NEWS

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SimplyAgree 0.3.1

• Fixed error in the jamovi module (and any other use where the analysis is saved and reloaded) where plots failed with "object 'y' not found" (#76).
  • The data needed by the plot and check methods are now stored in the returned object itself, rather than as a formula that pointed back at the environment the analysis was run in.
• Fixed plot() and print() failing for agree_test(), agree_reps(), agree_nest(), and agree_np() when delta (or conf.level, agree.level, TOST, prop_bias) was supplied as an expression (e.g., delta = log(1.10)) or from inside another function (#50). The stored call now holds the evaluated values of these settings rather than the code as typed; the data arguments are still not copied into the object.
• Fixed tolerance_limit() prediction and tolerance limits when the model has a variance function (from condition or a user-supplied weights). The limits previously used sigma(model), which is only the residual SD at the reference level of the variance function, so they were too narrow for other conditions or covariate values. The residual SD is now evaluated for each row of the limits (funnily caught when reviewing another manuscript!).
  • limits gains an SD column with the residual SD used for each row.
  • If the variance depends on avg (e.g., weights = nlme::varPower(form = ~avg)), the limits are reported at the minimum, median, and maximum of avg, even when prop_bias = FALSE.
• The model returned by tolerance_limit() is now self-contained: its call stores the data, correlation structure, and variance function, and calls nlme::gls. update(), nlme::getData(), and emmeans() now work on it outside of the function (previously they failed with errors like "object 'var1' not found").
• Renamed the tol_method options of tolerance_limit(): "approx" is now "analytic" (the default) and "perc" is now "boot_cal". The old names still work but are deprecated.
• Fixed the parametric bootstrap used by tolerance_limit(tol_method = "boot_cal") (previously "perc"). Results for the bootstrap will change.
  • Replicates are now simulated from the point estimates of the fitted model. Previously the coefficients were also drawn from their sampling distribution before adding residual noise, which roughly doubled the sampling variance of the bootstrapped bias and made the tolerance limits too wide.
  • The simulated residuals now have the fitted covariance. Previously the within-subject correlation blocks were placed assuming each subject's rows were contiguous (so data not sorted by id got correlation between the wrong rows), and the Cholesky factor was applied in the wrong orientation.
  • The bootstrap is about 5 times faster: the covariance factorization is computed once, the bias and its standard error are computed directly from each refit instead of calling emmeans, and results are no longer accumulated with rbind().
  • MASS and Matrix are no longer imported.
• The tolerance limits from tolerance_limit() now have a clearly defined target, set by the new bound_type argument, and both tol_method options estimate the same thing. Tolerance limits (lower.TL/upper.TL) will change; prediction limits do not.
  • bound_type = "joint" (default): a beta-content, gamma-confidence tolerance interval (with confidence tol_level, at least pred_level of differences lie within the limits). The analytic limits use Howe's approximation (unchanged for independent data). Previously the bootstrap took quantiles of the bootstrapped prediction limits, which targeted a different quantity.
  • bound_type = "iu": a one-sided tol_level bound on each of the lower and upper (1 -/+ pred_level)/2 percentiles, for an intersection-union test against a maximal allowable difference. The analytic bounds use the exact noncentral t for independent data and the MOVER otherwise.
  • For clustered data, the analytic limits previously used the degrees of freedom of the bias for the residual variance, which was very conservative. The upper confidence bound for the residual SD now combines the between- and within-subject variance components with the MOVER under compound symmetry, and uses an effective degrees of freedom for other correlation structures.
  • "boot_cal" is a bootstrap calibration of the analytic limits: the analytic limits are computed in each replicate over a range of nominal levels, and the level at which the replicates reach the target confidence is used (returned as lower.TL.level/upper.TL.level). Calibrating the raw bootstrap limits instead was liberal with few subjects, because the estimated correlation is treated as known.
  • With condition and compound symmetry, the variance components of each condition are bounded using that condition's cluster sizes and degrees of freedom (using those of the whole data set was liberal, about 0.93 coverage for a 95% target).
  • In simulations (95% target, 10 to 20 subjects), the analytic limits had coverage of about 0.95 to 0.965 with compound symmetry (with or without condition) and AR(1) correlation. "boot_cal" is marked experimental: treating the estimated correlation as known, its coverage ranged from about 0.93 to 0.96 with 10 to 15 subjects, so the analytic limits are recommended.
  • limits gains SD.df and SD.upper columns, and the printed output states what the tolerance limits claim.
• Added a model argument to tolerance_limit(). model = "lme" fits a linear mixed model (nlme::lme) with a random intercept for each id (which is then required), instead of the default marginal gls model.
  • With compound symmetry and no variance function it gives the same fit and limits as "gls".
  • With cor_type = "ar1" or "car1", the serial correlation is added to the residuals on top of the random intercept, so a persistent subject effect is kept. In simulations with a random subject effect and AR(1) residuals, the joint tolerance limits had coverage of about 0.95 with "lme" versus about 0.89 with "gls" (95% target).
  • With condition or weights, the variance function applies to the residuals only, so the between-subject variance is common to all conditions.
  • The degrees of freedom for the bias are the containment degrees of freedom, because emmeans' Satterthwaite approximation can fail for lme models.
  • The printed output names the model that was fit.
  • With model = "lme", id can name two nested levels, outer level first (e.g., id = c("golfer", "club")), for nested random intercepts. The tolerance limits combine the three variance components (outer, inner, and residual) with the MOVER, and limits gains an SD.nested column. In simulations, using the inner level alone as id gave limits that were too narrow (about 0.88 to 0.91 coverage for a 95% target), while nested random intercepts had about 0.96.
• print() for tolerance_limit() results now reports the SD of the differences (coefficient of variation when log-transformed), as agreement_limit() does. If the SD differs between rows (e.g., by condition), it is shown as an SD column instead. Clustered models also print the between- and within-subject SDs.
• print() for agreement_limit(data_type = "reps") results now reports the within-subject SDs of x and y. They were computed (as variances) but never printed.
• Fixed the pooled within-subject variances in agreement_limit(data_type = "reps") for unbalanced data. A subject with only one measurement of a method (or none) was still counted in that method's degrees of freedom, which biased the variance (and so the SD of the differences and the LoA) low. A subject with a single measurement of one method also had its replicates of the other method dropped. Each method is now pooled over its own replicates (the one-way ANOVA residual mean square), and the MOVER and Bland-Altman confidence bounds use those degrees of freedom. Results for balanced data are unchanged.
• Fixed predict_varFunc() (used internally by tolerance_limit()) returning NA residual SDs for some groups when the data were a tibble.
• tolerance_limit() now reports the between- and within-subject components of the residual SD (SD.between, SD.within) under compound symmetry.
• Documented the model assumptions of tolerance_limit(): autoregressive correlation ("ar1", "car1") has no persistent subject effect and can understate the uncertainty in the bias; with compound symmetry, variance functions also scale the between-subject covariance; only one level of clustering is supported; and a proportional bias slope can arise from unequal measurement error variances alone (also noted for agreement_limit()).
• Fixed agreement_limit(data_type = "nest") counting rows with a missing measurement, and dropping subjects with a single measurement, when computing the number of measurements, the number of subjects, and the harmonic mean cluster size used for the confidence bounds of the limits of agreement. These now match the data used to fit the mixed model. Results change only when the data have missing measurements or subjects with a single measurement.
• Corrected the formulas for the limits of agreement in the "Agreement & Tolerance Limits" vignette to match the implementation (notably for data_type = "reps", which uses separate within-subject variances for x and y).
• Added a bound_type argument to agreement_limit(). The default, "iu", keeps the existing results: each confidence bound of the limits of agreement is a one-sided 1 - alpha bound (the outer end of a 1 - 2 * alpha interval), for an intersection-union test against a maximal allowable difference. "joint" uses 1 - alpha/2 per side (the outer ends of 1 - alpha intervals, the Bland-Altman convention), giving joint confidence for both limits. The printed output and plot caption now say which is shown.
• Added a conf_level argument to tolerance_limit() for the confidence interval of the bias (lower.CL/upper.CL), which was previously fixed at 95%. The level is shown in the printed output.
• Corrected the documentation of the error.ratio argument in dem_reg. The documentation previously described it as the ratio of error variances y/x, but dem_reg has always computed (and, with replicate data via id, estimated) the error ratio as var(x)/var(y), consistent with pb_reg. The documentation, the Deming vignette (including the coefficient-of-variation formula for the error ratio), and the jamovi module now consistently state var(x)/var(y). The computation is unchanged, so results are unaffected for users who supplied var(x)/var(y) or who estimated the ratio from replicates. Anyone who supplied a y/x ratio following the old documentation should invert it (use 1/error.ratio) and refit. Thanks to Hugo Lachuer for reporting this.
• Fixed plot(..., smooth_method = "gam") raising an error instead of an informative message when the suggested ggeffects package is not installed (only mgcv was being checked). The Deming vignette now also builds when the suggested deming package is unavailable.
• Added ccc_test function for concordance correlation coefficient hypothesis testing, returning an htest object.
• Fixed the Passing-Bablok (pb_reg) bootstrap. The previous wild bootstrap perturbed the observed values rather than the fitted values, so resampled points never crossed the regression line; standard errors were roughly 4-5 times too small and joint tests rejected a true null hypothesis 80-95% of the time in simulations. pb_reg now uses a nonparametric pairs (case) bootstrap by default.
• Added a se_method argument to pb_reg: "bootstrap" (pairs bootstrap, default), "jackknife" (delete-d jackknife with d = n/2; Shao & Wu, 1989), and "dufey" (experimental; analytic variance-covariance matrix for the scissors estimator; Dufey, 2020). Standard errors in the model table are now taken from the variance-covariance matrix when one is available.
• Fixed the tie correction in the analytic pb_reg slope confidence interval. The correction for tied x values was subtracted after taking the square root of the variance of Kendall's S rather than inside it, which made intervals too narrow when x contained ties (simulated coverage fell to 72% at n = 80 with rounded data; now ~95%). The same tie-corrected variance is now also used for ci_slopes.
• Fixed pb_reg reporting reject_h0 = TRUE when a confidence interval collapsed onto the null value (common with coarsely rounded data), due to floating-point error in tan(pi/4).
• Fixed joint_test and plot_joint to use the finite-sample F(2, n-2) reference distribution (Sadler, 2010) instead of the asymptotic chi-squared(2) approximation. The previous chi-squared approach was anti-conservative for small samples. A test_method argument allows selecting "F" (default) or "asymptotic" (old behavior).

SimplyAgree 0.3.0 (2026-01-21)

• Add enhanced support for Deming regression.
  • Updated dem_reg function to include options for confidence intervals and plotting.
• Added more power and sample size determination functions for limits of agreement.
• Added Passing-Bablok regression function (pb_reg) for method comparison studies.
• Updated methods for simple_eiv objects to be more extensive and act like other statistical models

SimplyAgree 0.2.2

• Small fixes to errors related to plotting and df for lmer based results.

SimplyAgree 0.2.1 (2025-02-24)

• Add sympercent options for log transformed results in tolerance_limit and agreement_limit
• Add argument to agreement_limit to allow of asymptotic confidence intervals to avoid errors related emmeans calculations
  • Only a problem for "big" data with > 1000 observations
• Small edit to reli_stats documentation to remove references to BCa confidence intervals

SimplyAgree 0.2.0 (2024-03-21)

• Add universal tolerance limits function: tolerance_limit
• Add universal agreement limits function: agreement_limit

SimplyAgree 0.1.5

• Add option to drop "CCC" calculation from agree_reps and agree_nest functions.

SimplyAgree 0.1.4

• Major error in SEP/SEE calculations. SD total calculation adjusted. See vignette("reliability_analysis",package ="SimplyAgree") for details.

SimplyAgree 0.1.3

• Added chisq type of confidence intervals for "Other" CIs for reli_stats and reli_aov

SimplyAgree 0.1.2 (2022-12-14)

• Fix to agree_np plot. (colors now align with order of LoA elements).
• Added reli_aov function for sums of squares approach.

SimplyAgree 0.1.1

• Minor cosmetic updates for jamovi submission.
• Minor cosmetic changes to plots (transparent backgrounds)

SimplyAgree 0.1.0 (2022-08-24)

• Updated CV calculations for reli_stats with the cv_calc argument.
  • Options now included CV for the model residuals, mean-squared error (MSE), or for the standard error of measurement (SEM).
  • Parametric bootstrap CI for all "other" statistics (CV, SEM, SEE, and SEP)
• Updated plots to include the delta argument on plot if specified, and estimate of LoA.
• Line-of-identity plot now includes trend line from Deming regression rather than OLS.
• Deming regression function (dem_reg) has been added to the package.
• Agreement coefficients (agree_coef), otherwise known as
• Added agreement coefficient function (agree_coef).
• Added loa_lme function
  • loa_mixed now deprecated
  • loa_lme allows for heterogenous variance by condition
  • New function is faster and relies on parametric bootstrap
• Assumptions checks are added for all "simple-agree" class results
  • Checks include: normality, heteroscedasticity, and proportional bias
• Assumptions checks are added for all "simple-reli" class results
  • Checks include: normality and heteroscedasticity
• All jamovi functions updated with new UI layout
• Big thanks go to Greg Atkinson and Ivan Jukic for the many suggestions leading to this update.

SimplyAgree 0.0.3 (2022-03-14)

• Fixed error in agree_nest and agree_reps; now properly handles missing values
• Remove dependencies on sjstats and cccrm packages

SimplyAgree 0.0.2 (2021-05-18)

• Fixes typos in jamovi/jmv functions
• Adds more descriptive errors to jamovi output
• Remove dontrun from examples in documentation
• Add more details and references to the package's functions