Changelog

BUCSS 2.0.0

This is a major release that modernizes the user-facing API, refines the internal computation, and adds planning options. It is the first CRAN release since 1.2.1.

The bias and uncertainty correction and the sample size computations follow the same method as 1.2.1. The new options default to what 1.2.1 assumed: the regression planners’ random_predictors defaults to FALSE (predictors fixed by design) and the within-subjects and split-plot planners’ epsilon defaults to 1 (sphericity), so a plan that uses neither is computed in the frame 1.2.1 used. For every documented example that 1.2.1 can also compute, the rounded (three-decimal) adjusted noncentrality parameter matches the 1.2.1 value, except in two examples that set alpha_prior = 1. The main numerical change is the engine that finds the noncentrality parameter (see “Engine” below), which shifts some planned sample sizes, most by a few units, and makes them more accurate. Three smaller deliberate changes also move plans. An alpha_prior of 1 is computed as exactly 1, where 1.x computed with .999 in its place, which leaves most such plans unchanged and makes the rest slightly smaller (see “No Publication Filter” below). And, described under “Bug Fixes”, the general between-subjects function’s df_denominator argument, silently ignored in 1.x, now functions, and the independent t planner rounds the harmonic mean of two prior group sizes in integer arithmetic, which changes the plan (to a smaller, correct one) for pairs whose harmonic mean is a whole number. One request that 1.x answered now stops: a desired_power at or below alpha_planned, for which 1.x returned the smallest sample size it considers, is not allowable (also under “Bug Fixes”). Every plan also gains one row, its planning effect size (see “Sampled Predictors, Sphericity, and the Planning Effect Size” below).

Engine: Root Finding Instead of a Fixed Grid

The bias and uncertainty adjusted noncentrality parameter is found by solving the truncated-likelihood equation directly with stats::uniroot(), replacing the search over the fixed grid seq(0, 100, by = step) used in 1.x. Two consequences:
- The adjusted parameter is no longer capped at 100. Prior studies with very large effects, for which the implied parameter exceeds 100, previously returned a silently truncated (incorrect) result; they are now solved correctly.
- The parameter is exact rather than snapped to the grid resolution. The rounded (three-decimal) parameters are unchanged from 1.2.1 (apart from the harmonic-mean cases above and plans with alpha_prior = 1, described in the next section), but the extra precision shifts some planned sample sizes. For the documented unequal-n replication example (ss_buc_independent_t(t_observed = 3, n = c(50, 55), assurance = .90)), the necessary sample size moves from 1482 (the value BUCSS 1.x reported under its grid search) to 1485, which is the smallest sample size that actually reaches the target power. Across the developer characterization grid, leaving out the cases the old cap affected, the df_denominator change, and the harmonic-mean correction, about 94% of sample sizes are unchanged (the plans with alpha_prior = 1 included); most of the rest move by a few units and none by more than 2 percent. The exception lies close to the assurance ceiling, beyond all but one case in that grid, where the corrected parameter is small beside the 1.x search’s resolution of .001: within about 25 times that resolution on the F scale (50 times on the t scale) a 1.x sample size can differ from the 2.0.0 one by more than 2 percent, and within about .001 of the ceiling by 10 percent or more, in either direction. Among the documented examples, eight planned sample sizes move: 1482 to 1485 (ss_buc_independent_t); 1904 to 1902 (ss_buc_rm_anova); 968 to 969 (ss_buc_mixed_anova); 1491 to 1489 (ss_buc_mixed_anova_general); 3963 to 3960 (ss_buc_reg_joint); and, in the tables of the vignette “Assurance and the Publication Bias Correction”, 5069 to 5042 (ss_buc_independent_t(t_observed = 3, n = 20, assurance = .90)) and, from ss_buc_paired_t() with alpha_prior = 1 and assurance = .80, 377 to 376 and 915 to 910 (for a prior t from 30 pairs with a two-sided p value of .10 and of .15; in these two the rounded parameter moves as well, from .793 to .794 and from .508 to .509). Each new value is the smallest sample size that actually attains the target power at the exact noncentrality parameter; they are pinned in the package’s regression tests.
Every worked example in Anderson, Kelley, and Maxwell (2017) and its supplemental material is reproduced, and the values are pinned in the regression tests. The article’s three researchers plan from effect sizes it prints rounded (they are simulation medians; its notes 18 to 20), and at the printed values four of its sample sizes differ: d = 0.68 gives 330 per group where the article prints 332, an f squared of .09 gives 1,400 where it prints 1,418, and a paired d of .50 gives 50 and 80 where it prints 49 and 79. Each printed size is reproduced by a value that rounds to the printed one (d = 0.6799, an F of 6.4773, which is an f squared of about .08996, and a paired d of .502), in BUCSS 1.2.1 as in 2.0.0.
There is no grid-resolution argument or option. The 1.x step argument and any grid resolution setting are gone, since root finding has no resolution parameter.

No Publication Filter: An `alpha_prior` of 1 Is Exactly 1

An alpha_prior of 1 is computed as exactly 1. The method’s article describes the setting: “If publication bias is thought not to apply, [alpha_P] can be set equal to 1” (Anderson, Kelley, & Maxwell, 2017, p. 1552, where alpha_P is the level alpha_prior names). With no publication filter every result would have been published, so the publication critical value is 0, the power term that truncates the likelihood is 1, the prior statistic has its ordinary distribution, and the largest supportable assurance is exactly 1 - p. BUCSS 1.x computed with .999 in place of 1, that is, with a filter that drops results whose p value is above .999. No value is substituted for 1 any longer.
How far plans move. Computing with exactly 1 rather than .999 raises the adjusted noncentrality parameter a little, so a plan with alpha_prior = 1 is the same size or smaller, never larger. Across the 2,172 such plans of the developer characterization grid (all eighteen planners), computed both ways, the parameter rises by a median of about 0.002 percent and at most 1 percent; 95 percent of the planned sample sizes are unchanged, and the other 103 are smaller, most by one participant (79 of them), the largest moves being about 1 percent of a very large plan (36,146 to 35,787) and one participant in a small one (64 to 63, or 1.6 percent). Against BUCSS 1.2.1 on the grid described under “Engine”, 829 of the 855 comparable plans with alpha_prior = 1 (97 percent) have the sample size 1.2.1 gave, and the other 26 are one or two participants smaller, at most 1.6 percent (64 to 63 per group); none is larger. The rounded (three-decimal) parameter differs from the 1.2.1 value in 182 of the 855, each higher by .001 (at most 0.2 percent). Outside that grid, a paired t significant at .01 from 3,000 pairs, planned with alpha_prior = 1 at an assurance of .80, needs 7,823 pairs where 1.2.1 gave 7,825. Plans with alpha_prior below 1 are unaffected.
alpha_prior also accepts the string "No Publication Filter" as a synonym for 1, so that a call can say what the value means. It is read without regard to case, with spaces, underscores, or hyphens between the words; the result echoes it as an alpha_prior of 1; the deprecated functions’ alpha.prior accepts it too; and any other string is refused with a message naming the range (above 0, at most 1) and the string.

API Modernization

The exported functions are renamed to the ss_buc_* prefix, so the names make clear that these planners apply the bias and uncertainty correction rather than ordinary power analysis. The between-subjects ANOVA planner is split into a one-way and a two-way function, and the two t test planners gain effect size aliases:

1.x	2.0.0
`ss.power.it`	`ss_buc_independent_t` (alias `ss_buc_smd`)
`ss.power.dt`	`ss_buc_paired_t` (alias `ss_buc_smd_paired`)
`ss.power.ba`	`ss_buc_one_way_anova`, `ss_buc_factorial_anova`
`ss.power.ba.general`	`ss_buc_factorial_anova_general`
`ss.power.wa`	`ss_buc_rm_anova`
`ss.power.wa.general`	`ss_buc_rm_anova_general`
`ss.power.spa`	`ss_buc_mixed_anova`
`ss.power.spa.general`	`ss_buc_mixed_anova_general`
`ss.power.reg1`	`ss_buc_reg_coef`
`ss.power.reg.all`	`ss_buc_R2`
`ss.power.reg.joint`	`ss_buc_reg_joint`

Arguments are renamed to snake_case (for example t.observed becomes t_observed, alpha.prior becomes alpha_prior, df.numerator becomes df_numerator), the 1.x power argument is now desired_power (matching the DMAR package’s planners and the desired_power row it echoes), and effect values are snake_case ("factor.A" becomes "factor_A", "between.only" becomes "between_only", and so on).
The functions now return a tidy object of class bucss_plan rather than a printed two-element list, shaped like the result tables of the sibling package DMAR. It is a data.frame with a character term column and a numeric value column. The design results come first: the necessary sample size, named for its unit (necessary_n_per_group, necessary_n_per_cell, or necessary_N); total_N, the implied total, for per-group and per-cell designs; actual_power, the power of the plan returned; and ncp_adjusted, whose scale (that of the prior study’s t, F, or chi square statistic) each help page states. Rows echoing the planning inputs follow (a length-2 prior n echoes as n_1/n_2), so the assumptions travel with the result through subsetting and CSV export. Only non-numeric metadata (the design label, the effect tested, the unit) plus the assurance ceiling and the planned test’s degrees of freedom travel on attributes.

Backward Compatibility: 1.x Names Are Deprecated, Not Removed

The dot-named 1.x functions (ss.power.it, ss.power.dt, ss.power.ba, ss.power.ba.general, ss.power.wa, ss.power.wa.general, ss.power.spa, ss.power.spa.general, ss.power.reg1, ss.power.reg.all, ss.power.reg.joint) still work. Each accepts the 1.x argument names, translates them, and calls the corresponding ss_buc_* function, so scripts written for BUCSS 1.x continue to run. Calling one issues a deprecation warning that names its replacement, once per function per session; the ss_buc_* functions themselves never issue it. ss.power.ba dispatches to ss_buc_one_way_anova when levels.B is NULL and to ss_buc_factorial_anova otherwise; the dotted effect values are translated to snake_case; and the 1.x step argument is accepted and ignored.
The deprecated functions return a bucss_plan object, but a [[ method preserves the 1.x positional extraction: result[[1]] is the necessary sample size and result[[2]] is the adjusted noncentrality parameter, the same two values the 1.x functions returned as an unnamed list. Column and name access (result$value, result[["value"]]) are unaffected.

New Designs

Six designs join the twelve that 1.2.1 covered. Two re-present computations the package already made (the one-sample t and the analysis of covariance); four extend the correction to statistics or distributions it did not previously handle.

ss_buc_correlation() plans from a prior Pearson correlation (or its t). A correlation study samples both variables, so its test statistic is not noncentral F: conditional on the sampled predictor it is, but the noncentrality parameter is itself random, proportional to the sampled spread of the predictor. The marginal distribution is a chi square mixture of noncentral F distributions, and it is more dispersed. The planner uses that mixture, which has an exact closed form (the Poisson weights of the noncentral F become negative binomial weights), for both the correction and the planned study’s power, so the returned sample size is exact for bivariate normal data. Treating the statistic as noncentral F, which is correct only when the predictor is fixed by design, overstates the planned study’s power by roughly 1/n for a plan of n participants (about .01 at n = 100 and .05 at n = 20) and returns a sample size typically 2 to 5 participants light. When the predictor really is fixed, the analysis is a regression slope and ss_buc_reg_coef() with p = 1 and random_predictors = FALSE is the planner for it; with random_predictors = TRUE that call returns exactly this planner’s result. The largest supportable assurance is the same either way, because at a zero effect the mixture is the central F distribution.
ss_buc_one_sample_t() plans a one-sample t test. This is exactly the computation ss_buc_paired_t() performs, reported with a one-sample design label and a total sample size instead of a number of pairs.
ss_buc_ancova() plans an analysis of covariance from the number of covariates, rather than requiring the user to work out the error degrees of freedom for ss_buc_factorial_anova_general(). With no covariates it reproduces the corresponding ANOVA planner exactly. The omnibus group effect is the default; a single-degree-of-freedom contrast among the adjusted means is available through df_numerator = 1.
ss_buc_manova() plans a two-group multivariate comparison (Hotelling’s T squared), taking either the F or the T squared. This is the multivariate case whose hypothesis has rank one and therefore an exact noncentral F; hypotheses of higher rank have an eigenvalue spectrum rather than a single noncentrality parameter and are outside the method.
ss_buc_chisq_diff() plans from a nested model chi square difference (likelihood ratio) test, the test used for a constrained path in a structural equation model. It is not for an omnibus model fit chi square, where a publishable result is a small statistic and the selection region is inverted, and the function refuses that case rather than only warning about it. That is why it takes both models’ degrees of freedom, df_full and df_restricted, rather than only their difference: an omnibus fit chi square is the comparison of a model against the saturated model, which has zero degrees of freedom, so df_full = 0 is its signature. Given only the difference, nothing distinguishes a fit test from a genuine comparison of two substantive models and the mistake would pass silently. (A just-identified full model also has zero degrees of freedom; the help page and the error say how to plan that case.) The help page states the finite-sample liberality of the likelihood ratio test for a named model.
ss_buc_welch_t() plans a Welch (unequal variance) t test from the two prior group sizes and the groups’ relative spread. Only the ratio of the standard deviations enters the computation, but a paper usually reports the group standard deviations or variances rather than their ratio, so the spread may be given any of three ways: sd_ratio, or sd_1 and sd_2, or var_1 and var_2. Exactly one form is accepted per call, and the form given is the one echoed back in the result. A Welch statistic is not exactly noncentral t, so this planner is an approximation, and its help page says which assumption it needs and suggests re-running across a range of ratios. A minimum planned sample size of 10 per group is imposed because of estimation accuracy issues at smaller sample sizes: below 10 per group the approximation overstates the real Welch test’s power, badly so for the smallest, while from 10 per group it is within .006 of the real test’s power for every plan returned at an alpha_planned of .05. At a stricter alpha_planned it overstates the power of the smallest plans by more (at 10 per group by up to about .012 at .01 and .025 at .001) and is within .006 again from 14 and 20 per group, respectively.

The Assurance Ceiling, Drawn

Added plot.bucss_plan(), so plot() on any plan draws its assurance ceiling. The method re-runs that plan’s own planner across a range of assurance values and draws two panels sharing an assurance axis: the adjusted noncentrality parameter falling to zero at the ceiling, and the necessary sample size it implies growing asymptotically on a logarithmic scale. The ceiling is marked, the plan itself is marked, and the region beyond the ceiling is shaded and labeled as one where no plan exists.
The point of the figure is the shape. Assurance does not degrade gracefully as it rises, it hits a wall, and that is the hardest thing about the method to convey in prose. For a prior independent t test with t = 3.00 and 20 per group, the plan runs from 25 per group at assurance = .50 to over 65,000 just short of the ceiling at .905, and is undefined above it.
The grid of assurance values is spaced evenly in the logarithm of the remaining headroom rather than in assurance, so roughly half the plotted points fall in the last few hundredths below the ceiling. Spacing evenly in assurance would draw the wall as a single jump between two grid points and lose the shape.
The plotted values are returned invisibly as an ordinary data.frame, one row per assurance, so the figure can be redrawn with any other graphics system. The method is written in base graphics; graphics joins generics and stats in Imports, and no plotting package is required.

Auditing a Plan by Simulation

Added ss_buc_sensitivity(), a Monte Carlo companion that takes a plan from any ss_buc_* function and exercises the claim the method actually makes. It reads the design and the planning inputs off the plan, simulates prior studies of that design from a true effect the user names, discards the ones a literature would not have published, runs the same planner on each survivor, and reports how often a plan built this way reaches the target power. There is deliberately no default for true_ncp: defaulting to the corrected estimate would ask the method to grade its own answer.
The output distinguishes two attainment rates, and the distinction settles a question the method’s documentation had left implicit. attainment counts only the prior studies for which a plan was issued, which is what a user experiences; attainment_with_refusals counts a refusal as attaining, because declining to plan is the limiting case of an arbitrarily conservative plan, and it is the rate the assurance guarantee is about. When the refusal rate is high the first sits well below assurance, which is the visible price of the method refusing rather than guessing rather than a defect.
The package-wide verification ships with the package. The simulation harness that validated the planners, twenty-four checks covering assurance calibration across every sampling family, raw-data audits of the correlation planner, the Welch planner, and the regression coefficient planner with sampled predictors, and the finite-sample behavior behind the chi square caveat, is installed under inst/tools/ and reruns in a few seconds (under a minute in its full mode) with source(system.file("tools", "verify_by_simulation.R", package = "BUCSS")). It reports the version and location of the BUCSS it verified.
ss_buc_sensitivity() reports the Monte Carlo standard error of both attainment rates. Only the planner’s own refusals count as refusals; any other error stops the audit with its message, and the planner’s warnings are summarized once rather than repeated for every simulated study (the warning for an assurance below .5, which the plan already raised when it was built, is not repeated). A plan given as Hotelling’s T squared can be audited like any other.
Neither leaves a trace in the session. ss_buc_sensitivity()’s seed defaults to NULL, which draws from the caller’s own random number stream; a supplied seed is set through withr::local_seed(), so the caller’s generator state and kind are restored on exit (or removed again, if the caller had none), and the package never reads or writes the random number state itself. withr (>= 3.0.3; earlier versions of local_seed() did not restore anything when called from inside a function) joins Imports. The harness runs entirely inside local() with the same seed handling, so sourcing it creates or overwrites nothing in the global environment, leaves the caller’s random number state as it was, and reports a failed check as an error rather than quitting the session.

Sampled Predictors, Sphericity, and the Planning Effect Size

The three regression planners, ss_buc_reg_coef(), ss_buc_R2(), and ss_buc_reg_joint(), take a random_predictors argument (default FALSE, the fixed-predictor frame of 1.2.1) for predictors sampled along with the outcome, as is typical of observational research in the behavioral sciences (Kelley & Maxwell, 2003). Whether the predictors are fixed by design or sampled changes neither the estimates nor the distribution of the test statistic when the effect is zero, but it changes the statistic’s distribution, and so the power, when the effect is not zero (Sampson, 1974; Gatsonis & Sampson, 1989). With random_predictors = TRUE the noncentrality parameter is itself random, proportional to the sampled spread of the tested predictors once the other predictors are accounted for (chi square on N - 1 - (p - q) degrees of freedom when q of p predictors are tested), so the statistic follows a chi square mixture of noncentral F distributions. The mixture has an exact closed form (the Poisson weights of the noncentral F become negative binomial weights), used for both the correction and the planned study’s power; it is the distribution ss_buc_correlation() uses: with one predictor each of the three returns exactly what ss_buc_correlation() returns for the same prior study (ss_buc_reg_coef(p = 1) for the same t and N, and ss_buc_R2(p = 1) and ss_buc_reg_joint(p = 1, p_joint = 1) for an F_observed equal to t squared), with the planning effect size stated in each planner’s own metric. Treating sampled predictors as fixed overstates the planned study’s power, by about 1/n for a plan of n participants with one predictor and about 4/n for a coefficient among eight (1/n to 1.4/n for the squared multiple correlation). Which frame plans the larger study depends on the other predictors and on how far the plan outgrows the prior study; the help pages give the pattern. The largest supportable assurance is the same in both frames. The frame is echoed as a random_predictors row (1 or 0) on every regression plan, named in the printed footer, and followed by ss_buc_sensitivity() and plot(). The deprecated ss.power.reg1(), ss.power.reg.all(), and ss.power.reg.joint() keep the fixed-predictor computation of 1.x.
The within-subjects and split-plot planners, ss_buc_rm_anova(), ss_buc_rm_anova_general(), ss_buc_mixed_anova(), and ss_buc_mixed_anova_general(), take an epsilon argument (the last argument, default 1) for a prior study reported with Greenhouse-Geisser or Huynh-Feldt corrected degrees of freedom: epsilon is the reported numerator degrees of freedom divided by the uncorrected ones. The prior study’s F and the planned study’s power are both treated under the approximation of Muller and Barton (1989), with the degrees of freedom and the noncentrality parameter multiplied by epsilon (the convention of DMAR’s ss_power_rm_anova() and ss_power_split_plot_anova()), and ncp_adjusted stays on the sphericity scale. It applies to within-subjects effects and to the interaction of a between-subjects and a within-subjects factor, each with its own lower bound; the between-subjects effect of a split-plot design involves no sphericity, so a value other than 1 is ignored there, with a warning. The correction is an approximation, and the help pages report its accuracy in simulations of raw data: at an assurance of .80, plans reached the target power in .70 to .80 of replications for trend-like effects and in .92 to .99 for effects in the least variable contrasts, against .26 to .64 and .70 to .87, respectively, when the departure was ignored. The default, epsilon = 1, reproduces the plans that assume sphericity exactly. Every plan for an effect with a within-subjects component echoes epsilon as a row (1 under sphericity), and ss_buc_sensitivity(), plot(), and planning_sentence() follow it.
Every plan reports its planning effect size, an adjusted effect size: the value, in the design’s usual effect size metric, used to plan the sample size given the situation at hand (the prior study’s result, the publication bias assumed through alpha_prior, and the uncertainty allowed for through assurance). It is ncp_adjusted restated in that metric, as a row directly after ncp_adjusted, and it is what the plan is built on, not an estimate of the population effect to report: above an assurance of .5 it is deliberately conservative (a lower confidence limit), and at .5 it is the median unbiased estimate. The rows are smd_planning (the independent t planner and its alias ss_buc_smd(), and the Welch t planner, which standardizes by the root mean square of the two standard deviations, following Bonett, 2008), dz_planning (the paired t planner and its alias; Cohen’s d_z, the mean of the paired differences divided by their standard deviation, where the z names the difference score, which Cohen, 1988, writes Z = X - Y), std_mean_planning (the one-sample t), f_planning (Cohen’s f relative to the effect’s own error term, for all eight analysis of variance and covariance planners, so that f squared over 1 plus f squared is the population partial eta squared for the effect), f2_planning (Cohen’s f squared, for a regression coefficient and for a subset of predictors), R2_planning (the squared multiple correlation), rho_planning (the correlation), D2_planning (the squared Mahalanobis distance, for the two-group multivariate comparison), and rmsea_d_planning (RMSEA_D, with D for difference: the root mean square error of approximation of the chi square difference test between the two nested models, as distinct from the RMSEA of either model’s fit, following Savalei, Brace, & Fouladi, 2024). actual_power is exactly the ordinary power of a study of the returned sample size when the population effect is the planning effect size, in the plan’s own frame (with sampled predictors, the random-predictor power; with epsilon below 1, the Muller and Barton approximation, the effect staying on the sphericity scale). For an unbalanced prior study the row belongs to the same balanced design as ncp_adjusted. planning_sentence() names it in a second sentence and glance() gives it a column; tidy() and the 1.x [[1]]/[[2]] positions are unchanged. For the within-subjects and split-plot planners, f_planning is not the raw-score f that repeated measures power functions such as DMAR’s ss_power_rm_anova() take; the help pages give the conversion.

New Features

The independent and paired t test planners are available under both test-specific names (ss_buc_independent_t, ss_buc_paired_t) and effect size names (ss_buc_smd, ss_buc_smd_paired) for the standardized mean difference. The two names in each pair are the same function.
Added print.bucss_plan(), which prints the aligned term/value table in the DMAR display style (whole numbers clean, other values at options(bucss.digits) significant figures, with more where fewer would show a value below 1 as 1, stored values never rounded) and closes with factual footer lines naming the design, for the regression planners the predictor frame, the unit the sample size is counted in, and the largest assurance the prior result supports. A knitr::knit_print method renders the same table as a kable in R Markdown.
The effect argument also accepts case-insensitive shorthand in the two-way between-subjects, within-subjects, and split-plot planners, for example "A" for "factor_A", "AxB" for "interaction", and "bs"/"ws" for "between"/"within". The snake_case values remain the documented forms and the result always records the canonical one.
Added tidy() and glance() methods for bucss_plan results (from the generics package), with the same division of labor as DMAR’s planners: tidy() is the compact estimate view (term = "sample_size", estimate, and power), and glance() is the one-row wide view that begins with the same three columns and then has every quantity and echoed planning input as a column, plus the design metadata and the planned study’s test degrees of freedom (df_effect, and df_error where the test has a denominator). Both stop with an explanation, rather than reporting one plan’s numbers for another, when given several plans stacked into one object or a plan with one of its rows removed.
Every planner reports actual_power, the statistical power of the plan it reports: the power of a study of the returned sample size when the effect is the returned adjusted noncentrality parameter (equivalently, the planning effect size). Because a sample size must be a whole number, it is never below the requested desired_power and is usually a little above it. Each help page states this definition.
Added planning_sentence(), which turns a result into the sentence an author writes in a manuscript’s planning section (the analog of DMAR’s results_sentence()). The sentence names only the corrections the plan actually made: the publication bias correction, with its assumed threshold, when alpha_prior is below 1, and the uncertainty correction when assurance is above .5, and it notes a departure from sphericity when epsilon is below 1. A second sentence names the planning effect size and its metric.
The zero-noncentrality error now says plainly that re-running at a lower assurance is not free: the plan it returns carries that lower assurance, not the one originally requested, so the long run proportion of replications reaching the target power falls accordingly. Raising alpha_prior is a different kind of remedy, changing an assumption about the prior study’s literature rather than lowering the guarantee. The assurance vignette develops the distinction.
When the correction drives the corrected noncentrality parameter to zero, the error reports the largest assurance the prior result can support (the closed-form ceiling 1 - p/alpha_prior, where p is the prior study’s p value) and states whether lowering assurance to at or below that ceiling can reach a usable plan, or whether raising alpha_prior is the only remaining lever. The message clarifies that alpha_prior is the analyst’s assumption about the publication threshold the prior study’s literature faced, not a change to the prior study itself. Each planner’s documentation states this ceiling and illustrates it with a runnable try() example.
On a successful call, the result also reports the largest assurance the prior study can support and, for per-group and per-cell designs, the implied total sample size (total_N). Both are shown by print.bucss_plan() and travel on the object.
Added an inst/CITATION file. citation("BUCSS") returns the package reference, so that the version and CRAN source are credited, together with the four articles behind the methods: Anderson, Kelley, and Maxwell (2017), Anderson and Kelley (2024), Anderson and Maxwell (2017), and the Anderson
1. regression tutorial.
Added a vignette on planning replication studies, which walks through why a literature built from underpowered studies inflates effect sizes and how the bias and uncertainty correction repairs the resulting sample size plan.
Added a vignette on planning a correlation study, which explains why a correlation is not a fixed-predictor regression slope, when ss_buc_correlation() and ss_buc_reg_coef() each apply, and how to audit a correlation plan with ss_buc_sensitivity() and read its two attainment rates.

Bug Fixes

A negative observed t is now accepted by every planner that takes a t (ss_buc_independent_t(), ss_buc_paired_t(), ss_buc_one_sample_t(), ss_buc_welch_t(), ss_buc_reg_coef(), and ss_buc_correlation()), each of which plans from its magnitude. The publication rule the correction assumes is two-sided and the truncated likelihood is symmetric in the sign of t, so t_observed = -3 is the same evidence as t_observed = 3; the sign records which group was subtracted from which (or the direction of the paired difference, or the coefficient’s coding), never the strength of the prior result. In 1.x and the earlier 2.0.0 drafts the two t-test planners rejected any negative t as “nonsignificant”, however large its magnitude, with advice (raise alpha_prior) that could not help, while ss.power.reg1/ss_buc_reg_coef silently accepted the same input; the three planners now agree. The signed value is still echoed in the stored planning inputs, and a genuinely nonsignificant magnitude is still rejected. Scripts (and the designingexperiments.com apps) that previously received an error for a significant negative t now receive the plan its magnitude implies; no result that was previously returned has changed.
An assurance or power of exactly 0 or 1 (after the percentage coercion, so also power = 100) is now rejected with a clear error. In 1.x an assurance of 0 and a power of 0 or 100 percent were accepted and silently returned meaningless results: an assurance of 0 has no solution, so the reported noncentrality parameter was an artifact of the internal search bound, and a target power of 100 percent is unattainable, so the returned sample size was where the computed power crossed 1 by floating-point error. An assurance of 100 percent (assurance = 100) already stopped in 1.x, but with the misleading message that the corrected noncentrality parameter was zero. The documented rule that an assurance or power of exactly 1 is read as 1 percent is unchanged.
A desired_power (1.x power) at or below alpha_planned is not allowable and now stops, with an error that says so and names both values. A test’s power is never below its Type I error rate, so every sample size meets such a target and no plan is meaningful; 1.x returned the smallest sample size its search considers (2 per group, for instance), with no notice. The check follows the percentage reading, so desired_power = 1, which is read as 1 percent, stops unless alpha_planned is below .01, and the message then says how the value was read. The deprecated functions stop in the same way.
When the two prior group sizes given to ss_buc_independent_t() (or ss.power.it) have a harmonic mean that is a whole number, the planner no longer plans from a spurious second rounding one unit off. The mean was computed in floating point, where a whole number can land a hair away from itself: with t_observed = 3 and the other arguments at their defaults, n = c(49, 49) planned 222 per group where n = 49 plans 219, and n = c(10, 15), whose harmonic mean is exactly 12, planned 177 where 12 plans 154. The mean is now rounded in integer arithmetic. Only such inputs change, and the change is in the direction of a smaller, correct plan.
For a prior study whose group sizes are unequal, the between-subjects and split-plot planners (and the independent t and MANOVA planners) reduce it to a balanced design rounded both down and up. When a statistic is significant at the prior study’s own degrees of freedom but not at the rounded-down design, the error now says exactly that, rather than calling the prior result nonsignificant, and names an alpha_prior at which a plan can be made. And when the correction reaches zero, the error names the smaller of the two rounded designs’ ceilings, so re-running at the assurance it names now produces a plan; before, it could name the larger one, which failed again.
ss.power.ba and ss.power.wa refuse effect = "factor.B" (as well as the interaction) when levels.B is not given. 1.2.1 refused only the interaction, and silently returned the Factor A plan for "factor.B".
ss_buc_factorial_anova_general() and ss_buc_ancova() reject a df_numerator larger than cells - 1, and ss_buc_mixed_anova_general() rejects numerator degrees of freedom no design of its kind can have and warns that df_num_within is ignored except for the between-within interaction. Each used to return an ordinary-looking plan. The 1.x wrappers ss.power.ba.general and ss.power.spa.general inherit the same checks, so such 1.x calls now stop with an explanation (the wrapper passes df.num.within on only for the interaction, as 1.x used it, so 1.x calls that supply it with another effect do not warn).
df_denominator now functions in ss_buc_factorial_anova_general(). In 1.x it was accepted but silently ignored, and the documented example supplied an impossible value (df_denominator = 117 with N = 120 and cells = 6, for which the maximum residual degrees of freedom is 120 - 6 = 114). The difference (N - cells) - df_denominator is now treated as the number of nuisance parameters (covariates or blocking factors) and carried through to the planned study: supplying df_denominator = N - cells reproduces the no-nuisance case, while a smaller value correctly calls for a larger sample. Values greater than N - cells are rejected.
The error shown when N is omitted in ss_buc_reg_coef(), ss_buc_R2(), and ss_buc_reg_joint() incorrectly described N as the number of pairs. It now correctly describes N as the total sample size of the previous study.
The nonsignificance error in ss_buc_R2() and ss_buc_reg_joint() referred to t.observed. These functions test an F statistic, so the message now refers to F_observed.
Omitting effect in the between-subjects, within-subjects, and split-plot functions no longer fails with “the condition has length > 1” under R >= 4.2. effect now defaults to its first choice.
ss_buc_rm_anova() no longer silently returns the single-factor result when effect is "factor_B" or "interaction" but levels_B is not supplied; it now stops and explains that levels_B is required for those effects.
The planners give clearer errors for more input mistakes: a missing structural argument (cells, df_numerator, num_groups, df_num_within, p, or p_joint) and a prior N too small for the design (fewer than two observations per cell or group) are now reported with an informative message.
ss_buc_independent_t() now echoes the prior study’s sample size (n or N, whichever was supplied) in the result’s planning-input rows, and hence in the printed table, as every other planner already did, so a saved or printed result can be traced back to the prior study.
Every planner now validates that its observed statistic is a single finite number and that its design counts (n, N, levels, cells, num_groups, p, p_joint, prior degrees of freedom) are single whole numbers, naming the offending argument. In 1.x a fractional count could flow through the search and return a non-integer “sample size” (ss.power.reg1(t.observed = 3, N = 150, p = 2.5) returned 624.5), and NA, infinite, or vector inputs died inside base R with “missing value where TRUE/FALSE needed” or “the condition has length > 1”. A within-subjects N of 1, or an independent-samples total N below 4, is likewise rejected up front instead of reaching qt()/qf() with zero degrees of freedom.
A last round of guards. A malformed effect (two values included, of which the first was used without notice), a Welch spread or an alpha_planned too extreme to compute with, and a missing first argument to ss_buc_sensitivity() or planning_sentence() each get a clear error naming the argument; ss_buc_independent_t() reads n = NULL with N as N alone; ss_buc_manova() no longer raises a stray warning beside an error; a nonsignificance refusal at alpha_prior = 1 no longer advises raising alpha_prior; a printed value below 1 is never shown as 1; the 1.x positional [[ applies only to a single plan; and loading the package enforces the withr (>= 3.0.3) minimum, through an import in NAMESPACE.

Documentation

Added the reference Maxwell, Delaney, and Kelley (2027, Designing Experiments and Analyzing Data: A Model Comparison Perspective, 4th ed.) to the package references, DOIs for Anderson and Maxwell (2017) and Taylor and Muller (1996), and ORCIDs for all three authors.
The package help page, every planner’s assurance argument, and vignette("understanding-assurance", package = "BUCSS") spell out how assurance is used. It is the long run proportion of planned studies whose power at the true effect reaches desired_power, when each is planned from a prior study published under the rule alpha_prior describes, and the planners deliver it by planning from the lower limit of a one-sided confidence interval at level assurance, computed from the publication-truncated distribution of the prior statistic. The term follows Kelley and Maxwell (2003), who used assurance in sample size planning for the probability that a confidence interval will be no wider than desired; that reference joins every planner’s help page.
Safeguard power (Perugini, Gallucci, & Costantini, 2014) guards against a prior effect size that is too large by planning for the lower limit of a two-sided 60% confidence interval around it rather than for the effect size itself; it allows for uncertainty but not for publication bias. It is shown to be a special case of the method: with alpha_prior = 1 and assurance = .80, the adjusted noncentrality parameter is the lower limit of the two-sided 60% confidence interval that safeguard power plans from, and in general a two-sided confidence level C corresponds to assurance = (1 + C)/2. For F and chi square statistics the correspondence is exact. The package help page and the assurance vignette explain how, and where the two differ (for the t tests and other effects on one degree of freedom, where the interval here bounds the size of the effect whatever its sign; for an unbalanced prior design, read as a balanced one; and for the Welch planner’s minimum): every difference makes the plan here the same as the safeguard plan or larger, never smaller (for the regression planners, within the frame random_predictors names). tests/testthat/test-safeguard-power.R pins the equivalence.
Every planner’s alpha_prior argument and Details say plainly what the value means: the significance level a prior result had to reach to be published, so the correction assumes you see only results with p below it. alpha_prior = 1 means no publication filter, since no p value exceeds 1, so there is no publication bias to correct and only the correction for uncertainty, set by assurance, remains. The help says that the value may also be written "No Publication Filter", and cites Anderson, Kelley, and Maxwell (2017, p. 1552), who note that the level can be set equal to 1 when publication bias is thought not to apply. The desired_power argument’s help says that it must exceed alpha_planned.
Source citations join the help pages of the designs added in 2.0.0, each in the sentence it supports: Welch (1947) and Satterthwaite (1946) for the Welch test and its degrees of freedom; Hotelling (1931) for T squared; Fisher (1928) for the sampling distribution of the multiple correlation coefficient, on the correlation page and where the regression pages describe sampled predictors; and Steiger, Shapiro, and Browne (1985) and Satorra and Saris (1985) for the asymptotic distribution of chi square difference statistics and the power of the likelihood ratio test. Maxwell, Delaney, and Kelley (2027) is cited in the text of every help page that lists it.
The regression planners’ help pages describe both predictor frames, what treating sampled predictors as fixed costs, and which frame plans the larger study (see “Sampled Predictors, Sphericity, and the Planning Effect Size” above). The regression and chi square difference pages also state where the planned sample size search starts (a total of p + 3, and the number of constraints plus 2), so no plan is smaller than that.
The package help page and the Getting Started vignette list all eighteen planners.

Internal

The noncentrality search, the truncated-likelihood construction, and the planned sample size search each live in a single internal helper (.solve_ncp_assurance, .tm_f/.tm_t, and .smallest_n_for_power), shared by every planner, so the numerical core is maintained in one place.
The zero-noncentrality and nonsignificance stops carry the condition classes bucss_zero_ncp and bucss_nonsignificant, so code that loops over plans (including plot() and ss_buc_sensitivity()) can catch a refusal without catching anything else. plot() now draws the plans of a prior result whose ceiling is below .5 instead of treating the warning for such an assurance as a refusal.
R’s warning that a noncentral t probability “may not have been computed to full precision”, which the root finder triggered with large observed t statistics, is muffled where it arises; the solved parameter agrees with independent quadrature in every such case examined.
The planned sample size search stops with a message rather than looping forever once the answer exceeds about 4.5e15 (2^52). Doubles hold every whole number exactly only up to 2^53, beyond which the bisection could not finish; answers below the bound are found exactly. An assurance a hair below the ceiling gets there, as does an implausibly large prior sample size or an alpha_planned too small to compute with, and the message names each. The random-predictor series (the correlation planner’s, and the regression planners’ with random_predictors = TRUE) is one internal function, summed over a window around its mode, so it stays exact for a strong correlation in a very large prior study (an r of .9 with an N in the hundreds of thousands), where a fixed number of terms would have dropped most of its mass.
Seeds are set through withr (>= 3.0.3), and R 3.6.0 is now the stated minimum, which the package’s delayed S3 registration and its dependencies already required.
The planned sample size search brackets by doubling and then bisects instead of stepping by one, so its cost is logarithmic in the answer. Requests whose planned sample size runs to the millions (an assurance just below the prior’s ceiling) now return instantly instead of iterating for minutes, and a huge answer can no longer overflow the integer step counter. Power is monotone in the sample size, so the returned value is the identical integer the incremental search produced (verified case for case on the developer characterization grid).
Factored the shared planning-input validation into a single internal helper, and moved the duplicated @references, planning-argument, and @return documentation into man-roxygen templates, so each is maintained in one place across every planner. The stats functions are imported individually rather than wholesale, an inst/WORDLIST curates the domain terms for the spell check enabled by Language: en-US, and a GitHub Actions workflow runs R CMD check across platforms.
Added a testthat (edition 3) test suite: regression oracles that pin the computed outputs, object-structure smoke tests, input-validation tests, coercion tests, and tests confirming each deprecated dotted name forwards to its ss_buc_* replacement. The new options have their own files (test-random-predictors.R, test-epsilon.R, test-effect-rows.R, and test-safeguard-power.R), which check each against an independent computation: the random-predictor mixture against quadrature and against the correlation planner, epsilon against the corrected-degrees-of-freedom power, the planning effect size against the ordinary power of the returned sample size, and safeguard power against confidence limits computed directly. test-no-publication-filter.R checks every planner at alpha_prior = 1 against the statistic’s ordinary distribution, computed in base R.