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FerreiraAS · Jun 4, 2024 · 31cea17 · 31cea17
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diff --git a/Ciencia-com-R.docx b/Ciencia-com-R.docx
diff --git a/Ciencia-com-R.log b/Ciencia-com-R.log
diff --git a/Ciencia-com-R_files/figure-html/evento-dado-1.png b/Ciencia-com-R_files/figure-html/evento-dado-1.png
diff --git a/Ciencia-com-R_files/figure-html/regressao-media-plot-1.png b/Ciencia-com-R_files/figure-html/regressao-media-plot-1.png
diff --git a/Ciencia-com-R_files/figure-html/teorema-central-limite-continua-plot-1.png b/Ciencia-com-R_files/figure-html/teorema-central-limite-continua-plot-1.png
diff --git a/Ciencia-com-R_files/figure-html/teorema-central-limite-dado-plot-1.png b/Ciencia-com-R_files/figure-html/teorema-central-limite-dado-plot-1.png
diff --git a/Ciencia-com-R_files/figure-html/teorema-central-limite-moeda-plot-1.png b/Ciencia-com-R_files/figure-html/teorema-central-limite-moeda-plot-1.png
diff --git a/R/Descricao/extracolumn-N.R b/R/Descricao/extracolumn-N.R
@@ -11,11 +11,11 @@ N <- function(x, ...) {
         if (nlevels(g) > 2) {
             # For numeric variables with 3 or more groups
             model <- aov(g ~ y)
-            eta2 <- eta_squared(model, partial = TRUE)
+            eta2 <- rstatix::eta_squared(model, partial = TRUE)
             if (!is.na(eta2)) {
-                es <- eta2_to_f(eta2)
-                N <- pwr.anova.test(k = nlevels(g), f = eta2_to_f(es), sig.level = 0.05,
-                  power = 0.8)
+                es <- effectsize::eta2_to_f(eta2)
+                N <- pwr::pwr.anova.test(k = nlevels(g), f = effectsize::eta2_to_f(es),
+                  sig.level = 0.05, power = 0.8)
             }
         } else {
             # For numeric variables with 2 groups
@@ -27,15 +27,15 @@ N <- function(x, ...) {
             d <- (mu.1 - mu.2)/pool.sd
             if (!is.na(d)) {
                 es <- d
-                N <- ceiling(pwr.t.test(d = es, sig.level = 0.05, power = 1 - 0.2,
-                  type = "two.sample")$n)
+                N <- ceiling(pwr::pwr.t.test(d = es, sig.level = 0.05, power = 1 -
+                  0.2, type = "two.sample")$n)
             }
         }
     } else {
         # For categorical variables
         w <- chisq.test(table(y, g), correct = TRUE)
         es <- w$statistic
-        N <- pwr.chisq.test(w = w$statistic, df = w$parameter, sig.level = 0.05,
+        N <- pwr::pwr.chisq.test(w = w$statistic, df = w$parameter, sig.level = 0.05,
             power = 0.8)
     }
     # Format the ES value, using an HTML entity.  The initial empty string

diff --git a/R/Descricao/extracolumn-es.R b/R/Descricao/extracolumn-es.R
@@ -11,11 +11,11 @@ es <- function(x, ...) {
         if (nlevels(g) > 2) {
             # For numeric variables with 3 or more groups
             model <- aov(as.character(g) ~ y)
-            eta2 <- eta_squared(model, partial = TRUE)$Eta2
+            eta2 <- effectsize::eta_squared(model, partial = TRUE)$Eta2
             if (!is.na(eta2)) {
-                es <- eta2_to_f(eta2)
-                N <- ceiling(pwr.anova.test(k = nlevels(g), f = eta2_to_f(es), sig.level = 0.05,
-                  power = 0.8)$n)
+                es <- effectsize::eta2_to_f(eta2)
+                N <- ceiling(pwr::pwr.anova.test(k = nlevels(g), f = effectsize::eta2_to_f(es),
+                  sig.level = 0.05, power = 0.8)$n)
             }
         } else {
             # For numeric variables with 2 groups
@@ -27,15 +27,15 @@ es <- function(x, ...) {
             d <- (mu.1 - mu.2)/pool.sd
             if (!is.na(d)) {
                 es <- d
-                N <- ceiling(pwr.t.test(d = es, sig.level = 0.05, power = 1 - 0.2,
-                  type = "two.sample")$n)
+                N <- ceiling(pwr::pwr.t.test(d = es, sig.level = 0.05, power = 1 -
+                  0.2, type = "two.sample")$n)
             }
         }
     } else {
         # For categorical variables
         w <- chisq.test(table(y, g), correct = TRUE)
         es <- sqrt(as.numeric(w$statistic)/as.numeric(sum(w$observed)) * as.numeric(w$parameter))
-        N <- ceiling(pwr.chisq.test(w = es, N = NULL, df = w$parameter, sig.level = 0.05,
+        N <- ceiling(pwr::pwr.chisq.test(w = es, N = NULL, df = w$parameter, sig.level = 0.05,
             power = 0.8)$N)
     }
     # Format the ES value, using an HTML entity.  The initial empty string

diff --git a/R/Descricao/pilotdata_gopal.R b/R/Descricao/pilotdata_gopal.R
@@ -23,25 +23,26 @@ source("extracolumn-N.R")
 dataset <- data.frame(read_excel("pilotdata_gopal.xlsx", sheet = 1))
 
 # COMPARATIVE ANALYSIS
-table1(~Sex + Age + Height_cm + Weight_kg + BMI_kg.m. + Smoker + FVC + FEV1 + FEV1.FVC +
-    MIP + MEP | Group, data = dataset, extra.col = list(`P-value` = pvalue, `Effect size (d, f, V)` = es))
+table1::table1(~Sex + Age + Height_cm + Weight_kg + BMI_kg.m. + Smoker + FVC + FEV1 +
+    FEV1.FVC + MIP + MEP | Group, data = dataset, extra.col = list(`P-value` = pvalue,
+    `Effect size (d, f, V)` = es))
 
 # COMPARATIVE ANALYSIS
-table1(~Both.Leg...Eyes.Opened..Overall.balance.index...Two.Leg.. + Both.Leg...Eyes.Opened..Overall.balance.index...SD.. +
+table1::table1(~Both.Leg...Eyes.Opened..Overall.balance.index...Two.Leg.. + Both.Leg...Eyes.Opened..Overall.balance.index...SD.. +
     Both.Leg...Eyes.Opened..ANT.POST.balance.index...Two.Leg. + Both.Leg...Eyes.Opened..ANT.POST.balance.index...SD. +
     Both.Leg...Eyes.Opened..MED.LAT..balance.index..Two.Leg. + Both.Leg...Eyes.Opened..MED.LAT..balance.index..SD. |
     Group, data = dataset, extra.col = list(`P-value` = pvalue, `Effect size<br>(d, f, X)` = es,
     `Sample size<br>(per group)` = N))
 
 # COMPARATIVE ANALYSIS
-table1(~Both.Leg...Eyes.closed..Overall.balance.index..Two.leg. + Both.Leg...Eyes.closed..Overall.balance.index..SD. +
+table1::table1(~Both.Leg...Eyes.closed..Overall.balance.index..Two.leg. + Both.Leg...Eyes.closed..Overall.balance.index..SD. +
     Both.Leg...Eyes.Closed..ANT.POST.balance.index..two.leg. + Both.Leg...Eyes.Closed..ANT.POST.balance.index..SD. +
     Both.Leg...Eyes.Closed..MED.LAT..balance.index...two.Leg. + Both.Leg...Eyes.Closed..MED.LAT..balance.index...SD. |
     Group, data = dataset, extra.col = list(`P-value` = pvalue, `Effect size<br>(d, f, X)` = es,
     `Sample size<br>(per group)` = N))
 
 # COMPARATIVE ANALYSIS
-table1(~Sagittal.Imbalance.VP.DM.R. + Sagittal.Imbalance.VP.DM.L. + Coronal.Imbalance.VP.DM.mm.R +
+table1::table1(~Sagittal.Imbalance.VP.DM.R. + Sagittal.Imbalance.VP.DM.L. + Coronal.Imbalance.VP.DM.mm.R +
     Coronal.Imbalance.VP.DM.mm.L + Pelvic.Obliquity.mm.R + Pelvic.Obliquity.mm.L +
     Pelvic.Obliquity.mm + Pelvic.Torsion.DL.DR..R. + Pelvic.Torsion.DL.DR.L. + Pelvic.Torsion.DL.DR.. +
     Pelvic.Rotation.. + Pelvic.Rotation.R. + Pelvic.Rotation.L. + Kyphotic.Angle.ICT.ITL..MAX.... +

diff --git a/R/Desempenho diagnostico/diag-stats.R b/R/Desempenho diagnostico/diag-stats.R
@@ -67,16 +67,16 @@ diag.stats <- function(new.test, reference.test, new.lab, ref.lab, adjustment, c
     youden <- sen + spe - 1
 
     # Clopper-Pearson exact confidence intervals
-    sen.ci <- exactci(TP, (TP + FN), conf.level)
-    spe.ci <- exactci(TN, (FP + TN), conf.level)
-    ppv.ci <- exactci(TP, (TP + FP), conf.level)
-    npv.ci <- exactci(TN, (FN + TN), conf.level)
-    acc.ci <- exactci((TP + TN), (TP + FN + FP + TN), conf.level)
-    plr.ci <- riskscoreci(TP, (TP + FN), FP, (FP + TN), conf.level)
-    nlr.ci <- riskscoreci(FN, (TP + FN), TN, (FP + TN), conf.level)
-    dor.ci <- oddsratioci.mp((FP * FN), (TP * TN), conf.level)
+    sen.ci <- PropCIs::exactci(TP, (TP + FN), conf.level)
+    spe.ci <- PropCIs::exactci(TN, (FP + TN), conf.level)
+    ppv.ci <- PropCIs::exactci(TP, (TP + FP), conf.level)
+    npv.ci <- PropCIs::exactci(TN, (FN + TN), conf.level)
+    acc.ci <- PropCIs::exactci((TP + TN), (TP + FN + FP + TN), conf.level)
+    plr.ci <- PropCIs::riskscoreci(TP, (TP + FN), FP, (FP + TN), conf.level)
+    nlr.ci <- PropCIs::riskscoreci(FN, (TP + FN), TN, (FP + TN), conf.level)
+    dor.ci <- PropCIs::oddsratioci.mp((FP * FN), (TP * TN), conf.level)
     # DeLong confidence interval
-    auc.ci <- ci.auc(new.test, reference.test, conf.level = conf.level)
+    auc.ci <- pROC::ci.auc(new.test, reference.test, conf.level = conf.level)
     # Wallis confidence interval
     p1 <- sen
     p2 <- spe

diff --git a/R/Ensaio experimental aleatorizado/RCT-Figure1.R b/R/Ensaio experimental aleatorizado/RCT-Figure1.R
@@ -87,27 +87,27 @@ FIGURE.1 <- function(dataset, variables, covariate, bw.factor, wt.labels, missin
     # fit linear mixed model
     if (missing != "multiple.imputation") {
         if (!is.null(covariate)) {
-            mod1 <- lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M + COVARIATE_M, random = ~1 |
-                ID_M/TIME_M, data = data_M)
+            mod1 <- lme4::lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M + COVARIATE_M,
+                random = ~1 | ID_M/TIME_M, data = data_M)
         } else {
-            mod1 <- lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M, random = ~1 | ID_M/TIME_M,
-                data = data_M)
+            mod1 <- lme4::lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M, random = ~1 |
+                ID_M/TIME_M, data = data_M)
         }
         mod1.aov <- anova(mod1)
     } else {
-        ini <- mice(data = data_M, maxit = 0)
+        ini <- mice::mice(data = data_M, maxit = 0)
         pred <- ini$pred
         pred["OUTCOME_M", "ID_M"] <- -2
-        imp <- mice(data_M, pred = pred, method = "2l.pan", m = m.imputations, seed = 0,
-            print = FALSE)
+        imp <- mice::mice(data_M, pred = pred, method = "2l.pan", m = m.imputations,
+            seed = 0, print = FALSE)
         if (!is.null(covariate)) {
-            mod1 <- with(data = imp, lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M + COVARIATE_M,
-                random = ~1 | ID_M/TIME_M))
-            mod1.aov <- quiet(mi.anova(imp, formula = "OUTCOME_M ~ TIME_M * GROUP_M + COVARIATE_M"))
+            mod1 <- with(data = imp, lme4::lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M +
+                COVARIATE_M, random = ~1 | ID_M/TIME_M))
+            mod1.aov <- quiet(miceadds::mi.anova(imp, formula = "OUTCOME_M ~ TIME_M * GROUP_M + COVARIATE_M"))
         } else {
-            mod1 <- with(data = imp, lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M, random = ~1 |
-                ID_M/TIME_M))
-            mod1.aov <- quiet(mi.anova(imp, formula = "OUTCOME_M ~ TIME_M * GROUP_M"))
+            mod1 <- with(data = imp, lme4::lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M,
+                random = ~1 | ID_M/TIME_M))
+            mod1.aov <- quiet(miceadds::mi.anova(imp, formula = "OUTCOME_M ~ TIME_M * GROUP_M"))
         }
         mod1.aov <- mod1.aov$anova.table[1:3, 2:5]
         mod1.aov <- rbind(rep(NA, 4), mod1.aov)
@@ -133,7 +133,7 @@ FIGURE.1 <- function(dataset, variables, covariate, bw.factor, wt.labels, missin
         "*\",\"*", " ~ p", p.value))
 
     # calculate CI
-    myCI <- group.CI(OUTCOME_M ~ GROUP_M * as.factor(TIME_M), data = cbind(GROUP_M,
+    myCI <- Rmisc::group.CI(OUTCOME_M ~ GROUP_M * as.factor(TIME_M), data = cbind(GROUP_M,
         as.factor(TIME_M), OUTCOME_M), ci = 1 - alpha)
     myCI[, 2] <- rep(wt.labels, each = nlevels(bw.factor))
 

diff --git a/R/Ensaio experimental aleatorizado/RCT-Missingness.R b/R/Ensaio experimental aleatorizado/RCT-Missingness.R
@@ -14,7 +14,7 @@ missing.data <- function(dataset, variables, covariate, digits = 3, p.digits = 3
         dataset <- cbind(dataset, covariate)
     }
 
-    test.res <- na.test(dataset, digits = digits, p.digits = p.digits, as.na = as.na,
+    test.res <- misty::na.test(dataset, digits = digits, p.digits = p.digits, as.na = as.na,
         check = check, output = output)
 
     # output results

diff --git a/R/Ensaio experimental aleatorizado/RCT-Table2a.R b/R/Ensaio experimental aleatorizado/RCT-Table2a.R
@@ -124,27 +124,27 @@ TABLE.2a <- function(dataset, variables, covariate, bw.factor, control.g, wt.lab
     # fit linear mixed model
     if (missing != "multiple.imputation") {
         if (!is.null(covariate)) {
-            mod1 <- lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M + COVARIATE_M, random = ~1 |
-                ID_M/TIME_M, data = data_M)
+            mod1 <- lme4::lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M + COVARIATE_M,
+                random = ~1 | ID_M/TIME_M, data = data_M)
         } else {
-            mod1 <- lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M, random = ~1 | ID_M/TIME_M,
-                data = data_M)
+            mod1 <- lme4::lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M, random = ~1 |
+                ID_M/TIME_M, data = data_M)
         }
         mod1.aov <- anova(mod1)
     } else {
-        ini <- mice(data = data_M, maxit = 0)
+        ini <- mice::mice(data = data_M, maxit = 0)
         pred <- ini$pred
         pred["OUTCOME_M", "ID_M"] <- -2
-        imp <- mice(data_M, pred = pred, method = "2l.pan", m = m.imputations, seed = 0,
-            print = FALSE)
+        imp <- mice::mice(data_M, pred = pred, method = "2l.pan", m = m.imputations,
+            seed = 0, print = FALSE)
         if (!is.null(covariate)) {
-            mod1 <- with(data = imp, lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M + COVARIATE_M,
-                random = ~1 | ID_M/TIME_M))
-            mod1.aov <- quiet(mi.anova(imp, formula = "OUTCOME_M ~ TIME_M * GROUP_M + COVARIATE_M"))
+            mod1 <- with(data = imp, lme4::lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M +
+                COVARIATE_M, random = ~1 | ID_M/TIME_M))
+            mod1.aov <- quiet(miceadds::mi.anova(imp, formula = "OUTCOME_M ~ TIME_M * GROUP_M + COVARIATE_M"))
         } else {
-            mod1 <- with(data = imp, lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M, random = ~1 |
-                ID_M/TIME_M))
-            mod1.aov <- quiet(mi.anova(imp, formula = "OUTCOME_M ~ TIME_M * GROUP_M"))
+            mod1 <- with(data = imp, lme4::lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M,
+                random = ~1 | ID_M/TIME_M))
+            mod1.aov <- quiet(miceadds::mi.anova(imp, formula = "OUTCOME_M ~ TIME_M * GROUP_M"))
         }
         mod1.aov <- mod1.aov$anova.table[1:3, 2:5]
         mod1.aov <- rbind(rep(NA, 4), mod1.aov)
@@ -212,8 +212,8 @@ TABLE.2a <- function(dataset, variables, covariate, bw.factor, control.g, wt.lab
     model.res[3, 1] <- interaction
 
     # calcula e preenche a subtabela WITHIN-GROUP (SAME LINEAR MIXED MODEL)
-    mult.within <- summary(pairs(emmeans(mod1, ~TIME_M | GROUP_M), reverse = FALSE),
-        infer = c(TRUE, TRUE))
+    mult.within <- multcomp::summary(emmGrid::pairs(emmeans::emmeans(mod1, ~TIME_M |
+        GROUP_M), reverse = FALSE), infer = c(TRUE, TRUE))
     wt <- c()
     wt.pvalues <- c()
     for (i in 1:nlevels(bw.factor)) {
@@ -255,22 +255,23 @@ TABLE.2a <- function(dataset, variables, covariate, bw.factor, control.g, wt.lab
             if (!is.null(covariate)) {
                 df <- data.frame(ID, bw.factor, BASELINE_M, FOLLOWUP_M, CHANGE_M,
                   COVARIATE_M)
-                mod2 <- lme(CHANGE_M ~ bw.factor + BASELINE_M + COVARIATE_M, random = ~1 |
-                  ID, data = df)
+                mod2 <- lme4::lme(CHANGE_M ~ bw.factor + BASELINE_M + COVARIATE_M,
+                  random = ~1 | ID, data = df)
             } else {
                 df <- data.frame(ID, bw.factor, BASELINE_M, FOLLOWUP_M, CHANGE_M)
-                mod2 <- lme(CHANGE_M ~ bw.factor + BASELINE_M, random = ~1 | ID,
-                  data = df)
+                mod2 <- lme4::lme(CHANGE_M ~ bw.factor + BASELINE_M, random = ~1 |
+                  ID, data = df)
             }
-            mod2.sum <- summary(glht(mod2, linfct = mcp(bw.factor = "Tukey")), test = adjusted("holm"))
+            mod2.sum <- multcomp::summary(multcomp::glht(mod2, linfct = mcp::mcp(bw.factor = "Tukey")),
+                test = adjusted("holm"))
             names <- names(coef(mod2.sum))
             estimate <- round(confint(mod2.sum, level = 1 - alpha)$confint[, "Estimate"],
                 digits = n.digits)
             low.ci <- round(confint(mod2.sum, level = 1 - alpha)$confint[, "lwr"],
                 digits = n.digits)
             upp.ci <- round(confint(mod2.sum, level = 1 - alpha)$confint[, "upr"],
                 digits = n.digits)
-            p.value <- summary(mod2)$tTable[, "p-value"][2]
+            p.value <- multcomp::summary(mod2)$tTable[, "p-value"][2]
         } else {
             if (!is.null(covariate)) {
                 df <- data.frame(ID, bw.factor, BASELINE_M, FOLLOWUP_M, CHANGE_M,
@@ -282,7 +283,7 @@ TABLE.2a <- function(dataset, variables, covariate, bw.factor, control.g, wt.lab
             pred <- ini$pred
             pred["FOLLOWUP_M", "ID"] <- -2
             pred["CHANGE_M", "ID"] <- -2
-            imp <- mice(data = df, pred = pred, method = "2l.pan", m = m.imputations,
+            imp <- mice::mice(data = df, pred = pred, method = "2l.pan", m = m.imputations,
                 seed = 0, print = FALSE)
             implist <- mids2mitml.list(imp)
             mod2 <- with(data = implist, lm(CHANGE_M ~ bw.factor + BASELINE_M, random = ~1 |
@@ -315,7 +316,7 @@ TABLE.2a <- function(dataset, variables, covariate, bw.factor, control.g, wt.lab
         group_data[bw.factor == bw.factor] <- 0
         group_data[bw.factor != control.g] <- 1
         data <- data.frame(group_data, CHANGE_M)
-        smd <- stddiff.numeric(data = data, gcol = 1, vcol = 2)
+        smd <- stddiff::stddiff.numeric(data = data, gcol = 1, vcol = 2)
         estimate <- round(smd[7], digits = n.digits)
         lower <- round(smd[8], digits = n.digits)
         upper <- round(smd[9], digits = n.digits)

diff --git a/R/Ensaio experimental aleatorizado/RCT-Table2b.R b/R/Ensaio experimental aleatorizado/RCT-Table2b.R
@@ -122,27 +122,27 @@ TABLE.2b <- function(dataset, variables, covariate, bw.factor, control.g, wt.lab
     # fit linear mixed model
     if (missing != "multiple.imputation") {
         if (!is.null(covariate)) {
-            mod1 <- lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M + COVARIATE_M, random = ~1 |
-                ID_M/TIME_M, data = data_M)
+            mod1 <- lme4::lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M + COVARIATE_M,
+                random = ~1 | ID_M/TIME_M, data = data_M)
         } else {
-            mod1 <- lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M, random = ~1 | ID_M/TIME_M,
-                data = data_M)
+            mod1 <- lme4::lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M, random = ~1 |
+                ID_M/TIME_M, data = data_M)
         }
         mod1.aov <- anova(mod1)
     } else {
-        ini <- mice(data = data_M, maxit = 0)
+        ini <- mice::mice(data = data_M, maxit = 0)
         pred <- ini$pred
         pred["OUTCOME_M", "ID_M"] <- -2
-        imp <- mice(data_M, pred = pred, method = "2l.pan", m = m.imputations, seed = 0,
-            print = FALSE)
+        imp <- mice::mice(data_M, pred = pred, method = "2l.pan", m = m.imputations,
+            seed = 0, print = FALSE)
         if (!is.null(covariate)) {
             mod1 <- with(data = imp, lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M + COVARIATE_M,
                 random = ~1 | ID_M/TIME_M))
-            mod1.aov <- quiet(mi.anova(imp, formula = "OUTCOME_M ~ TIME_M * GROUP_M + COVARIATE_M"))
+            mod1.aov <- quiet(miceadds::mi.anova(imp, formula = "OUTCOME_M ~ TIME_M * GROUP_M + COVARIATE_M"))
         } else {
-            mod1 <- with(data = imp, lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M, random = ~1 |
-                ID_M/TIME_M))
-            mod1.aov <- quiet(mi.anova(imp, formula = "OUTCOME_M ~ TIME_M * GROUP_M"))
+            mod1 <- with(data = imp, lme4::lme(fixed = OUTCOME_M ~ TIME_M * GROUP_M,
+                random = ~1 | ID_M/TIME_M))
+            mod1.aov <- quiet(miceadds::mi.anova(imp, formula = "OUTCOME_M ~ TIME_M * GROUP_M"))
         }
         mod1.aov <- mod1.aov$anova.table[1:3, 2:5]
         mod1.aov <- rbind(rep(NA, 4), mod1.aov)
@@ -208,8 +208,8 @@ TABLE.2b <- function(dataset, variables, covariate, bw.factor, control.g, wt.lab
     }
 
     # calcula e preenche a subtabela WITHIN-GROUP (SAME LINEAR MIXED MODEL)
-    mult.within <- summary(pairs(emmeans(mod1, ~TIME_M | GROUP_M), reverse = FALSE),
-        infer = c(TRUE, TRUE))
+    mult.within <- multcomp::summary(emmGrid::pairs(emmeans::emmeans(mod1, ~TIME_M |
+        GROUP_M), reverse = FALSE), infer = c(TRUE, TRUE))
     wt <- c()
     wt.pvalues <- c()
     for (i in 1:nlevels(bw.factor)) {
@@ -239,8 +239,8 @@ TABLE.2b <- function(dataset, variables, covariate, bw.factor, control.g, wt.lab
     bw <- c()
     bw.pvalues <- c()
     smd.values <- c()
-    mult.between <- summary(pairs(emmeans(mod1, ~GROUP_M | TIME_M), reverse = FALSE),
-        infer = c(TRUE, TRUE))
+    mult.between <- multcomp::summary(emmGrid::pairs(emmeans::emmeans(mod1, ~GROUP_M |
+        TIME_M), reverse = FALSE), infer = c(TRUE, TRUE))
     for (i in 1:(length(wt.labels))) {
         group.data <- mult.between[i, ]
         # reverse signs due to mult.within order
@@ -259,7 +259,7 @@ TABLE.2b <- function(dataset, variables, covariate, bw.factor, control.g, wt.lab
         group_data[bw.factor == bw.factor] <- 0
         group_data[bw.factor != control.g] <- 1
         data <- data.frame(group_data, OUTCOME_M[TIME_M == i])
-        smd <- stddiff.numeric(data = data, gcol = 1, vcol = 2)
+        smd <- stddiff::stddiff.numeric(data = data, gcol = 1, vcol = 2)
         estimate <- round(smd[7], digits = n.digits)
         lower <- round(smd[8], digits = n.digits)
         upper <- round(smd[9], digits = n.digits)