; tidy return.
MBESS::sd.unbiased(...)	DMAR::sd_unbiased(...)	Holtzman-corrected SD.
MBESS::signal.to.noise.R2(R.Square, ...)	DMAR::signal_to_noise_R2(R2, ...)	R.Square -> R2 per the meaningful-capital rule.
MBESS::smd(...), MBESS::smd.c(...)	DMAR::smd(...), DMAR::smd_c(...)	Standardized mean difference point estimates.
MBESS::HS, MBESS::Prime.Time	DMAR::holzinger_swineford, DMAR::prime_time_achievement	Same data under the documented snake_case names.

The convention for the rename is: 1. Replace . with _ in function and argument names (ci.smd -> ci_smd, n.1 -> n_1, conf.level -> conf_level). 2. Lowercase abbreviations that are not statistical notation (R.Square -> R2, Mean -> mean, SD -> sd); keep meaningful capitals (R2, N, S, Lambda, F_value). 3. Use p for the number of predictors (MBESS sometimes used K). 4. Use random_predictors (not random.regressors).

The DMAR return is a tidy data.frame with stable column schemas across the package. Scripts that consumed MBESS’s named-list returns generally need only adjust the extraction (e.g., out$Lower.Conf.Limit.smd becomes out$value[out$term == "lower_limit"]).

What DMAR Adds Beyond the MBESS Scope

The migration story is only half the story. The reason to move forward is what DMAR does that MBESS does not. The additions cluster in five areas.

1. Maximum Likelihood Multiple Regression With FIML (mlmr() / mlmr_mv())

mlmr() is an lm()-like front end to full information maximum likelihood regression. The formula interface and S3 methods mirror lm() (coef, vcov, confint, summary, anova, predict, update); the default confidence intervals are profile likelihood intervals with Wald and bootstrap as alternatives; and the missing data handling is missing = "fiml" by default. The multivariate sibling mlmr_mv() takes cbind(y1, y2) ~ ... and models the joint distribution of correlated outcomes, which is the case where the FIML advantage over listwise deletion is largest. The companion vignette vignette("mlmr", package = "DMAR") walks through the missingness scenarios in which FIML actually matters.

2. broom-Style Integration (generics::tidy() and generics::glance())

DMAR outputs dispatch through the broom-ecosystem generics in the generics package, so purrr::map_dfr(fits, generics::tidy) works across DMAR fits the same way it works across lm(), glm(), and other broom-supported models. The families covered as of this release: mlmr, mlmr_mv, cfa_1, the reliability family, the long-format CI family, the ANOVA effect size CI family, and the power planner family.

3. AIPE Sensitivity Analysis

Every closed-form AIPE planner has a Monte Carlo sensitivity companion (ss_aipe_*_sensitivity()) that simulates from a true population value to quantify the realized CI width and empirical coverage when the planning value is wrong. The companion is the recommended workflow when the planning value comes from a small pilot or from a literature with publication bias. A 10,000-replication sweep across the planner family, reporting realized interval width and empirical coverage for each, is maintained separately from the package.

4. ANOVA and ANCOVA Wrappers

DMAR adds tidy entry points for several ANOVA designs that required manual model fitting in MBESS: ancova() for the classical ANCOVA with adjusted means and the omnibus $\hat\omega^2_{\text{partial}}$ CI; mixed_anova() for the fixed-random F-ratio bookkeeping in a two-way crossed design; anova_within_two_way() for the two-factor within-subjects ANOVA with sphericity corrections per effect; manova_split_plot() for the mixed-design multivariate ANOVA. The functions return tidy data.frames suitable for direct piping into reporting tables.

5. Reliability With Proper CIs

The reliability family (reliability_alpha, reliability_omega (with a model implied or observed total-variance denominator; the latter is MBESS::ci.reliability’s “hierarchical” type), reliability_omega_categorical, reliability_kr20, reliability_H, and the dispatch wrapper reliability()) returns the point estimate alongside the Feldt/Bonett/Fisher CI, the delta method SE when applicable, the sample size, and the number of items, in a tidy schema that matches the rest of the package. cfa_1() provides the single-factor CFA fit on which several of those estimators depend.

Other Additions

• welch_t(): tidy two-sample t test with separate variances.
• randomization_test_paired(): exact paired sign-flip randomization test with Monte Carlo fallback.
• power_fisher_exact(): power of Fisher’s exact 2×2 test.
• is_orthogonal_set(): check orthogonality of an entire contrast matrix.
• cv_dunnett(), ci_dunnett(), ci_tukey_kramer(), ci_scheffe(): critical values and CIs for the classical multiple-comparison procedures, returning tidy data.frames.
• icc_lmer(): tidy ICC + CI from a fitted lmerMod.
• cv_smm(), cv_scheffe(), cv_tukey_hsd(): critical values for the classical procedures, with explicit handling of the Studentized maximum modulus distribution.
• A modernized, ggplot2-based set of plotting functions: plot_smd(), plot_ci(), plot_R2(), plot_trajectories(), and plot_trajectories_fitted().
• A package-wide term/value data.frame schema; broom-style S3 methods for many families; set.seed(113) as the package-wide reproducibility seed for examples and tests; seed = NULL as the default for every bootstrap and Monte Carlo function, with explicit save/restore of .Random.seed when the user supplies a seed.

A Short Worked Migration

A small MBESS script computing a noncentral t CI on the standardized mean difference (Cohen’s $d$), its companion AIPE sample size plan, and the realized CI width under the plan looks like this in MBESS:

# MBESS, classic
library(MBESS)
out_ci  <- ci.smd(ncp = 4, n.1 = 30, n.2 = 30, conf.level = 0.95)
out_ss  <- ss.aipe.smd(delta = 0.5, conf.level = 0.95, width = 0.40)
out_ci$Lower.Conf.Limit.smd
out_ci$Upper.Conf.Limit.smd

The DMAR equivalent, with tidy returns and the generics::tidy() route into a unified table:

ci  <- ci_smd(ncp = 4, n_1 = 30, n_2 = 30, conf_level = 0.95)
ss  <- ss_aipe_smd(delta = 0.5, conf_level = 0.95, width = 0.40)
ci

term	value
lower_limit	0.489
smd	1.03
upper_limit	1.57

Confidence level: 95%

ss

term	value
necessary_n_per_group	199
supposed_smd	0.5
width	0.4

Confidence level: 95%

generics::tidy(ci)
#>   term estimate  ci_lower ci_upper conf_level
#> 1  smd 1.032796 0.4891759 1.568559       0.95

The two return values compose with dplyr summaries and ggplot2 plots without further wrapping.

See Also

• vignette("DMAR", package = "DMAR") for the introductory tour.
• vignette("mlmr", package = "DMAR") for the FIML regression family.
• vignette("reliability", package = "DMAR") for the reliability family.
• NEWS.md for the per-release changelog.

References

Anderson, S. F., Kelley, K., & Maxwell, S. E. (2017). Sample size planning for more accurate statistical power: A method adjusting sample effect sizes for publication bias and uncertainty. Psychological Science, 28(11), 1547–1562. https://doi.org/10.1177/0956797617723724

Kelley, K. (2007a). Confidence intervals for standardized effect sizes: Theory, application, and implementation. Journal of Statistical Software, 20(8), 1–24. https://doi.org/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. https://doi.org/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.

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.

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. https://doi.org/10.1146/annurev.psych.59.103006.093735

Steiger, J. H. (2004). Beyond the F test: Effect size confidence intervals and tests of close fit in the analysis of variance and contrast analysis. Psychological Methods, 9(2), 164–182.