User Defined Functions (UDFs)
This page is auto-generated from XML doc comments in the VB files under the add-in udfs/ folder.
The pages below document the worksheet functions exposed by the add-in. Each group corresponds to the
Excel Function Wizard category (e.g. BESHStatNG - Nonparametric).
Groups
Function index
Agreement
BESH.AGREE.BLANDALTMAN_ALLOWABLE_BIAS— Assess Bland–Altman bias against allowable limits on the active analysis scale.BESH.AGREE.BLANDALTMAN_DECISION— Assess Bland–Altman bias and limits of agreement against allowable decision limits.BESH.AGREE.BLANDALTMAN_FIT— Bland–Altman analysis for two paired methods. Returns a labeled result table.BESH.AGREE.BLANDALTMAN_PLOTDATA— Bland–Altman plot data (observation and subject-mean coordinates).BESH.AGREE.BLANDALTMAN_STATS— Bland–Altman bias and limits of agreement for two paired methods.BESH.AGREE.DEMING_COEF— Weighted / generalized Deming regression coefficients and confidence intervals.BESH.AGREE.DEMING_FIT— Weighted / generalized Deming regression for two paired methods. Returns a labeled result table.BESH.AGREE.ICC_FIT— Intraclass correlation coefficient (ICC) result table for a selected ICC model.BESH.AGREE.ICC_RC— Repeatability coefficient (RC) and SEM for a selected ICC design.BESH.AGREE.ICC_VALUE— Point estimate of a selected intraclass correlation coefficient (ICC) model.BESH.AGREE.KAPPA_FIT— Cohen's / weighted kappa for two paired rating columns. Returns a labeled result table.BESH.AGREE.KAPPA_VALUE— Cohen's / weighted kappa value for two paired rating columns.BESH.AGREE.LINCCC_FIT— Lin's concordance correlation coefficient for two paired methods. Returns a labeled result table.BESH.AGREE.LINCCC_VALUE— Lin's concordance correlation coefficient value for two paired methods.BESH.AGREE.PASSINGBABLOK_COEF— Passing–Bablok regression coefficients and confidence intervals for two paired methods.BESH.AGREE.PASSINGBABLOK_FIT— Passing–Bablok regression for two paired methods. Returns a labeled result table.
See: Agreement UDFs
Assumptions
BESH.ASM.ANDERSON_DARLING— Anderson-Darling normality test for a single sample.BESH.ASM.BARTLETT— Bartlett test for homogeneity of variances across groups.BESH.ASM.BOX_M— Box's M test for equality of covariance matrices across groups.BESH.ASM.DAGOSTINO_PEARSON— D'Agostino-Pearson K² normality test for a single sample.BESH.ASM.FLIGNER_KILLEEN— Fligner-Killeen test for homogeneity of variances across groups.BESH.ASM.GRUBBS— Grubbs test for a single outlier in a univariate sample.BESH.ASM.LEVENE— Levene or Brown-Forsythe test for homogeneity of variances across groups.BESH.ASM.MAUCHLY— Mauchly's test of sphericity for repeated-measures data.BESH.ASM.ROSNER— Rosner generalized ESD test for multiple outliers in a univariate sample.BESH.ASM.SHAPIRO_WILK— Shapiro-Wilk normality test for a single sample.BESH.ASM.SQUARED_RANKS— Squared-ranks test for homogeneity of variances across groups.BESH.ASM.SYMMETRY— Symmetry test about an unknown median: MGG (default) or Cabilio-Masaro.
See: Assumptions UDFs
Contingency Tables
BESH.CT.CHI2— Pearson chi-square test of independence for an r×c contingency table.BESH.CT.FFH_EXACT— Fisher-Freeman-Halton exact test for a general r×c contingency table.BESH.CT.FISHER_2X2— Fisher's exact test for a 2×2 contingency table, including mid-p values.BESH.CT.MANTEL_HAENSZEL— Mantel-Haenszel pooled test and common odds ratio across stacked 2×2 strata.BESH.CT.MCNEMAR_EXACT— Exact paired 2×2 analysis: McNemar/Liddell p-value and matched-pairs odds-ratio interval.BESH.CT.NOMINAL_ASSOC— Cramér's V, Pearson's contingency coefficient, and Phi for an r×c table.BESH.CT.ODDS_RATIO— Odds ratio for a 2×2 table with Woolf and Cornfield confidence intervals.BESH.CT.ORDINAL_ASSOC— Ordinal association measures: Kendall tau-b, tau-c, gamma, and Somers' D.BESH.CT.PAIRED_PROPORTIONS— Estimate the difference between two paired proportions and return a confidence interval.BESH.CT.RISK_RATIO— Risk ratio (relative risk) for a 2×2 contingency table.BESH.CT.SINGLE_PROPORTION— Estimate a single proportion and return a Wilson score confidence interval.BESH.CT.TREND— Cochran-Armitage test for linear trend in proportions across ordered groups.BESH.CT.TWO_INDEPENDENT_PROPORTIONS— Estimate the difference between two independent proportions and return a confidence interval.BESH.CT.TWO_INDEPENDENT_PROPORTIONS_EQUIV— TOST-style equivalence comparison for two independent proportions with interval-based decision reporting.BESH.CT.TWO_INDEPENDENT_PROPORTIONS_NI— Non-inferiority comparison for two independent proportions with CI-based decision reporting.
Distributions
BESH.DIST.F_PDF— F distribution PDF (equivalent to F.DIST(x, df1, df2, FALSE)).BESH.DIST.PRTRNG— Studentized range CDF: returns P(0 ≤ Q ≤ q) for df=v and r groups (AS190).BESH.DIST.PRTRNG.TAIL— Studentized range upper-tail: returns P(Q > q) = 1 - BESH.DIST.PRTRNG(q,v,r).
See: Distributions UDFs
Multivariate Analysis
BESH.MULTI.CA_COLUMNS— Returns column-category overview statistics for a fitted correspondence-analysis model.BESH.MULTI.CA_COL_CONTRIB— Returns column contributions for each axis of a fitted correspondence-analysis model.BESH.MULTI.CA_COL_COORD— Returns the column principal-coordinate matrix for a fitted correspondence-analysis model.BESH.MULTI.CA_COL_COS2— Returns column cos² values for each axis of a fitted correspondence-analysis model.BESH.MULTI.CA_DROP— Removes a correspondence-analysis handle from memory.BESH.MULTI.CA_EIGEN— Returns inertia and explained-percentage summaries for a fitted correspondence-analysis model.BESH.MULTI.CA_FIT— Fits a simple correspondence-analysis model to a contingency table and returns a reusable handle.BESH.MULTI.CA_ROWS— Returns row-category overview statistics for a fitted correspondence-analysis model.BESH.MULTI.CA_ROW_CONTRIB— Returns row contributions for each axis of a fitted correspondence-analysis model.BESH.MULTI.CA_ROW_COORD— Returns the row principal-coordinate matrix for a fitted correspondence-analysis model.BESH.MULTI.CA_ROW_COS2— Returns row cos² values for each axis of a fitted correspondence-analysis model.BESH.MULTI.CA_SUMMARY— Returns a compact settings summary for a fitted correspondence-analysis model.BESH.MULTI.DA_CANONCOEF— Returns the canonical coefficient matrix for a fitted linear discriminant-analysis model.BESH.MULTI.DA_CANONICAL— Returns the canonical discriminant-functions summary for a fitted linear discriminant-analysis model.BESH.MULTI.DA_CASEWISE— Returns the casewise classification table for the training or validation pass.BESH.MULTI.DA_CENTROIDS— Returns the group centroids in canonical discriminant space for a fitted linear discriminant-analysis model.BESH.MULTI.DA_CONFUSION— Returns an observed-versus-predicted classification table for the training or validation pass.BESH.MULTI.DA_COVARIANCE— Returns the covariance matrix used by a fitted discriminant-analysis model.BESH.MULTI.DA_DROP— Removes a discriminant-analysis handle from memory.BESH.MULTI.DA_FIT— Fits a discriminant-analysis model and returns a reusable handle.BESH.MULTI.DA_FUNCTIONS— Returns the linear classification-function table for a fitted linear discriminant-analysis model.BESH.MULTI.DA_GROUPSUMMARY— Returns group counts, prior probabilities, and covariance diagnostics for a fitted discriminant-analysis model.BESH.MULTI.DA_MEANS— Returns the group mean table for a fitted discriminant-analysis model.BESH.MULTI.DA_PREDICT— Applies a fitted discriminant-analysis model to a new predictor matrix and returns casewise predictions.BESH.MULTI.DA_PREPROCESS— Returns the preprocessing constants used by a fitted discriminant-analysis model.BESH.MULTI.DA_REMOVED— Returns the rows removed by the missing-value policy before discriminant analysis was fitted.BESH.MULTI.DA_SUMMARY— Returns a compact settings and performance summary for a fitted discriminant-analysis model.BESH.MULTI.FA_COMMUNALITIES— Returns communalities and uniquenesses for a fitted factor-analysis model.BESH.MULTI.FA_DROP— Removes a factor-analysis handle from memory.BESH.MULTI.FA_EIGEN— Returns the initial and retained variance table for a fitted factor-analysis model.BESH.MULTI.FA_FACTORABILITY— Returns factorability diagnostics for a fitted factor-analysis model.BESH.MULTI.FA_FACTORCORR— Returns the factor-correlation matrix for a fitted factor-analysis model.BESH.MULTI.FA_FIT— Fits an exploratory factor-analysis model and returns a reusable handle.BESH.MULTI.FA_LOADINGS— Returns the rotated loading matrix for a fitted factor-analysis model.BESH.MULTI.FA_MATRIX— Returns the working covariance or correlation matrix for a fitted factor-analysis model.BESH.MULTI.FA_SCORES— Returns factor scores for the analyzed observations.BESH.MULTI.FA_STRUCTURE— Returns the strctre matrix for a fitted factor-analysis model.BESH.MULTI.FA_SUMMARY— Returns a compact settings and convergence summary for a fitted factor-analysis model.BESH.MULTI.HCLUST_AGGLOM— Returns the agglomeration schedule for a fitted hierarchical clustering model.BESH.MULTI.HCLUST_DROP— Removes a hierarchical clustering handle from memory.BESH.MULTI.HCLUST_FIT— Fits an agglomerative hierarchical clustering model and returns a reusable handle.BESH.MULTI.HCLUST_LEAFORDER— Returns the leaf order used to display the fitted hierarchical tree.BESH.MULTI.HCLUST_MEMBERSHIP— Returns a cluster-membership table obtained by cutting the fitted hierarchical tree.BESH.MULTI.HCLUST_PREPROCESS— Returns the preprocessing constants used by a fitted hierarchical clustering model.BESH.MULTI.HCLUST_REMOVED— Returns the rows removed by the missing-value policy before hierarchical clustering was fitted.BESH.MULTI.HCLUST_SUMMARY— Returns a compact settings and fit summary for a fitted hierarchical clustering model.BESH.MULTI.KMEANS_ASSIGNMENTS— Returns the active-observation assignment table for a fitted k-means model.BESH.MULTI.KMEANS_CENTERS— Returns the fitted k-means cluster centers.BESH.MULTI.KMEANS_DROP— Removes a k-means handle from memory.BESH.MULTI.KMEANS_FIT— Fits a k-means clustering model and returns a reusable handle.BESH.MULTI.KMEANS_PREPROCESS— Returns the preprocessing constants used by a fitted k-means model.BESH.MULTI.KMEANS_REMOVED— Returns the rows removed by the missing-value policy before k-means fitting.BESH.MULTI.KMEANS_SUMMARY— Returns a compact settings and fit summary for a fitted k-means model.BESH.MULTI.MCA_BURT— Returns the Burt table for a fitted multiple correspondence-analysis model.BESH.MULTI.MCA_CATEGORIES— Returns category-level overview statistics for a fitted multiple correspondence-analysis model.BESH.MULTI.MCA_CONTRIB— Returns category contributions for each axis of a fitted multiple correspondence-analysis model.BESH.MULTI.MCA_COORD— Returns category principal coordinates for a fitted multiple correspondence-analysis model.BESH.MULTI.MCA_COS2— Returns category cos² values for each axis of a fitted multiple correspondence-analysis model.BESH.MULTI.MCA_DROP— Removes a multiple correspondence-analysis handle from memory.BESH.MULTI.MCA_EIGEN— Returns inertia and explained-percentage summaries for a fitted multiple correspondence-analysis model.BESH.MULTI.MCA_FIT— Fits a multiple correspondence-analysis model to a categorical data matrix and returns a reusable handle.BESH.MULTI.MCA_INDICATOR— Returns the indicator (design) matrix used internally by a fitted multiple correspondence-analysis model.BESH.MULTI.MCA_SUMMARY— Returns a compact settings summary for a fitted multiple correspondence-analysis model.BESH.MULTI.PCA_DROP— Removes a principal component analysis handle from memory.BESH.MULTI.PCA_EIGEN— Returns eigenvalues and explained-variance summaries for a fitted principal component model.BESH.MULTI.PCA_FIT— Fits a principal component analysis model and returns a reusable handle.BESH.MULTI.PCA_LOADINGS— Returns the retained loading matrix for a fitted principal component model.BESH.MULTI.PCA_MATRIX— Returns the analyzed covariance or correlation matrix for a fitted principal component model.BESH.MULTI.PCA_SCORES— Returns principal-component scores for the analyzed observations.BESH.MULTI.PCA_SUMMARY— Returns a compact settings summary for a fitted principal component analysis model.
See: Multivariate Analysis UDFs
Nonparametric
BESH.NP.FRIEDMAN_MCP— Friedman post-hoc multiple comparisons: Dunn (default) or Conover.BESH.NP.FRIEDMAN_P— Friedman test p-value for repeated-measures/blocked designs (chi-square or F-approximation).BESH.NP.FRIEDMAN_STAT— Friedman test statistic for repeated-measures/blocked designs (T1 chi-square or T2 F-approximation).BESH.NP.KENDALL_P— P-value for Kendall rank correlation test (τb) for two paired samples.BESH.NP.KENDALL_TAU— Kendall rank correlation coefficient (τb) for two paired samples.BESH.NP.KW_MCP— Kruskal-Wallis post-hoc multiple comparisons (Dunn test).BESH.NP.KW_P— P-value for Kruskal-Wallis test (based on H or tie-corrected Hcor) for 2+ independent groups.BESH.NP.KW_STAT— Kruskal-Wallis test statistic H (or tie-corrected Hcor) for 2+ independent groups.BESH.NP.MW_P_EXACT— Mann–Whitney test: exact p-value (n ≤ 50). side: two/lower/upper.BESH.NP.MW_P_NORM— Mann–Whitney test: two-sided p-value (normal approximation with ties & continuity correction).BESH.NP.SPEARMAN_P— Two-sided p-value for Spearman rank correlation test (paired samples).BESH.NP.SPEARMAN_RHO— Spearman rank correlation coefficient (ρ) for two paired samples.BESH.NP.WILCOX_P_EXACT— Wilcoxon signed-rank test: exact p-value (paired samples; up to 60 non-zero diffs). side: two/lower/upper.BESH.NP.WILCOX_P_NORM— Wilcoxon signed-rank test: two-sided p-value (normal approximation; paired samples).
See: Nonparametric UDFs
Parametric
BESH.PAR.ANOVA1— One-way ANOVA table. Input: one column per group.BESH.PAR.ANOVA1_MCP— One-way ANOVA multiple comparisons: Tukey-Kramer, Games-Howell, Fisher LSD, or Bonferroni.BESH.PAR.ANOVA1_WELCH— Welch one-way ANOVA summary. Input: one column per group.BESH.PAR.ANOVA2_NESTED— Two-way nested ANOVA. Input: 3 columns = group, subgroup, response.BESH.PAR.RMANOVA1— One-way repeated-measures ANOVA table. Input: rows=subjects, cols=conditions.BESH.PAR.RMANOVA1_MCP— Repeated-measures ANOVA multiple comparisons: TukeyKramerRM2 (default) or Tukey assuming sphericity.BESH.PAR.TTEST_PAIRED— Paired t-test for two matched samples. Returns a labeled result table.BESH.PAR.TTEST_UNPAIRED— Two-sample unpaired t-test. Returns pooled, Welch, or both result tables.BESH.PAR.TTEST_UNPAIRED_EQUIV— TOST-style equivalence comparison for two independent means with interval-based decision reporting.BESH.PAR.TTEST_UNPAIRED_NI— Non-inferiority comparison for two independent means with CI-based decision reporting.
See: Parametric UDFs
Plot Data
BESH.PLOT.CALIB_POINTS— Calibration-bin points for plotting observed event rate vs. mean predicted probability.BESH.PLOT.HIST_BINS— Histogram bin midpoints and frequencies for one or more numeric series.BESH.PLOT.HIST_NORMAL— Normal-overlay coordinates matched to the GUI histogram frequency scale.BESH.PLOT.KM_CURVE— Step-ready Kaplan-Meier survival-curve coordinates with optional confidence limits.BESH.PLOT.ROC_POINTS— ROC thresholds and curve coordinates (false-positive rate vs. true-positive rate).BESH.PLOT.ROC_STATS— Numerical ROC summary: AUC, standard errors, confidence intervals, and p-value.
See: Plot Data UDFs
Regression Models
BESH.CLASS.BRIER— Returns the Brier score for observed binary outcomes and predicted probabilities.BESH.CLASS.CALIB— Returns calibration-plot data for observed binary outcomes and predicted probabilities.BESH.CLASS.CONFUSION— Returns a threshold-based confusion-matrix report for observed binary outcomes and predicted probabilities.BESH.CLASS.THRESH— Returns a threshold-performance table for observed binary outcomes and predicted probabilities.BESH.REGR.FORMULA_VALIDATE— Validates a regression-model formula string and returns TRUE or a descriptive validation message.BESH.REGR.GEE_BRIER— Returns the Brier score for a fitted binomial generalized estimating equation model.BESH.REGR.GEE_CALIB— Returns calibration-plot data for a fitted binomial generalized estimating equation model.BESH.REGR.GEE_CLASS— Returns a threshold-based classification report for a fitted binomial generalized estimating equation model.BESH.REGR.GEE_DROP— Removes a fitted generalized estimating equation handle from memory.BESH.REGR.GEE_FIT— Fits a generalized estimating equation model and returns a reusable handle.BESH.REGR.GEE_PRED— Returns predicted marginal means and linear predictors for new data under a fitted generalized estimating equation model.BESH.REGR.GEE_RESID— Returns residual diagnostics for a fitted generalized estimating equation handle.BESH.REGR.GEE_SUMMARY— Returns the coefficient summary table for a fitted generalized estimating equation handle.BESH.REGR.GEE_TESTS— Returns model-level diagnostics and fit statistics for a fitted generalized estimating equation handle.BESH.REGR.GEE_THRESH— Returns a threshold table for a fitted binomial generalized estimating equation model.BESH.REGR.GEE_VCOV— Returns the covariance matrix of the estimated generalized estimating equation coefficients.BESH.REGR.GEE_WCORR— Returns the fitted working correlation matrix for a generalized estimating equation handle.BESH.REGR.GLMNB_DROP— Removes a fitted Negative Binomial regression handle from memory.BESH.REGR.GLMNB_FIT— Fits a Negative Binomial regression model with estimated overdispersion and returns a reusable handle.BESH.REGR.GLMNB_PRED— Returns predicted means and linear predictors for new data under a fitted Negative Binomial regression model.BESH.REGR.GLMNB_RESID— Returns residual diagnostics for a fitted Negative Binomial regression handle.BESH.REGR.GLMNB_SUMMARY— Returns the coefficient summary table for a fitted Negative Binomial regression handle.BESH.REGR.GLMNB_TESTS— Returns model-level diagnostics and fit statistics for a fitted Negative Binomial regression handle.BESH.REGR.GLM_BRIER— Returns the Brier score for a fitted binomial generalized linear model.BESH.REGR.GLM_CALIB— Returns calibration-plot data for a fitted binomial generalized linear model.BESH.REGR.GLM_CLASS— Returns a threshold-based classification report for a fitted binomial generalized linear model.BESH.REGR.GLM_DROP— Removes a fitted generalized linear model handle from memory.BESH.REGR.GLM_FIT— Fits a generalized linear model and returns a reusable handle.BESH.REGR.GLM_PRED— Returns predicted responses and linear predictors for new data under a fitted generalized linear model.BESH.REGR.GLM_RESID— Returns residual diagnostics for a fitted generalized linear model handle.BESH.REGR.GLM_SUMMARY— Returns the coefficient summary table for a fitted generalized linear model handle.BESH.REGR.GLM_TESTS— Returns model-level diagnostics and fit statistics for a fitted generalized linear model handle.BESH.REGR.GLM_THRESH— Returns a threshold table for a fitted binomial generalized linear model.BESH.REGR.LM_ANOVA— Returns an overall, Type I, or Type III ANOVA table for a fitted linear-model handle.BESH.REGR.LM_DROP— Removes a fitted linear-model handle from memory.BESH.REGR.LM_FIT— Fits a Gaussian linear regression model and returns a reusable handle.BESH.REGR.LM_PRED— Returns predicted mean responses for new observations from a fitted linear-model handle.BESH.REGR.LM_RESID— Returns residual diagnostics for a fitted linear-model handle.BESH.REGR.LM_SUMMARY— Returns the coefficient summary table for a fitted linear-model handle.BESH.REGR.LM_TESTS— Returns model-level diagnostics and fit statistics for a fitted linear-model handle.BESH.REGR.LM_VIF— Returns the variance-inflation-factor and partial-correlation table for a fitted linear-model handle.BESH.REGR.MMRM_CLEAR_ALL— Drops all fitted MMRM handles from the session cache.BESH.REGR.MMRM_COEF— Returns the fixed-effect coefficient table for a fitted MMRM handle.BESH.REGR.MMRM_CONTRASTS— Returns observed-design-grid group contrasts for a fitted MMRM handle.BESH.REGR.MMRM_COVPARMS— Returns covariance-parameter estimates for a fitted MMRM handle.BESH.REGR.MMRM_DROP— Drops a fitted MMRM handle from the session cache.BESH.REGR.MMRM_FIT— Fits an MMRM and returns a reusable handle.BESH.REGR.MMRM_FITSTATS— Returns fit statistics for a fitted MMRM handle.BESH.REGR.MMRM_FITTED— Returns marginal fitted values for a fitted MMRM handle.BESH.REGR.MMRM_LSMEANS— Returns observed-design-grid LS-means for a fitted MMRM handle.BESH.REGR.MMRM_LSMESTIMATE— Returns custom SAS-style LS-mean estimates/contrasts for a fitted MMRM handle.BESH.REGR.MMRM_RESID— Returns fitted values and raw marginal residuals for a fitted MMRM handle.BESH.REGR.MMRM_RESULTS— Returns all MMRM result tables or one named table for a fitted MMRM handle.BESH.REGR.MMRM_R_CORR— Returns the fitted R-side correlation matrix for a fitted MMRM handle.BESH.REGR.MMRM_R_COV— Returns the fitted R-side covariance matrix for a fitted MMRM handle.BESH.REGR.MMRM_TYPE3— Returns the Kenward-Roger term-level F-test table for a fitted MMRM handle.BESH.REGR.MNLOGIT_CLASS— Returns the classification confusion matrix for a fitted multinomial-logit model handle.BESH.REGR.MNLOGIT_DROP— Removes a fitted multinomial-logit model handle from memory.BESH.REGR.MNLOGIT_FIT— Fits a baseline-category multinomial logistic regression model and returns a reusable handle.BESH.REGR.MNLOGIT_PRED— Returns fitted probabilities and predicted categories for new data under a fitted multinomial-logit model.BESH.REGR.MNLOGIT_RESID— Returns residual diagnostics for a fitted multinomial-logit model handle.BESH.REGR.MNLOGIT_SUMMARY— Returns the parameter summary table for a fitted multinomial-logit model handle.BESH.REGR.MNLOGIT_TESTS— Returns model-level diagnostics and tests for a fitted multinomial-logit model handle.BESH.REGR.ORDLOGIT_CLASS— Returns the classification confusion matrix for a fitted ordinal-logit model handle.BESH.REGR.ORDLOGIT_DROP— Removes a fitted ordinal-logit model handle from memory.BESH.REGR.ORDLOGIT_FIT— Fits a proportional-odds ordinal logistic regression model and returns a reusable handle.BESH.REGR.ORDLOGIT_PRED— Returns fitted probabilities and predicted categories for new data under a fitted ordinal-logit model.BESH.REGR.ORDLOGIT_RESID— Returns residual diagnostics for a fitted ordinal-logit model handle.BESH.REGR.ORDLOGIT_SUMMARY— Returns the parameter summary table for a fitted ordinal-logit model handle.BESH.REGR.ORDLOGIT_TESTS— Returns model-level diagnostics and tests for a fitted ordinal-logit model handle.BESH.REGR.ZIP_DROP— Removes a fitted Zero-Inflated Poisson model handle from memory.BESH.REGR.ZIP_FIT— Fits a Zero-Inflated Poisson regression model and returns a reusable handle.BESH.REGR.ZIP_PRED— Returns predicted means and component predictions for new data under a fitted Zero-Inflated Poisson model.BESH.REGR.ZIP_RESID— Returns residual diagnostics for a fitted Zero-Inflated Poisson model.BESH.REGR.ZIP_SUMMARY— Returns coefficient summaries for the count and/or zero component of a fitted Zero-Inflated Poisson model.BESH.REGR.ZIP_TESTS— Returns model-level diagnostics and fit statistics for a fitted Zero-Inflated Poisson model.
Sample Size
BESH.SSIZE.BLANDALTMAN— Required pairs for a Bland-Altman agreement study with a target limits-of-agreement precision.BESH.SSIZE.COX_BINARY— Required events for a Cox design with a binary covariate, with optional total-sample estimate.BESH.SSIZE.COX_CONTINUOUS— Required events for a Cox design with a continuous covariate, with optional total-sample estimate.BESH.SSIZE.ICC— Required subjects for a reliability study based on an intraclass-correlation target.BESH.SSIZE.LOGRANK— Required events and subjects for a two-group log-rank design.BESH.SSIZE.PROP_INDEP— Required group sizes for superiority, non-inferiority, or equivalence comparisons of two independent proportions.BESH.SSIZE.PROP_SINGLE— Required sample size for a one-sample two-sided proportion test.BESH.SSIZE.TTEST_PAIRED— Required number of pairs for a paired two-sided t-test.BESH.SSIZE.TTEST_UNPAIRED— Required group sizes for unpaired superiority, non-inferiority, or equivalence t-test planning.
See: Sample Size UDFs
Survival
BESH.SURV.COX_BASELINE— Returns baseline survival or cumulative hazard from a fitted Cox model.BESH.SURV.COX_DROP— Removes a fitted Cox model handle from memory.BESH.SURV.COX_FIT— Fits a Cox proportional hazards model and returns a handle for use with other COX_* functions.BESH.SURV.COX_PRED— Computes predictions from a fitted Cox model (linear predictor, risk, survival, or cumulative hazard).BESH.SURV.COX_RESID— Returns residual diagnostics for a fitted Cox model.BESH.SURV.COX_SUMMARY— Returns coefficient table (beta, SE, z, p, HR, CI) for a fitted Cox model handle.BESH.SURV.COX_TESTS— Returns global tests (LR, Wald) and fit statistics for a fitted Cox model handle.BESH.SURV.KM_TABLE— Kaplan-Meier tabular survival curve: group, time, at-risk, S(t), SE, lower/upper CI.BESH.SURV.LOGRANK_P— Log-rank family test p-value for comparing survival curves across groups (optionally stratified; supports multiple weight schemes).BESH.SURV.LOGRANK_STAT— Log-rank family test chi-square statistic for comparing survival curves across groups (optionally stratified; supports multiple weight schemes).BESH.SURV.MEDIAN_CI— Kaplan–Meier median survival time with Brookmeyer–Crowley CI (overall or by group). Returns a 2D table.
See: Survival UDFs