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.
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::ci_nc_t(...) |
Noncentral t; t.value -> t_value. |
MBESS::conf.limits.ncf(...) |
DMAR::ci_nc_F(...) |
Noncentral \(F\). |
MBESS::conf.limits.nc.chisq(...) |
DMAR::ci_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"]).
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.
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.
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.
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.
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.
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.
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.plot_smd(), plot_ci(), plot_R2(),
plot_trajectories(), and
plot_trajectories_fitted().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 the caller’s generator
state restored when a function given a seed returns.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.smdThe 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%
| 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 0.95The two return values compose with dplyr summaries and
ggplot2 plots without further wrapping.
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.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.