R Under development (unstable) (2026-02-10 r89394 ucrt) -- "Unsuffered Consequences" Copyright (C) 2026 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(evanverse) > > test_check("evanverse") x RDS file not found: 'D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file1392417ec6364.rds' -- Compiling Color Palettes (JSON -> RDS) -------------------------------------- v Added 'demo_palette' (Type: qualitative, 2 colors) v Saved RDS: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file1392414671ace.rds -- Compilation Summary -- i Sequential: 0 i Diverging: 0 i Qualitative: 1 v All palettes compiled successfully! -- Compiling Color Palettes (JSON -> RDS) -------------------------------------- ! Failed to parse JSON: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\palettes_139246f0665cb/qualitative/invalid.json v Saved RDS: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139247bad1b26.rds -- Compilation Summary -- i Sequential: 0 i Diverging: 0 i Qualitative: 0 v All palettes compiled successfully! -- Compiling Color Palettes (JSON -> RDS) -------------------------------------- ! Missing fields (colors) in: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\palettes_139245fc651cb/qualitative/incomplete.json v Saved RDS: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139244dec6eaa.rds -- Compilation Summary -- i Sequential: 0 i Diverging: 0 i Qualitative: 0 v All palettes compiled successfully! -- Compiling Color Palettes (JSON -> RDS) -------------------------------------- v Added 'seq_pal' (Type: sequential, 2 colors) v Added 'div_pal' (Type: diverging, 2 colors) v Saved RDS: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139243380c81.rds -- Compilation Summary -- i Sequential: 1 i Diverging: 1 i Qualitative: 0 v All palettes compiled successfully! i Converting symbols to UPPERCASE (human standard) i Creating standardized column: symbol_upper i Creating standardized column: symbol_lower i Converting symbols to UPPERCASE (human standard) i Converting symbols to UPPERCASE (human standard) i Directory created: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\palette_test_13924255c7358/qualitative v Palette saved: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\palette_test_13924255c7358/qualitative/testset.json i Directory created: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\reuse_test_1392456336305/diverging v Palette saved: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\reuse_test_1392456336305/diverging/reused.json ! Palette already exists: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\reuse_test_1392456336305/diverging/reused.json i Directory created: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\log_test_1392452f7638a/qualitative v Palette saved: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\log_test_1392452f7638a/qualitative/logtest.json Setting options('download.file.method.GEOquery'='auto') Setting options('GEOquery.inmemory.gpl'=FALSE) -- Directory Tree: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file1392433a16aab +-- test.txt -- Directory Tree: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139243a1282b +-- file1.txt +-- subdir +-- file2.txt -- Directory Tree: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file1392420654581 +-- level1 -- Directory Tree: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file1392420654581 +-- level1 +-- level2 +-- deep_file.txt -- Directory Tree: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139244ec74ba3 +-- example.txt v File tree log saved to: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139244ec74ba3\logs\tree\test.log -- Directory Tree: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139245bae7b53 +-- sample.txt v File tree log saved to: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139245bae7b53\logs\content_test.log -- Directory Tree: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139242ffb6e1 +-- test.txt v File tree log saved to: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139242ffb6e1\logs\file_tree_20260211_084448.log -- Directory Tree: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file13924b56b94 - +-- file1.txt v File tree log saved to: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file13924214d4ff9\overwrite_test.log -- Directory Tree: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file13924b56b94 - +-- file1.txt v File tree log saved to: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file13924214d4ff9\overwrite_test.log -- Directory Tree: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139246fcf11b3 +-- file1.txt v File tree log saved to: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139246d4f65ce\append_test.log -- Directory Tree: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139246fcf11b3 +-- file1.txt v File tree log saved to: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139246d4f65ce\append_test.log x Directory does not exist: d:\RCompile\CRANincoming\R-devel\evanverse.Rcheck\tests\testthat\nonexistent\directory\path -- Directory Tree: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139245aa01ca3 -- Directory Tree: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file13924374a2204 +-- test.txt v File tree log saved to: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file13924374a2204\new\log\path\test.log v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors i #FF8000 -> RGB: c(255, 128, 0) i #00FF00 -> RGB: c(0, 255, 0) i Mapping completed for 'id': 1 unmatched value(s) assigned to default. i Mapping completed for 'id': 1 unmatched value(s) assigned to default. i Mapping completed for 'group': 1 unmatched value(s) assigned to default. i Mapping completed for 'id': 1 unmatched value(s) assigned to default. -- Objects are NOT identical --------------------------------------------------- x Type mismatch: integer vs double x Class mismatch: integer vs numeric x Values differ at 1 position(s), e.g., index 3: 3 vs 4 -- Objects are NOT identical --------------------------------------------------- x Values differ at 1 position(s), e.g., index 2: b vs c -- Objects are NOT identical --------------------------------------------------- x Type mismatch: integer vs double x Values differ at 1 cell(s), e.g., [2,2]: 4 vs 5 -- Objects are NOT identical --------------------------------------------------- x Values differ in column 'y' at 1 row(s), e.g., row 2: b vs c -- Objects are NOT identical --------------------------------------------------- x Names differ: a, b vs b, a x Values differ at 2 position(s), e.g., index 1: 1 vs 2 -- Objects are NOT identical --------------------------------------------------- x Column names differ: x, y vs y, x -- Objects are NOT identical --------------------------------------------------- x Dimnames differ: (r1|r2; c1|c2) vs (r1|r2; cX|c2) -- Objects are NOT identical --------------------------------------------------- -- Objects are NOT identical --------------------------------------------------- -- Objects are NOT identical --------------------------------------------------- x Dimension mismatch: 0x0 vs 1x0 -- Objects are NOT identical --------------------------------------------------- x Length mismatch: 2 vs 3 -- Objects are NOT identical --------------------------------------------------- x Dimension mismatch: 2x2 vs 1x4 -- Objects are NOT identical --------------------------------------------------- x Column names differ: x vs y x Unsupported type for 'a': list x Unsupported type for 'b': function ! Large n (25) may cause numeric overflow. Consider using the 'gmp' package for arbitrary precision. ! Large n (171) may cause numeric overflow. Consider using the 'gmp' package for arbitrary precision. -- Configuring cran mirror -- v CRAN mirror set to: i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech" i View current settings: `getOption('repos')` & `getOption('BioC_mirror')` -- Configuring cran mirror -- v CRAN mirror set to: i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech" i View current settings: `getOption('repos')` & `getOption('BioC_mirror')` -- Configuring cran mirror -- v CRAN mirror set to: i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech" i View current settings: `getOption('repos')` & `getOption('BioC_mirror')` -- Configuring bioc mirror -- v Bioconductor mirror set to: i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju" i View current settings: `getOption('repos')` & `getOption('BioC_mirror')` -- Configuring bioc mirror -- v Bioconductor mirror set to: i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju" i View current settings: `getOption('repos')` & `getOption('BioC_mirror')` -- Configuring bioc mirror -- v Bioconductor mirror set to: i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju" i View current settings: `getOption('repos')` & `getOption('BioC_mirror')` -- Configuring all mirror -- v CRAN mirror set to: v Bioconductor mirror set to: i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech" i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju" i View current settings: `getOption('repos')` & `getOption('BioC_mirror')` -- Configuring all mirror -- v CRAN mirror set to: v Bioconductor mirror set to: i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech" i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju" i View current settings: `getOption('repos')` & `getOption('BioC_mirror')` -- Configuring cran mirror -- -- Configuring bioc mirror -- -- Configuring cran mirror -- v CRAN mirror set to: i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech" i View current settings: `getOption('repos')` & `getOption('BioC_mirror')` -- Configuring all mirror -- v CRAN mirror set to: v Bioconductor mirror set to: i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech" i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju" i View current settings: `getOption('repos')` & `getOption('BioC_mirror')` -- Configuring cran mirror -- i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech" i View current settings: `getOption('repos')` & `getOption('BioC_mirror')` -- Configuring bioc mirror -- i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju" i View current settings: `getOption('repos')` & `getOption('BioC_mirror')` -- Configuring cran mirror -- v CRAN mirror set to: i View current settings: `getOption('repos')` & `getOption('BioC_mirror')` -- Configuring cran mirror -- v CRAN mirror set to: i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech" -- Configuring cran mirror -- v CRAN mirror set to: i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech" i View current settings: `getOption('repos')` & `getOption('BioC_mirror')` -- Configuring bioc mirror -- -- Configuring all mirror -- -- Configuring cran mirror -- v CRAN mirror set to: i Available CRAN mirrors: "official", "rstudio", "tuna", "ustc", "aliyun", "sjtu", "pku", "hku", "westlake", "nju", and "sustech" i View current settings: `getOption('repos')` & `getOption('BioC_mirror')` -- Configuring bioc mirror -- v Bioconductor mirror set to: i Available Bioc mirrors: "official", "tuna", "ustc", "westlake", and "nju" i View current settings: `getOption('repos')` & `getOption('BioC_mirror')` v Installed: stats v Installed: cli v Installed: ggplot2 v Installed: stats v Installed: utils -- Package: stats -- i Matched exported names: 461 .MFclass .checkMFClasses .getXlevels .lm.fit .nknots.smspl .preformat.ts .vcov.aliased AIC ARMAacf ARMAtoMA BIC Box.test C D DF2formula Gamma HoltWinters IQR KalmanForecast KalmanLike KalmanRun KalmanSmooth NLSstAsymptotic NLSstClosestX NLSstLfAsymptote NLSstRtAsymptote PP.test Pair SSD SSasymp SSasympOff SSasympOrig SSbiexp SSfol SSfpl SSgompertz SSlogis SSmicmen SSweibull StructTS TukeyHSD acf acf2AR add.scope add1 addmargins aggregate aggregate.data.frame aggregate.ts alias anova ansari.test aov approx approxfun ar ar.burg ar.mle ar.ols ar.yw arima arima.sim arima0 arima0.diag as.dendrogram as.dist as.formula as.hclust as.stepfun as.ts asOneSidedFormula ave bandwidth.kernel bartlett.test binom.test binomial biplot bw.SJ bw.bcv bw.nrd bw.nrd0 bw.ucv cancor case.names ccf chisq.test cmdscale coef coefficients complete.cases confint confint.default confint.lm constrOptim contr.SAS contr.helmert contr.poly contr.sum contr.treatment contrasts contrasts<- convolve cooks.distance cophenetic cor cor.test cov cov.wt cov2cor covratio cpgram cutree cycle dbeta dbinom dcauchy dchisq decompose delete.response deltat dendrapply density density.default deriv deriv3 deviance dexp df df.kernel df.residual dfbeta dfbetas dffits dgamma dgeom dhyper diffinv dist dlnorm dlogis dmultinom dnbinom dnorm dpois drop.scope drop.terms drop1 dsignrank dt dummy.coef dummy.coef.lm dunif dweibull dwilcox ecdf eff.aovlist effects embed end estVar expand.model.frame extractAIC factanal factor.scope family fft filter fisher.test fitted fitted.values fivenum fligner.test formula frequency friedman.test ftable gaussian getCall getInitial get_all_vars glm glm.control glm.fit hasTsp hat hatvalues hclust heatmap influence influence.measures integrate interaction.plot inverse.gaussian is.empty.model is.leaf is.mts is.stepfun is.ts is.tskernel isoreg kernapply kernel kmeans knots kruskal.test ks.test ksmooth lag lag.plot line lm lm.fit lm.influence lm.wfit loadings loess loess.control loess.smooth logLik loglin lowess ls.diag ls.print lsfit mad mahalanobis make.link makeARIMA makepredictcall manova mantelhaen.test mauchly.test mcnemar.test median median.default medpolish model.extract model.frame model.frame.default model.matrix model.matrix.default model.matrix.lm model.offset model.response model.tables model.weights monthplot mood.test mvfft na.action na.contiguous na.exclude na.fail na.omit na.pass napredict naprint naresid nextn nlm nlminb nls nls.control nobs numericDeriv offset oneway.test optim optimHess optimise optimize order.dendrogram p.adjust p.adjust.methods pacf pairwise.prop.test pairwise.t.test pairwise.table pairwise.wilcox.test pbeta pbinom pbirthday pcauchy pchisq pexp pf pgamma pgeom phyper plclust plnorm plogis plot.ecdf plot.spec.coherency plot.spec.phase plot.stepfun plot.ts pnbinom pnorm poisson poisson.test poly polym power power.anova.test power.prop.test power.t.test ppoints ppois ppr prcomp predict predict.glm predict.lm preplot princomp printCoefmat profile proj promax prop.test prop.trend.test psignrank psmirnov pt ptukey punif pweibull pwilcox qbeta qbinom qbirthday qcauchy qchisq qexp qf qgamma qgeom qhyper qlnorm qlogis qnbinom qnorm qpois qqline qqnorm qqplot qr.influence qsignrank qsmirnov qt qtukey quade.test quantile quasi quasibinomial quasipoisson qunif qweibull qwilcox r2dtable rWishart rbeta rbinom rcauchy rchisq read.ftable rect.hclust reformulate relevel reorder replications reshape resid residuals residuals.glm residuals.lm rexp rf rgamma rgeom rhyper rlnorm rlogis rmultinom rnbinom rnorm rpois rsignrank rsmirnov rstandard rstudent rt runif runmed rweibull rwilcox scatter.smooth screeplot sd se.contrast selfStart setNames shapiro.test sigma simulate smooth smooth.spline smoothEnds sortedXyData spec.ar spec.pgram spec.taper spectrum spline splinefun splinefunH start stat.anova step stepfun stl summary.aov summary.glm summary.lm summary.manova summary.stepfun supsmu symnum t.test termplot terms terms.formula time toeplitz toeplitz2 ts ts.intersect ts.plot ts.union tsSmooth tsdiag tsp tsp<- uniroot update update.default update.formula var var.test variable.names varimax vcov weighted.mean weighted.residuals weights wilcox.test window window<- write.ftable xtabs -- Package: stats -- i Matched exported names: 461 .MFclass .checkMFClasses .getXlevels .lm.fit .nknots.smspl .preformat.ts .vcov.aliased AIC ARMAacf ARMAtoMA BIC Box.test C D DF2formula Gamma HoltWinters IQR KalmanForecast KalmanLike KalmanRun KalmanSmooth NLSstAsymptotic NLSstClosestX NLSstLfAsymptote NLSstRtAsymptote PP.test Pair SSD SSasymp SSasympOff SSasympOrig SSbiexp SSfol SSfpl SSgompertz SSlogis SSmicmen SSweibull StructTS TukeyHSD acf acf2AR add.scope add1 addmargins aggregate aggregate.data.frame aggregate.ts alias anova ansari.test aov approx approxfun ar ar.burg ar.mle ar.ols ar.yw arima arima.sim arima0 arima0.diag as.dendrogram as.dist as.formula as.hclust as.stepfun as.ts asOneSidedFormula ave bandwidth.kernel bartlett.test binom.test binomial biplot bw.SJ bw.bcv bw.nrd bw.nrd0 bw.ucv cancor case.names ccf chisq.test cmdscale coef coefficients complete.cases confint confint.default confint.lm constrOptim contr.SAS contr.helmert contr.poly contr.sum contr.treatment contrasts contrasts<- convolve cooks.distance cophenetic cor cor.test cov cov.wt cov2cor covratio cpgram cutree cycle dbeta dbinom dcauchy dchisq decompose delete.response deltat dendrapply density density.default deriv deriv3 deviance dexp df df.kernel df.residual dfbeta dfbetas dffits dgamma dgeom dhyper diffinv dist dlnorm dlogis dmultinom dnbinom dnorm dpois drop.scope drop.terms drop1 dsignrank dt dummy.coef dummy.coef.lm dunif dweibull dwilcox ecdf eff.aovlist effects embed end estVar expand.model.frame extractAIC factanal factor.scope family fft filter fisher.test fitted fitted.values fivenum fligner.test formula frequency friedman.test ftable gaussian getCall getInitial get_all_vars glm glm.control glm.fit hasTsp hat hatvalues hclust heatmap influence influence.measures integrate interaction.plot inverse.gaussian is.empty.model is.leaf is.mts is.stepfun is.ts is.tskernel isoreg kernapply kernel kmeans knots kruskal.test ks.test ksmooth lag lag.plot line lm lm.fit lm.influence lm.wfit loadings loess loess.control loess.smooth logLik loglin lowess ls.diag ls.print lsfit mad mahalanobis make.link makeARIMA makepredictcall manova mantelhaen.test mauchly.test mcnemar.test median median.default medpolish model.extract model.frame model.frame.default model.matrix model.matrix.default model.matrix.lm model.offset model.response model.tables model.weights monthplot mood.test mvfft na.action na.contiguous na.exclude na.fail na.omit na.pass napredict naprint naresid nextn nlm nlminb nls nls.control nobs numericDeriv offset oneway.test optim optimHess optimise optimize order.dendrogram p.adjust p.adjust.methods pacf pairwise.prop.test pairwise.t.test pairwise.table pairwise.wilcox.test pbeta pbinom pbirthday pcauchy pchisq pexp pf pgamma pgeom phyper plclust plnorm plogis plot.ecdf plot.spec.coherency plot.spec.phase plot.stepfun plot.ts pnbinom pnorm poisson poisson.test poly polym power power.anova.test power.prop.test power.t.test ppoints ppois ppr prcomp predict predict.glm predict.lm preplot princomp printCoefmat profile proj promax prop.test prop.trend.test psignrank psmirnov pt ptukey punif pweibull pwilcox qbeta qbinom qbirthday qcauchy qchisq qexp qf qgamma qgeom qhyper qlnorm qlogis qnbinom qnorm qpois qqline qqnorm qqplot qr.influence qsignrank qsmirnov qt qtukey quade.test quantile quasi quasibinomial quasipoisson qunif qweibull qwilcox r2dtable rWishart rbeta rbinom rcauchy rchisq read.ftable rect.hclust reformulate relevel reorder replications reshape resid residuals residuals.glm residuals.lm rexp rf rgamma rgeom rhyper rlnorm rlogis rmultinom rnbinom rnorm rpois rsignrank rsmirnov rstandard rstudent rt runif runmed rweibull rwilcox scatter.smooth screeplot sd se.contrast selfStart setNames shapiro.test sigma simulate smooth smooth.spline smoothEnds sortedXyData spec.ar spec.pgram spec.taper spectrum spline splinefun splinefunH start stat.anova step stepfun stl summary.aov summary.glm summary.lm summary.manova summary.stepfun supsmu symnum t.test termplot terms terms.formula time toeplitz toeplitz2 ts ts.intersect ts.plot ts.union tsSmooth tsdiag tsp tsp<- uniroot update update.default update.formula var var.test variable.names varimax vcov weighted.mean weighted.residuals weights wilcox.test window window<- write.ftable xtabs -- Package: stats -- i Matched exported names: 24 .lm.fit KalmanForecast KalmanLike KalmanRun KalmanSmooth confint.lm contr.helmert dummy.coef.lm glm glm.control glm.fit lm lm.fit lm.influence lm.wfit model.matrix.lm nlm nlminb predict.glm predict.lm residuals.glm residuals.lm summary.glm summary.lm -- Package: stats -- i Matched exported names: 24 .lm.fit KalmanForecast KalmanLike KalmanRun KalmanSmooth confint.lm contr.helmert dummy.coef.lm glm glm.control glm.fit lm lm.fit lm.influence lm.wfit model.matrix.lm nlm nlminb predict.glm predict.lm residuals.glm residuals.lm summary.glm summary.lm -- Package: stats -- i Matched exported names: 0 ! No exported names matched keyword: "zzzzzz" i s1 had 75 duplicates, now de-duplicated. i s2 had 94 duplicates, now de-duplicated. i s1 had 75 duplicates, now de-duplicated. i s2 had 94 duplicates, now de-duplicated. i a had 26 duplicates, now de-duplicated. i b had 22 duplicates, now de-duplicated. v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors ! Could not load palette "qual_bold". Using defaults. v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors -- Quick ANOVA Results -- i Method: anova v Significant group differences (p < 0.001) -- Descriptive Statistics -- Post-hoc Summary (tukey) v Loaded palette "qual_vivid" ("qualitative"), 9 colors -- Detailed Quick ANOVA Summary -- -- Omnibus Test i Effect sizes: eta_squared 0.448, omega_squared 0.430 -- Descriptive Statistics -- Normality Checks (Shapiro-Wilk) A: n = 25, p = 0.5812 B: n = 25, p = 0.8166 C: n = 25, p = 0.9928 i Decision: Small sample size (n < 30). Data appears normal (Shapiro p >= 0.05). Using t-test. -- Variance Equality (Levene) Levene's test: p = 0.9887 Equal variance: TRUE -- Post-hoc Comparisons (tukey) Analysis performed: 2026-02-11 08:45:05 v Loaded palette "qual_vivid" ("qualitative"), 9 colors -- Automatic Method Selection -- i Checking normality for each group... v Data appears normal (Shapiro-Wilk p >= 0.05). A: n = 25, p = 0.581 B: n = 25, p = 0.817 C: n = 25, p = 0.993 v Variances appear equal (Levene's test, p = 0.989) -- Omnibus Test -- v Completed classical one-way ANOVA (p = 0.0000) i Applied Tukey HSD post-hoc comparisons. v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors i var1 converted to factor with 1 level. i var2 converted to factor with 2 levels. i var2 converted to factor with 2 levels. ! Failed to load palette 'qual_vivid': Palette "qual_vivid" not found under "sequential", but exists under "qualitative". Try: `get_palette("qual_vivid", type = "qualitative")`. Using default colors. ! Removed 10 rows with missing values. i var1 converted to factor with 3 levels. i var2 converted to factor with 2 levels. ! Failed to load palette 'qual_vivid': Palette "qual_vivid" not found under "sequential", but exists under "qualitative". Try: `get_palette("qual_vivid", type = "qualitative")`. Using default colors. ! Pearson residuals not available. Using grouped bar chart. -- Data Preparation -- i Automatically selected 6 numeric columns. -- Computing Correlations -- i Found 1 significant correlation out of 10 tests -- Creating Heatmap -- v Analysis complete! -- Data Preparation -- i Automatically selected 5 numeric columns. -- Computing Correlations -- i Found 1 significant correlation out of 10 tests -- Creating Heatmap -- v Analysis complete! -- Data Preparation -- -- Data Preparation -- i Automatically selected 1 numeric column. -- Data Preparation -- -- Data Preparation -- -- Data Preparation -- i Automatically selected 3 numeric columns. -- Computing Correlations -- i Found 0 significant correlations out of 3 tests -- Creating Heatmap -- v Analysis complete! -- Quick Correlation Analysis Results -- i Method: pearson i Variables: 5 i Significant pairs: 1 -- Top 5 Significant Correlations Use `summary()` for detailed results. -- Detailed Correlation Analysis Summary -- -- Analysis Parameters Correlation method: pearson Missing value handling: pairwise.complete.obs P-value adjustment: none Number of variables: 5 -- Descriptive Statistics variable n mean sd median min max var1 50 10.068807 1.851740 9.854719 6.066766 14.33791 var2 50 15.439225 2.716342 15.457737 8.072493 21.56200 var3 50 18.984398 3.957336 18.780520 11.787011 28.40044 var4 50 25.194034 4.654875 24.616200 18.445992 41.20520 var5 50 8.046525 1.578258 8.027126 4.130347 12.53476 -- Correlation Summary Min correlation: -0.156 Max correlation: 0.81 Mean |correlation|: 0.144 -- Significant Correlations i Significant pairs are based on unadjusted p-values Significant pairs: 1 out of 10 tests All significant pairs: var1 var2 correlation p_value var1 var5 0.8100595 1.041235e-12 Analysis performed: 2026-02-11 08:45:17 -- Detailed Correlation Analysis Summary -- -- Analysis Parameters Correlation method: pearson Missing value handling: pairwise.complete.obs P-value adjustment: bonferroni Number of variables: 5 -- Descriptive Statistics variable n mean sd median min max var1 50 10.068807 1.851740 9.854719 6.066766 14.33791 var2 50 15.439225 2.716342 15.457737 8.072493 21.56200 var3 50 18.984398 3.957336 18.780520 11.787011 28.40044 var4 50 25.194034 4.654875 24.616200 18.445992 41.20520 var5 50 8.046525 1.578258 8.027126 4.130347 12.53476 -- Correlation Summary Min correlation: -0.156 Max correlation: 0.81 Mean |correlation|: 0.144 -- Significant Correlations i Significant pairs are based on adjusted p-values (method: bonferroni) Significant pairs: 1 out of 10 tests All significant pairs: var1 var2 correlation p_value var1 var5 0.8100595 1.041235e-11 Analysis performed: 2026-02-11 08:45:18 v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors ! Could not load palette "qual_bold". Using defaults. v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors -- Quick t-test Results -- i Method: t.test v Significant difference (p < 0.001) -- Descriptive Statistics Use `summary()` for detailed results. v Loaded palette "qual_vivid" ("qualitative"), 9 colors -- Detailed Quick t-test Summary -- -- Test Method Method used: t.test Paired: FALSE Alternative: two.sided Equal variance: TRUE -- Test Result -- Descriptive Statistics -- Normality Tests (Shapiro-Wilk) Control: n = 25, p = 0.5812 Treatment: n = 25, p = 0.8166 i Decision: Small sample size (n < 30). Data appears normal (Shapiro p >= 0.05). Using t-test. -- Variance Equality Test (Levene) Levene's test: p = 0.9863 Equal variances: TRUE Analysis performed: 2026-02-11 08:45:23 v Loaded palette "qual_vivid" ("qualitative"), 9 colors -- Automatic Method Selection -- i Checking normality for each group... v Data appears normal (Shapiro-Wilk p >= 0.05). Control: n = 25, p = 0.581 Treatment: n = 25, p = 0.817 v Variances appear equal (Levene's test, p = 0.986) v Using Student's t-test (equal variances assumed) -- Statistical Test -- v Significant difference detected (p < 0.001) -- Creating Visualization -- v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Analysis complete! v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors v Loaded palette "qual_vivid" ("qualitative"), 9 colors -- Reading Excel file -- i Sheets in 'D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139245692c88.xlsx': Sheet1 v Column names cleaned with janitor::clean_names(). v Successfully read sheet 1 from 'D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139245692c88.xlsx'. -- Reading Excel file -- i Sheets in 'D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139244ce75ffc.xlsx': Sheet1 v Column names cleaned with janitor::clean_names(). v Successfully read sheet 1 from 'D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139244ce75ffc.xlsx'. -- Reading Excel file -- i Sheets in 'D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139245ad0683f.xlsx': Sheet1 v Successfully read sheet 1 from 'D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139245ad0683f.xlsx'. -- Reading Excel file -- i Sheets in 'D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file1392426927e9e.xlsx': Sheet1 v Column names cleaned with janitor::clean_names(). v Successfully read sheet 1 from 'D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file1392426927e9e.xlsx'. Path: 'D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139245a7949ec.csv' Separator: "," | Encoding: "UTF-8" OpenMP version (_OPENMP) 201511 omp_get_num_procs() 48 R_DATATABLE_NUM_PROCS_PERCENT unset (default 50) R_DATATABLE_NUM_THREADS unset R_DATATABLE_THROTTLE unset (default 1024) omp_get_thread_limit() 2 omp_get_max_threads() 48 OMP_THREAD_LIMIT 2 OMP_NUM_THREADS unset RestoreAfterFork true data.table is using 2 threads with throttle==1024. See ?setDTthreads. freadR.c has been passed a filename: D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139245a7949ec.csv [01] Check arguments Using 2 threads (omp_get_max_threads()=48, nth=2) NAstrings = [<>] None of the NAstrings look like numbers. show progress = 1 0/1 column will be read as integer Y/N column will be read as character [02] Opening the file Opening file D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139245a7949ec.csv File opened, size = 241 bytes. Memory mapped ok [03] Detect and skip BOM [04] Arrange mmap to be \0 terminated \n has been found in the input (counts: 0 \r by themselves vs 6 [\r]*\n) and different lines can end with different line endings (e.g. mixed \n and \r\n in one file). This is common and ideal. [05] Skipping initial rows if needed Positioned on line 1 starting: <> [06] Detect separator, quoting rule, and ncolumns Using supplied sep ',' sep=',' with 6 lines of 11 fields using quote rule 0 Detected 11 columns on line 1. This line is either column names or first data row. Line starts as: <> Quote rule picked = 0 fill=false and the most number of columns found is 11 [07] Detect column types, dec, good nrow estimate and whether first row is column names 'header' changed by user from 'auto' to true sep=',' so dec set to '.' Number of sampling jump points = 1 because (239 bytes from row 1 to eof) / (2 * 239 jump0size) == 0 Type codes (jump 000) : 97779997777 Quote rule 0 All rows were sampled since file is small so we know nrow=5 exactly [08] Assign column names [09] Apply user overrides on column types After 0 type and 0 drop user overrides : 97779997777 [10] Allocate memory for the datatable Allocating 11 column slots (11 - 0 dropped) with 5 rows [11] Read the data jumps=[0..1), chunk_size=1048576, total_size=193 Read 5 rows x 11 columns from 241 bytes file in 00:00.001 wall clock time [12] Finalizing the datatable Type counts: 7 : int32 '7' 4 : float64 '9' ============================= 0.000s ( 33%) Memory map 0.000GiB file 0.000s ( 52%) sep=',' ncol=11 and header detection 0.000s ( 6%) Column type detection using 5 sample rows 0.000s ( 6%) Allocation of 5 rows x 11 cols (0.000GiB) of which 5 (100%) rows used 0.000s ( 4%) Reading 1 chunks (0 swept) of 1.000MiB (each chunk 5 rows) using 1 threads + 0.000s ( 0%) Parse to row-major thread buffers (grown 0 times) + 0.000s ( 0%) Transpose + 0.000s ( 4%) Waiting 0.000s ( 0%) Rereading 0 columns due to out-of-sample type exceptions 0.001s Total v File loaded successfully (5 rows x 11 cols) -- glimpse `glimpse(df)` from dplyr/tibble gives a compact overview. -- glimpse `glimpse(df)` from dplyr/tibble gives a compact overview. -- read_excel `readxl::read_excel("yourfile.xlsx")` reads Excel files. Supports `sheet =`, `range =`, etc. -- glimpse `glimpse(df)` from dplyr/tibble gives a compact overview. -- Usage Examples -------------------------------------------------------------- -- glimpse `glimpse(df)` from dplyr/tibble gives a compact overview. -- read_excel `readxl::read_excel("yourfile.xlsx")` reads Excel files. Supports `sheet =`, `range =`, etc. -- droplevels `droplevels(df)` removes unused factor levels from a data frame or factor. -- modifyList `modifyList(x, y)` merges two lists; elements in `y` overwrite those in `x`. -- do.call `do.call(fun, args)` calls a function with arguments in a list: `do.call(plot, list(x = 1:10))`. -- sprintf `sprintf("Hello, %s!", name)` formats strings with `%s`, `%d`, etc. -- scRNAseq `scRNAseq` (Bioconductor) provides scRNA-seq datasets, e.g., `ZeiselBrainData()`. -- basename `basename(path)` extracts the filename from a full path. See also `dirname()`. -- here `here::here("data", "raw", "sample1.rds")` builds a path from project root. -- stopifnot `stopifnot(cond1, cond2, ...)` throws if any condition is FALSE. -- object.size `object.size(x)` estimates memory size; use `format()` to pretty-print. -- slice `slice(df, 1:3)` selects rows by position; see `slice_head()`, `slice_tail()`, `slice_max()`. -- unzip `unzip("file.zip", exdir = "dir")` extracts ZIP archives. -- gunzip `R.utils::gunzip("file.csv.gz", remove = FALSE)` decompresses .gz files. -- untar `untar("file.tar.gz", exdir = "dir")` extracts .tar or .tar.gz archives. -- NoLegend `NoLegend()` removes legends from ggplot2/Seurat plots. -- RotatedAxis `RotatedAxis()` rotates x-axis text for readability in dot plots. -- guides `guides(fill = "none")` customizes or removes legends (with `scale_*`). -- log2 `log2(x)` base-2 logarithm (often for fold change). -- log `log(x, base = exp(1))` natural log by default; set `base = 10` or `2` for others. -- log10 `log10(x)` base-10 logarithm (orders of magnitude). -- round `round(x, digits = 0)` rounds; use `signif()` for significant digits. -- floor `floor(x)` greatest integer <= x (e.g., `floor(2.8)` -> 2). -- ceiling `ceiling(x)` smallest integer >= x (e.g., `ceiling(2.1)` -> 3). -- cut `cut(x, breaks)` bins numeric vector; `breaks = 3` or custom; `labels = FALSE` for group indices. -- cumsum `cumsum(x)` cumulative sum. -- cumprod `cumprod(x)` cumulative product. -- cummin `cummin(x)` running minimum. -- cummax `cummax(x)` running maximum. -- row_number `row_number(x)` order rank (ties broken arbitrarily). -- min_rank `min_rank(x)` ties get the same minimum rank. -- dense_rank `dense_rank(x)` like `min_rank()` but without gaps. -- percent_rank `percent_rank(x)` relative rank in [0,1], normalized by n-1. -- cume_dist `cume_dist(x)` cumulative proportion of values <= x. -- str_view `stringr::str_view(string, pattern)` highlights regex matches; `str_view_all()` for all. -- str_c `stringr::str_c(...)` concatenates; use `sep`/`collapse` as needed. -- str_glue `glue::glue("Hello, {name}!")` inline expressions with `{}`. -- str_flatten `stringr::str_flatten(x, collapse = ", ")` join a character vector. -- str_length `stringr::str_length(x)` string lengths. -- str_sub `stringr::str_sub(x, start, end)` extract/replace substrings (supports negative indices). -- today `lubridate::today()` current Date (no time). -- now `lubridate::now()` current POSIXct date-time. -- Sys.timezone `Sys.timezone()` system time zone name. -- skimr `skimr::skim(df)` compact, readable data summaries. -- par `par(mfrow = c(m, n))` split plotting area (e.g., 2x2). -- layout `layout(matrix, widths, heights)` flexible plot arrangement. -- datatable `DT::datatable(data)` interactive table (search/filter/sort/paginate). -- windowsFonts `windowsFonts()` register system fonts (Windows). -- sign `sign(x)` returns -1/0/1 for negative/zero/positive. -- reactable `reactable::reactable(data)` modern interactive table. -- trimws `trimws(x)` removes leading and trailing whitespace. -- cranlogs `cranlogs::cran_downloads('pkgname', from = 'last-month')` gets CRAN download stats; use `'last-week'`, `'last-day'`, or specific dates. -- dlstats `dlstats::cran_stats('pkgname')` shows CRAN download trends with plots; supports Bioconductor via `source = 'bioc'`. -- Available Keywords ---------------------------------------------------------- `glimpse, read_excel, droplevels, modifyList, do.call, sprintf, scRNAseq, basename, here, stopifnot, object.size, slice, unzip, gunzip, untar, NoLegend, RotatedAxis, guides, log2, log, log10, round, floor, ceiling, cut, cumsum, cumprod, cummin, cummax, row_number, min_rank, dense_rank, percent_rank, cume_dist, str_view, str_c, str_glue, str_flatten, str_length, str_sub, today, now, Sys.timezone, skimr, par, layout, datatable, windowsFonts, sign, reactable, trimws, cranlogs, dlstats` x No match found for keyword: "notakeyword" -- min_rank `min_rank(x)` ties get the same minimum rank. -- dense_rank `dense_rank(x)` like `min_rank()` but without gaps. -- percent_rank `percent_rank(x)` relative rank in [0,1], normalized by n-1. v Palette removed from qualitative: 'D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\palettes_test_1392436c22aa2/qualitative/test_palette.json' ! Palette not found in any type: nonexistent v Palette removed from diverging: 'D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\palettes_test_1392476d17536/diverging/test_palette.json' v Palette removed from qualitative: 'D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\custom_palettes_13924263917d/qualitative/custom_test.json' v RGB: c(255, 128, 0) -> HEX: #FF8000 v RGB: c(0, 0, 0) -> HEX: #000000 v RGB: c(255, 255, 255) -> HEX: #FFFFFF v Converted 3 RGB values to HEX. i RGB: c(255, 128, 0) -> HEX: #FF8000 i RGB: c(0, 255, 0) -> HEX: #00FF00 i RGB: c(0, 0, 255) -> HEX: #0000FF v RGB: c(0, 1, 255) -> HEX: #0001FF x An error occurred: non-numeric argument to mathematical function -- Statistical Power Analysis -------------------------------------------------- i Test: t.test (two.sample) i Effect size: 0.500 i Sample size: 30 (per group) i Significance level (alpha): 0.050 i Alternative: two.sided -- Result -- x Statistical Power: "47.79%" (Very Low) This study has only 47.8% power, meaning there is a 47.8% chance of detecting a true effect of size 0.50. This is considered very low power. i Recommendation: To achieve 80% power, increase sample size from 30 to 64 per group. -- Statistical Power Analysis -------------------------------------------------- i Test: t.test (two.sample) i Effect size: 0.500 i Sample size: 30 (per group) i Significance level (alpha): 0.050 i Alternative: two.sided -- Result -- x Statistical Power: "47.79%" (Very Low) This study has only 47.8% power, meaning there is a 47.8% chance of detecting a true effect of size 0.50. This is considered very low power. i Recommendation: To achieve 80% power, increase sample size from 30 to 64 per group. -- Statistical Power Analysis -------------------------------------------------- i Test: t.test (two.sample) i Effect size: 0.500 i Sample size: 30 (per group) i Significance level (alpha): 0.050 i Alternative: two.sided -- Result -- x Statistical Power: "47.79%" (Very Low) This study has only 47.8% power, meaning there is a 47.8% chance of detecting a true effect of size 0.50. This is considered very low power. i Recommendation: To achieve 80% power, increase sample size from 30 to 64 per group. -- Sample Size Estimation ------------------------------------------------------ i Test: t.test (two.sample) i Target power: 0.80 i Effect size: 0.500 i Significance level (alpha): 0.050 i Alternative: two.sided -- Result -- v Sample size per group: 64 v Total sample size: 128 To achieve 80% power for detecting an effect size of 0.50, you need 64 subjects per group (128 total). i Recommendation: Consider recruiting 10-20% more subjects to account for potential dropout, missing data, or protocol violations. -- Sample Size Estimation ------------------------------------------------------ i Test: t.test (two.sample) i Target power: 0.80 i Effect size: 0.500 i Significance level (alpha): 0.050 i Alternative: two.sided -- Result -- v Sample size per group: 64 v Total sample size: 128 To achieve 80% power for detecting an effect size of 0.50, you need 64 subjects per group (128 total). i Recommendation: Consider recruiting 10-20% more subjects to account for potential dropout, missing data, or protocol violations. -- Sample Size Estimation ------------------------------------------------------ i Test: t.test (two.sample) i Target power: 0.80 i Effect size: 0.500 i Significance level (alpha): 0.050 i Alternative: two.sided -- Result -- v Sample size per group: 64 v Total sample size: 128 To achieve 80% power for detecting an effect size of 0.50, you need 64 subjects per group (128 total). i Recommendation: Consider recruiting 10-20% more subjects to account for potential dropout, missing data, or protocol violations. i Square test started at 2026-02-11 08:45:34 v Square test completed in 0.000 seconds i Silent task started at 2026-02-11 08:45:34 v Silent task completed in 0.000 seconds v Excel file written to 'D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file1392487962c1.xlsx' v Excel file written to 'D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file1392415c8564c.xlsx' v Excel file written to 'D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139247f12197e.xlsx' ! File already exists and will be overwritten: 'D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139247f12197e.xlsx' v Excel file written to 'D:\temp\2026_02_11_08_40_16_6147\Rtmpqckwgq\file139247f12197e.xlsx' v Excel file written to 'D:/temp/2026_02_11_08_40_16_6147/Rtmpqckwgq/test-write-1392421c66712_2026-02-11.xlsx' [ FAIL 0 | WARN 0 | SKIP 68 | PASS 2138 ] ══ Skipped tests (68) ══════════════════════════════════════════════════════════ • On CRAN (68): 'test-download_batch.R:19:3', 'test-download_batch.R:39:3', 'test-download_gene_ref.R:66:3', 'test-download_gene_ref.R:109:3', 'test-download_gene_ref.R:150:3', 'test-download_gene_ref.R:174:3', 'test-download_gene_ref.R:198:3', 'test-download_gene_ref.R:218:3', 'test-download_gene_ref.R:237:3', 'test-download_gene_ref.R:264:3', 'test-download_gene_ref.R:292:3', 'test-download_gene_ref.R:320:3', 'test-download_gene_ref.R:339:3', 'test-download_gene_ref.R:359:3', 'test-download_url.R:206:3', 'test-download_url.R:238:3', 'test-download_url.R:266:3', 'test-download_url.R:296:3', 'test-download_url.R:319:3', 'test-download_url.R:342:3', 'test-download_url.R:365:3', 'test-file_info.R:12:3', 'test-file_info.R:26:3', 'test-file_info.R:48:3', 'test-file_info.R:59:3', 'test-file_info.R:71:3', 'test-pkg.R:389:3', 'test-pkg.R:528:3', 'test-pkg.R:536:3', 'test-pkg.R:546:3', 'test-pkg.R:561:3', 'test-plot_forest.R:61:3', 'test-plot_forest.R:77:3', 'test-plot_forest.R:96:3', 'test-plot_forest.R:113:3', 'test-plot_forest.R:134:3', 'test-plot_forest.R:149:3', 'test-plot_forest.R:164:3', 'test-plot_forest.R:183:3', 'test-plot_forest.R:200:3', 'test-plot_forest.R:217:3', 'test-plot_forest.R:235:3', 'test-plot_forest.R:253:3', 'test-plot_forest.R:275:3', 'test-plot_forest.R:292:3', 'test-plot_forest.R:310:3', 'test-plot_forest.R:327:3', 'test-plot_forest.R:350:3', 'test-plot_forest.R:368:3', 'test-plot_forest.R:389:3', 'test-plot_forest.R:410:3', 'test-plot_forest.R:427:3', 'test-plot_forest.R:444:3', 'test-plot_forest.R:475:3', 'test-plot_forest.R:493:3', 'test-plot_forest.R:515:3', 'test-plot_forest.R:532:3', 'test-plot_forest.R:553:3', 'test-plot_forest.R:570:3', 'test-plot_forest.R:594:3', 'test-plot_forest.R:611:3', 'test-plot_forest.R:632:3', 'test-plot_forest.R:662:3', 'test-plot_forest.R:696:3', 'test-plot_forest.R:730:3', 'test-plot_forest.R:756:3', 'test-plot_forest.R:772:3', 'test-plot_forest.R:789:3' [ FAIL 0 | WARN 0 | SKIP 68 | PASS 2138 ] > > proc.time() user system elapsed 60.79 3.54 64.35