diff --git a/R/anova.rma.r b/R/anova.rma.r
index c683a7dcd..8e82232f4 100644
--- a/R/anova.rma.r
+++ b/R/anova.rma.r
@@ -215,8 +215,8 @@ anova.rma <- function(object, object2, btt, X, att, Z, rhs, digits, refit=FALSE,
 
             ### omnibus test of all hypotheses (only possible if 'Z' is of full rank)
 
-            QS  <- NA_real_ ### need this in case QS cannot be calculated below
-            QSp <- NA_real_ ### need this in case QSp cannot be calculated below
+            QS  <- NA_real_ # need this in case QS cannot be calculated below
+            QSp <- NA_real_ # need this in case QSp cannot be calculated below
 
             if (rankMatrix(Z) == m) {
 
@@ -240,24 +240,24 @@ anova.rma <- function(object, object2, btt, X, att, Z, rhs, digits, refit=FALSE,
 
             hyp <- rep("", m)
             for (j in seq_len(m)) {
-               Zj <- round(Z[j,], digits[["est"]]) ### coefficients for the jth contrast
-               sel <- Zj != 0 ### TRUE if coefficient is != 0
-               hyp[j] <- paste(paste(Zj[sel], rownames(x$alpha)[sel], sep="*"), collapse=" + ") ### coefficient*variable + coefficient*variable ...
-               hyp[j] <- gsub("1*", "", hyp[j], fixed=TRUE) ### turn '+1' into '+' and '-1' into '-'
-               hyp[j] <- gsub("+ -", "- ", hyp[j], fixed=TRUE) ### turn '+ -' into '-'
+               Zj <- round(Z[j,], digits[["est"]]) # coefficients for the jth contrast
+               sel <- Zj != 0 # TRUE if coefficient is != 0
+               hyp[j] <- paste(paste(Zj[sel], rownames(x$alpha)[sel], sep="*"), collapse=" + ") # coefficient*variable + coefficient*variable ...
+               hyp[j] <- gsub("1*", "", hyp[j], fixed=TRUE) # turn '+1' into '+' and '-1' into '-'
+               hyp[j] <- gsub("+ -", "- ", hyp[j], fixed=TRUE) # turn '+ -' into '-'
             }
             if (identical(rhs, rep(0,m))) {
-               hyp <- paste0(hyp, " = 0") ### add '= 0' at the right
+               hyp <- paste0(hyp, " = 0") # add '= 0' at the right
             } else {
                if (length(unique(rhs)) == 1L) {
-                  hyp <- paste0(hyp, " = ", round(rhs, digits=digits[["est"]])) ### add '= rhs' at the right
+                  hyp <- paste0(hyp, " = ", round(rhs, digits=digits[["est"]])) # add '= rhs' at the right
                } else {
-                  hyp <- paste0(hyp, " = ", fmtx(rhs, digits=digits[["est"]])) ### add '= rhs' at the right
+                  hyp <- paste0(hyp, " = ", fmtx(rhs, digits=digits[["est"]])) # add '= rhs' at the right
                }
             }
             hyp <- data.frame(hyp, stringsAsFactors=FALSE)
             colnames(hyp) <- ""
-            rownames(hyp) <- paste0(seq_len(m), ":") ### add '1:', '2:', ... as row names
+            rownames(hyp) <- paste0(seq_len(m), ":") # add '1:', '2:', ... as row names
 
             res <- list(QS=QS, QSdf=QSdf, QSp=QSp, hyp=hyp, Za=Za, se=se, zval=zval, pval=pval, k=x$k, q=x$q, m=m, test=x$test, ddf=x$ddf.alpha, digits=digits, type="Wald.Za")
 
@@ -400,24 +400,24 @@ anova.rma <- function(object, object2, btt, X, att, Z, rhs, digits, refit=FALSE,
 
             hyp <- rep("", m)
             for (j in seq_len(m)) {
-               Xj <- round(X[j,], digits[["est"]]) ### coefficients for the jth contrast
-               sel <- Xj != 0 ### TRUE if coefficient is != 0
-               hyp[j] <- paste(paste(Xj[sel], rownames(x$beta)[sel], sep="*"), collapse=" + ") ### coefficient*variable + coefficient*variable ...
-               hyp[j] <- gsub("1*", "", hyp[j], fixed=TRUE) ### turn '+1' into '+' and '-1' into '-'
-               hyp[j] <- gsub("+ -", "- ", hyp[j], fixed=TRUE) ### turn '+ -' into '-'
+               Xj <- round(X[j,], digits[["est"]]) # coefficients for the jth contrast
+               sel <- Xj != 0 # TRUE if coefficient is != 0
+               hyp[j] <- paste(paste(Xj[sel], rownames(x$beta)[sel], sep="*"), collapse=" + ") # coefficient*variable + coefficient*variable ...
+               hyp[j] <- gsub("1*", "", hyp[j], fixed=TRUE) # turn '+1' into '+' and '-1' into '-'
+               hyp[j] <- gsub("+ -", "- ", hyp[j], fixed=TRUE) # turn '+ -' into '-'
             }
             if (identical(rhs, rep(0,m))) {
-               hyp <- paste0(hyp, " = 0") ### add '= 0' at the right
+               hyp <- paste0(hyp, " = 0") # add '= 0' at the right
             } else {
                if (length(unique(rhs)) == 1L) {
-                  hyp <- paste0(hyp, " = ", round(rhs, digits=digits[["est"]])) ### add '= rhs' at the right
+                  hyp <- paste0(hyp, " = ", round(rhs, digits=digits[["est"]])) # add '= rhs' at the right
                } else {
-                  hyp <- paste0(hyp, " = ", fmtx(rhs,  digits=digits[["est"]])) ### add '= rhs' at the right
+                  hyp <- paste0(hyp, " = ", fmtx(rhs,  digits=digits[["est"]])) # add '= rhs' at the right
                }
             }
             hyp <- data.frame(hyp, stringsAsFactors=FALSE)
             colnames(hyp) <- ""
-            rownames(hyp) <- paste0(seq_len(m), ":") ### add '1:', '2:', ... as row names
+            rownames(hyp) <- paste0(seq_len(m), ":") # add '1:', '2:', ... as row names
 
             res <- list(QM=QM, QMdf=QMdf, QMp=QMp, hyp=hyp, Xb=Xb, se=se, zval=zval, pval=pval, k=x$k, p=x$p, m=m, test=x$test, ddf=ddf, digits=digits, type="Wald.Xb")
 
diff --git a/R/bldiag.r b/R/bldiag.r
index d62870286..7996ff0a3 100644
--- a/R/bldiag.r
+++ b/R/bldiag.r
@@ -22,7 +22,7 @@ bldiag <- function(..., order) {
    if (any(is00))
       mlist <- mlist[!is00]
 
-   csdim <- rbind(c(0,0), apply(sapply(mlist,dim), 1, cumsum)) ### consider using rowCumsums() from matrixStats package
+   csdim <- rbind(c(0,0), apply(sapply(mlist,dim), 1, cumsum)) # consider using rowCumsums() from matrixStats package
 
    out  <- array(0, dim=csdim[length(mlist) + 1,])
    add1 <- matrix(rep(1:0, 2L), ncol=2)
@@ -31,9 +31,9 @@ bldiag <- function(..., order) {
 
       indx <- apply(csdim[i:(i+1),] + add1, 2, function(x) x[1]:x[2])
 
-      if (is.null(dim(indx))) {                 ### non-square matrix
+      if (is.null(dim(indx))) {                 # non-square matrix
          out[indx[[1]],indx[[2]]] <- mlist[[i]]
-      } else {                                  ### square matrix
+      } else {                                  # square matrix
          out[indx[,1],indx[,2]] <- mlist[[i]]
       }
 
diff --git a/R/cooks.distance.rma.mv.r b/R/cooks.distance.rma.mv.r
index a6e6ba491..8cc15aa8c 100644
--- a/R/cooks.distance.rma.mv.r
+++ b/R/cooks.distance.rma.mv.r
@@ -4,7 +4,7 @@ cooks.distance.rma.mv <- function(model, progbar=FALSE, cluster, reestimate=TRUE
 
    .chkclass(class(model), must="rma.mv")
 
-   #if (inherits(model, "robust.rma")) ### can compute Cook's distance also for 'robust.rma' objects
+   #if (inherits(model, "robust.rma")) # can compute Cook's distance also for 'robust.rma' objects
    #   stop(mstyle$stop("Method not available for objects of class \"robust.rma\"."))
 
    na.act <- getOption("na.action")
diff --git a/R/cumul.rma.mh.r b/R/cumul.rma.mh.r
index c0fe1b029..1b58e6350 100644
--- a/R/cumul.rma.mh.r
+++ b/R/cumul.rma.mh.r
@@ -133,7 +133,7 @@ cumul.rma.mh <- function(x, order, digits, transf, targs, progbar=FALSE, ...) {
 
    ### if requested, apply transformation function
 
-   if (.isTRUE(transf) && is.element(x$measure, c("OR","RR","IRR"))) ### if transf=TRUE, apply exp transformation to ORs, RRs, and IRRs
+   if (.isTRUE(transf) && is.element(x$measure, c("OR","RR","IRR"))) # if transf=TRUE, apply exp transformation to ORs, RRs, and IRRs
       transf <- exp
 
    if (is.function(transf)) {
diff --git a/R/cumul.rma.peto.r b/R/cumul.rma.peto.r
index 9049429ec..11b1501da 100644
--- a/R/cumul.rma.peto.r
+++ b/R/cumul.rma.peto.r
@@ -123,7 +123,7 @@ cumul.rma.peto <- function(x, order, digits, transf, targs, progbar=FALSE, ...)
 
    ### if requested, apply transformation function
 
-   if (.isTRUE(transf)) ### if transf=TRUE, apply exp transformation to ORs
+   if (.isTRUE(transf)) # if transf=TRUE, apply exp transformation to ORs
       transf <- exp
 
    if (is.function(transf)) {
diff --git a/R/forest.rma.r b/R/forest.rma.r
index 08d0d1fa3..356403a1c 100644
--- a/R/forest.rma.r
+++ b/R/forest.rma.r
@@ -920,7 +920,7 @@ lty, fonts, cex, cex.lab, cex.axis, ...) {
             if (length(addpred) == 1L)
                addpred <- c(addpred, addpred)
             temp <- predict(x, level=level, tau2.levels=addpred[1], gamma2.levels=addpred[2], pi.type=pi.type)
-            addpred <- TRUE ### set addpred to TRUE, so if (!is.element(x$method, c("FE","EE","CE")) && addpred) further below works
+            addpred <- TRUE # set addpred to TRUE, so if (!is.element(x$method, c("FE","EE","CE")) && addpred) further below works
          } else {
             if (addpred) {
                ### here addpred=TRUE, but user has not specified the level, so throw an error
diff --git a/R/hatvalues.rma.mv.r b/R/hatvalues.rma.mv.r
index b8bfc44bc..c31599b87 100644
--- a/R/hatvalues.rma.mv.r
+++ b/R/hatvalues.rma.mv.r
@@ -19,7 +19,7 @@ hatvalues.rma.mv <- function(model, type="diagonal", ...) {
       W     <- chol2inv(chol(x$M))
       stXWX <- chol2inv(chol(as.matrix(t(x$X) %*% W %*% x$X)))
       H     <- as.matrix(x$X %*% stXWX %*% crossprod(x$X,W))
-      #H <- as.matrix(x$X %*% x$vb %*% crossprod(x$X,W)) ### x$vb may have been changed through robust()
+      #H <- as.matrix(x$X %*% x$vb %*% crossprod(x$X,W)) # x$vb may have been changed through robust()
    } else {
       A     <- x$W
       stXAX <- chol2inv(chol(as.matrix(t(x$X) %*% A %*% x$X)))
@@ -33,7 +33,7 @@ hatvalues.rma.mv <- function(model, type="diagonal", ...) {
 
       hii <- rep(NA_real_, x$k.f)
       hii[x$not.na] <- as.vector(diag(H))
-      hii[hii > 1 - 10 * .Machine$double.eps] <- 1 ### as in lm.influence()
+      hii[hii > 1 - 10 * .Machine$double.eps] <- 1 # as in lm.influence()
       names(hii) <- x$slab
 
       if (na.act == "na.omit")
diff --git a/R/hatvalues.rma.uni.r b/R/hatvalues.rma.uni.r
index 3ef243a67..4206cdb7c 100644
--- a/R/hatvalues.rma.uni.r
+++ b/R/hatvalues.rma.uni.r
@@ -20,7 +20,7 @@ hatvalues.rma.uni <- function(model, type="diagonal", ...) {
          W     <- diag(1/(x$vi + x$tau2), nrow=x$k, ncol=x$k)
          stXWX <- .invcalc(X=x$X, W=W, k=x$k)
          H     <- x$X %*% stXWX %*% crossprod(x$X,W)
-         #H <- x$X %*% (x$vb / x$s2w) %*% crossprod(x$X,W) ### x$vb may be changed through robust() (and when test="knha")
+         #H <- x$X %*% (x$vb / x$s2w) %*% crossprod(x$X,W) # x$vb may be changed through robust() (and when test="knha")
       } else {
          A     <- diag(x$weights, nrow=x$k, ncol=x$k)
          stXAX <- .invcalc(X=x$X, W=A, k=x$k)
@@ -37,7 +37,7 @@ hatvalues.rma.uni <- function(model, type="diagonal", ...) {
 
       hii <- rep(NA_real_, x$k.f)
       hii[x$not.na] <- diag(H)
-      hii[hii > 1 - 10 * .Machine$double.eps] <- 1 ### as in lm.influence()
+      hii[hii > 1 - 10 * .Machine$double.eps] <- 1 # as in lm.influence()
       names(hii) <- x$slab
 
       if (na.act == "na.omit")
diff --git a/R/hc.rma.uni.r b/R/hc.rma.uni.r
index cd347cded..9b6c23e38 100644
--- a/R/hc.rma.uni.r
+++ b/R/hc.rma.uni.r
@@ -48,7 +48,7 @@ hc.rma.uni <- function(object, digits, transf, targs, control, ...) {
    ### original code by Henmi & Copas (2012), modified by Michael Dewey, small adjustments
    ### for consistency with other functions in the metafor package by Wolfgang Viechtbauer
 
-   wi <- 1/vi ### fixed effects weights
+   wi <- 1/vi # fixed effects weights
 
    W1 <- sum(wi)
    W2 <- sum(wi^2) / W1
diff --git a/R/labbe.rma.r b/R/labbe.rma.r
index 10d533ed9..3c7a895d6 100644
--- a/R/labbe.rma.r
+++ b/R/labbe.rma.r
@@ -137,7 +137,7 @@ add=x$add, to=x$to, transf, targs, pch=21, psize, plim=c(0.5,3.5), col, bg, grid
    t1i[yi.is.na] <- NA_real_
    t2i[yi.is.na] <- NA_real_
 
-   options(na.action = "na.pass") ### to make sure dat.t and dat.c are of the same length
+   options(na.action = "na.pass") # to make sure dat.t and dat.c are of the same length
 
    measure <- switch(x$measure, "RR"="PLN", "OR"="PLO", "RD"="PR", "AS"="PAS", "IRR"="IRLN", "IRD"="IR", "IRSD"="IRS")
 
diff --git a/R/leave1out.rma.mh.r b/R/leave1out.rma.mh.r
index a3d479f36..f69fb05e4 100644
--- a/R/leave1out.rma.mh.r
+++ b/R/leave1out.rma.mh.r
@@ -100,7 +100,7 @@ leave1out.rma.mh <- function(x, digits, transf, targs, progbar=FALSE, ...) {
 
    ### if requested, apply transformation function
 
-   if (.isTRUE(transf) && is.element(x$measure, c("OR","RR","IRR"))) ### if transf=TRUE, apply exp transformation to ORs, RRs, and IRRs
+   if (.isTRUE(transf) && is.element(x$measure, c("OR","RR","IRR"))) # if transf=TRUE, apply exp transformation to ORs, RRs, and IRRs
       transf <- exp
 
    if (is.function(transf)) {
diff --git a/R/leave1out.rma.peto.r b/R/leave1out.rma.peto.r
index d1f3991f7..4616b6b1d 100644
--- a/R/leave1out.rma.peto.r
+++ b/R/leave1out.rma.peto.r
@@ -92,7 +92,7 @@ leave1out.rma.peto <- function(x, digits, transf, targs, progbar=FALSE, ...) {
 
    ### if requested, apply transformation function
 
-   if (.isTRUE(transf)) ### if transf=TRUE, apply exp transformation to ORs
+   if (.isTRUE(transf)) # if transf=TRUE, apply exp transformation to ORs
       transf <- exp
 
    if (is.function(transf)) {
diff --git a/R/llplot.r b/R/llplot.r
index 15990c0d3..13d9e080f 100644
--- a/R/llplot.r
+++ b/R/llplot.r
@@ -96,7 +96,7 @@ lty, lwd, col, level=99.99, refline=0, ...) {
       if (!.equal.length(yi, vi))
          stop(mstyle$stop("Supplied data vectors are not all of the same length."))
 
-      k <- length(yi) ### number of outcomes before subsetting
+      k <- length(yi) # number of outcomes before subsetting
 
       ### subsetting
 
@@ -146,7 +146,7 @@ lty, lwd, col, level=99.99, refline=0, ...) {
       if (any(c(n1i < 0, n2i < 0), na.rm=TRUE))
          stop(mstyle$stop("One or more group sizes are negative."))
 
-      k <- length(ai) ### number of outcomes before subsetting
+      k <- length(ai) # number of outcomes before subsetting
 
       ### note studies that have at least one zero cell
 
@@ -179,8 +179,8 @@ lty, lwd, col, level=99.99, refline=0, ...) {
 
       dat <- .do.call(escalc, measure="OR", ai=ai, bi=bi, ci=ci, di=di, drop00=drop00, onlyo1=onlyo1, addyi=addyi, addvi=addvi)
 
-      yi <- dat$yi ### one or more yi/vi pairs may be NA/NA
-      vi <- dat$vi ### one or more yi/vi pairs may be NA/NA
+      yi <- dat$yi # one or more yi/vi pairs may be NA/NA
+      vi <- dat$vi # one or more yi/vi pairs may be NA/NA
 
    }
 
diff --git a/R/methods.list.rma.r b/R/methods.list.rma.r
index fc528c247..6e911f813 100644
--- a/R/methods.list.rma.r
+++ b/R/methods.list.rma.r
@@ -19,7 +19,7 @@
    if (length(out[[1]]) == 0L)
       return(NULL)
 
-   #out <- out[j] ### this causes all kinds of problems, so left out for now (TODO: check if this is really a problem)
+   #out <- out[j] # this causes all kinds of problems, so left out for now (TODO: check if this is really a problem)
 
    out$slab <- x$slab[i]
 
diff --git a/R/misc.func.hidden.escalc.r b/R/misc.func.hidden.escalc.r
index 255e4f5be..208cb46ac 100644
--- a/R/misc.func.hidden.escalc.r
+++ b/R/misc.func.hidden.escalc.r
@@ -256,8 +256,8 @@
 }
 
 #integrate(function(x) .dcor(x, n=5, rho=.8), lower=-1, upper=1)
-#integrate(function(x) x*.dcor(x, n=5, rho=.8), lower=-1, upper=1) ### should not be rho due to bias!
-#integrate(function(x) x*.Fcalc(1/2, 1/2, (5-2)/2, 1-x^2)*.dcor(x, n=5, rho=.8), lower=-1, upper=1) ### should be ~rho
+#integrate(function(x) x*.dcor(x, n=5, rho=.8), lower=-1, upper=1) # should not be rho due to bias!
+#integrate(function(x) x*.Fcalc(1/2, 1/2, (5-2)/2, 1-x^2)*.dcor(x, n=5, rho=.8), lower=-1, upper=1) # should be ~rho
 
 ### pdf of ZCOR
 
diff --git a/R/misc.func.hidden.glmm.r b/R/misc.func.hidden.glmm.r
index d8eaf9d8c..5ee8daaed 100644
--- a/R/misc.func.hidden.glmm.r
+++ b/R/misc.func.hidden.glmm.r
@@ -4,7 +4,7 @@
 ### Liao, J. G. & Rosen, O. (2001). Fast and stable algorithms for computing and sampling from the
 ### noncentral hypergeometric distribution. The American Statistician, 55, 366-369.
 
-.dnoncenhypergeom <- function (x=NA_real_, n1, n2, m1, psi) { ### x=ai, n1=ai+bi, n2=ci+di, m1=ai+ci, psi=ORi
+.dnoncenhypergeom <- function (x=NA_real_, n1, n2, m1, psi) { # x=ai, n1=ai+bi, n2=ci+di, m1=ai+ci, psi=ORi
 
    mstyle <- .get.mstyle()
 
@@ -114,8 +114,8 @@
 
    p    <- ncol(X.fit)
    k    <- length(ai)
-   beta <- parms[seq_len(p)]                  ### first p elemenets in parms are the model coefficients
-   tau2 <- ifelse(random, exp(parms[p+1]), 0) ### next value is tau^2 -- optimize over exp(tau^2) value or hold at 0 if random=FALSE
+   beta <- parms[seq_len(p)]                  # first p elemenets in parms are the model coefficients
+   tau2 <- ifelse(random, exp(parms[p+1]), 0) # next value is tau^2 -- optimize over exp(tau^2) value or hold at 0 if random=FALSE
    mu.i <- X.fit %*% cbind(beta)
 
    lli  <- rep(NA_real_, k)
diff --git a/R/misc.func.hidden.mv.r b/R/misc.func.hidden.mv.r
index dc3b42da5..fb7cfbe29 100644
--- a/R/misc.func.hidden.mv.r
+++ b/R/misc.func.hidden.mv.r
@@ -74,7 +74,7 @@
 
    ### get variables names in mf.g
 
-   g.names <- names(mf.g) ### names for inner and outer factors/variables
+   g.names <- names(mf.g) # names for inner and outer factors/variables
 
    ### check that inner variable is a factor (or character variable) for structures that require this (no longer required)
 
@@ -107,7 +107,7 @@
 
    ### get number of levels of each variable in mf.g (vector with two values, for the inner and outer factor)
 
-   #g.nlevels <- c(nlevels(mf.g[[1]]), nlevels(mf.g[[2]])) ### works only for factors
+   #g.nlevels <- c(nlevels(mf.g[[1]]), nlevels(mf.g[[2]])) # works only for factors
    if (is.element(struct, c("SPEXP","SPGAU","SPLIN","SPRAT","SPSPH","PHYBM","PHYPL","PHYPD","GEN","GDIAG"))) {
       g.nlevels <- c(length(unique(apply(mf.g[-nvars], 1, paste, collapse=" + "))), length(unique(mf.g[[nvars]])))
    } else {
@@ -116,7 +116,7 @@
 
    ### get levels of each variable in mf.g
 
-   #g.levels <- list(levels(mf.g[[1]]), levels(mf.g[[2]])) ### works only for factors
+   #g.levels <- list(levels(mf.g[[1]]), levels(mf.g[[2]])) # works only for factors
    if (is.element(struct, c("SPEXP","SPGAU","SPLIN","SPRAT","SPSPH","PHYBM","PHYPL","PHYPD","GEN","GDIAG"))) {
       g.levels <- list(sort(unique(apply(mf.g[-nvars], 1, paste, collapse=" + "))), sort(unique((mf.g[[nvars]]))))
    } else {
@@ -279,7 +279,7 @@
 
    ### redo: get number of levels of each variable in mf.g (vector with two values, for the inner and outer factor)
 
-   #g.nlevels <- c(nlevels(mf.g[[1]]), nlevels(mf.g[[2]])) ### works only for factors
+   #g.nlevels <- c(nlevels(mf.g[[1]]), nlevels(mf.g[[2]])) # works only for factors
    if (is.element(struct, c("SPEXP","SPGAU","SPLIN","SPRAT","SPSPH","PHYBM","PHYPL","PHYPD","GEN","GDIAG"))) {
       g.nlevels <- c(length(unique(apply(mf.g[-nvars], 1, paste, collapse=" + "))), length(unique(mf.g[[nvars]])))
    } else {
@@ -288,7 +288,7 @@
 
    ### redo: get levels of each variable in mf.g
 
-   #g.levels <- list(levels(mf.g[[1]]), levels(mf.g[[2]])) ### works only for factors
+   #g.levels <- list(levels(mf.g[[1]]), levels(mf.g[[2]])) # works only for factors
    if (is.element(struct, c("SPEXP","SPGAU","SPLIN","SPRAT","SPSPH","PHYBM","PHYPL","PHYPD","GEN","GDIAG"))) {
       g.levels <- list(sort(unique(apply(mf.g[-nvars], 1, paste, collapse=" + "))), sort(unique((mf.g[[nvars]]))))
    } else {
@@ -613,7 +613,7 @@
    if (any(g.levels.r) && struct == "UNR") {
       G[g.levels.r,] <- 0
       G[,g.levels.r] <- 0
-      diag(G) <- tau2 ### don't really need this
+      diag(G) <- tau2 # don't really need this
       rho <- G[lower.tri(G)]
       warning(mstyle$warning(paste0("Fixed ", ifelse(isG, 'rho', 'phi'), " value(s) to 0 for removed level(s).")), call.=FALSE)
    }
diff --git a/R/misc.func.hidden.r b/R/misc.func.hidden.r
index fcad079d5..ac704505c 100644
--- a/R/misc.func.hidden.r
+++ b/R/misc.func.hidden.r
@@ -8,14 +8,14 @@
 
    if (missing(btt) || is.null(btt)) {
 
-      if (p > 1L) {                       ### if the model matrix has more than one column
+      if (p > 1L) {                       # if the model matrix has more than one column
          if (int.incl) {
-            btt <- seq.int(from=2, to=p)     ### and the model has an intercept term, test all coefficients except the intercept
+            btt <- seq.int(from=2, to=p)     # and the model has an intercept term, test all coefficients except the intercept
          } else {
-            btt <- seq_len(p)                ### and the model does not have an intercept term, test all coefficients
+            btt <- seq_len(p)                # and the model does not have an intercept term, test all coefficients
          }
       } else {
-         btt <- 1L                        ### if the model matrix has a single column, test that single coefficient
+         btt <- 1L                        # if the model matrix has a single column, test that single coefficient
       }
 
    } else {
diff --git a/R/print.list.rma.r b/R/print.list.rma.r
index aed9a4000..674e3d9fe 100644
--- a/R/print.list.rma.r
+++ b/R/print.list.rma.r
@@ -56,7 +56,7 @@ print.list.rma <- function(x, digits=x$digits, ...) {
    sav <- out[,seq_len(min.pos-1)]
 
    for (i in seq_len(min.pos-1)) {
-      if (inherits(out[,i], c("integer","logical","factor","character"))) { ### do not apply formating to these classes
+      if (inherits(out[,i], c("integer","logical","factor","character"))) { # do not apply formating to these classes
          out[,i] <- out[,i]
       } else {
          if (names(out)[i] %in% c("pred", "resid"))
diff --git a/R/print.rma.mv.r b/R/print.rma.mv.r
index 9a040d7ac..68eb3e30e 100644
--- a/R/print.rma.mv.r
+++ b/R/print.rma.mv.r
@@ -180,7 +180,7 @@ print.rma.mv <- function(x, digits, showfit=FALSE, signif.stars=getOption("show.
             diag(G.info) <- "-"
 
             vc <- cbind(G, "", G.info)
-            colnames(vc) <- c(paste0("rho.", abbreviate(x$g.levels.f[[1]])), "", abbreviate(x$g.levels.f[[1]])) ### FIXME: x$g.levels.f[[1]] may be numeric, in which case a wrapping 'header' is not recognized
+            colnames(vc) <- c(paste0("rho.", abbreviate(x$g.levels.f[[1]])), "", abbreviate(x$g.levels.f[[1]])) # FIXME: x$g.levels.f[[1]] may be numeric, in which case a wrapping 'header' is not recognized
             rownames(vc) <- x$g.levels.f[[1]]
             tmp <- capture.output(print(vc, quote=FALSE, right=right, print.gap=2))
             .print.table(tmp, mstyle)
@@ -311,7 +311,7 @@ print.rma.mv <- function(x, digits, showfit=FALSE, signif.stars=getOption("show.
             diag(H.info) <- "-"
 
             vc <- cbind(H, "", H.info)
-            colnames(vc) <- c(paste0("phi.", abbreviate(x$h.levels.f[[1]])), "", abbreviate(x$h.levels.f[[1]])) ### FIXME: x$h.levels.f[[1]] may be numeric, in which case a wrapping 'header' is not recognized
+            colnames(vc) <- c(paste0("phi.", abbreviate(x$h.levels.f[[1]])), "", abbreviate(x$h.levels.f[[1]])) # FIXME: x$h.levels.f[[1]] may be numeric, in which case a wrapping 'header' is not recognized
             rownames(vc) <- x$h.levels.f[[1]]
             tmp <- capture.output(print(vc, quote=FALSE, right=right, print.gap=2))
             .print.table(tmp, mstyle)
diff --git a/R/qqnorm.rma.uni.r b/R/qqnorm.rma.uni.r
index 923b055c0..082a71fc2 100644
--- a/R/qqnorm.rma.uni.r
+++ b/R/qqnorm.rma.uni.r
@@ -35,7 +35,7 @@ label=FALSE, offset=0.3, pos=13, lty, ...) {
       bg <- .coladj(par("bg","fg"), dark=0.35, light=-0.35)
 
    if (missing(lty)) {
-      lty <- c("solid", "dotted") ### 1st value = diagonal line, 2nd value = pseudo confidence envelope
+      lty <- c("solid", "dotted") # 1st value = diagonal line, 2nd value = pseudo confidence envelope
    } else {
       if (length(lty) == 1L)
          lty <- c(lty, lty)
@@ -94,11 +94,11 @@ label=FALSE, offset=0.3, pos=13, lty, ...) {
       ei  <- ImH %*% dat
       ei  <- apply(ei, 2, sort)
       if (bonferroni) {
-         lb <- apply(ei, 1, quantile,   (level/2)/x$k) ### consider using rowQuantiles() from matrixStats package
-         ub <- apply(ei, 1, quantile, 1-(level/2)/x$k) ### consider using rowQuantiles() from matrixStats package
+         lb <- apply(ei, 1, quantile,   (level/2)/x$k) # consider using rowQuantiles() from matrixStats package
+         ub <- apply(ei, 1, quantile, 1-(level/2)/x$k) # consider using rowQuantiles() from matrixStats package
       } else {
-         lb <- apply(ei, 1, quantile,   (level/2)) ### consider using rowQuantiles() from matrixStats package
-         ub <- apply(ei, 1, quantile, 1-(level/2)) ### consider using rowQuantiles() from matrixStats package
+         lb <- apply(ei, 1, quantile,   (level/2)) # consider using rowQuantiles() from matrixStats package
+         ub <- apply(ei, 1, quantile, 1-(level/2)) # consider using rowQuantiles() from matrixStats package
       }
 
       temp.lb <- qqnorm(lb, plot.it=FALSE)
diff --git a/R/radial.rma.r b/R/radial.rma.r
index e6db443b5..9c21f7286 100644
--- a/R/radial.rma.r
+++ b/R/radial.rma.r
@@ -43,8 +43,8 @@ transf, targs, pch=21, col, bg, back, arc.res=100, cex, cex.lab, cex.axis, ...)
       beta <- c(x$beta)
       ci.lb <- x$ci.lb
       ci.ub <- x$ci.ub
-      tau2 <- 1/mean(1/x$tau2) ### geometric mean of tau^2 values (hackish solution for models with multiple tau^2 values)
-                               ### note: this works for 1/mean(1/0) = 0; TODO: consider something more sophisticated here
+      tau2 <- 1/mean(1/x$tau2) # geometric mean of tau^2 values (hackish solution for models with multiple tau^2 values)
+                               # note: this works for 1/mean(1/0) = 0; TODO: consider something more sophisticated here
       if (is.null(aty)) {
          atyis <- range(yi)
       } else {
@@ -82,18 +82,18 @@ transf, targs, pch=21, col, bg, back, arc.res=100, cex, cex.lab, cex.axis, ...)
    ### set x-axis limits if none are specified
 
    if (missing(xlim)) {
-      xlims <- c(0, (1.30*max(xi)))                   ### add 30% to upper bound
+      xlims <- c(0, (1.30*max(xi)))                   # add 30% to upper bound
    } else {
       xlims <- sort(xlim)
    }
 
    ### x-axis position of the confidence interval
 
-   ci.xpos <- xlims[2] + 0.12*(xlims[2]-xlims[1])     ### add 12% of range to upper bound
+   ci.xpos <- xlims[2] + 0.12*(xlims[2]-xlims[1])     # add 12% of range to upper bound
 
    ### x-axis position of the y-axis on the right
 
-   ya.xpos <- xlims[2] + 0.14*(xlims[2]-xlims[1])     ### add 14% of range to upper bound
+   ya.xpos <- xlims[2] + 0.14*(xlims[2]-xlims[1])     # add 14% of range to upper bound
 
    xaxismax <- xlims[2]
 
diff --git a/R/ranef.rma.mv.r b/R/ranef.rma.mv.r
index 74ce6cd7e..0d08ad86b 100644
--- a/R/ranef.rma.mv.r
+++ b/R/ranef.rma.mv.r
@@ -60,7 +60,7 @@ ranef.rma.mv <- function(object, level, digits, transf, targs, verbose=FALSE, ..
 
    ### compute residuals
 
-   ei <- c(x$yi - x$X %*% x$beta) ### use this instead of resid(), since this guarantees that the length is correct
+   ei <- c(x$yi - x$X %*% x$beta) # use this instead of resid(), since this guarantees that the length is correct
 
    ### create identity matrix
 
diff --git a/R/ranef.rma.uni.r b/R/ranef.rma.uni.r
index 45de4c14a..db16766d8 100644
--- a/R/ranef.rma.uni.r
+++ b/R/ranef.rma.uni.r
@@ -52,7 +52,7 @@ ranef.rma.uni <- function(object, level, digits, transf, targs, ...) {
 
    li <- ifelse(is.infinite(x$tau2.f), 1, x$tau2.f / (x$tau2.f + x$vi.f))
 
-   for (i in seq_len(x$k.f)[x$not.na]) { ### note: skipping NA cases
+   for (i in seq_len(x$k.f)[x$not.na]) { # note: skipping NA cases
       Xi <- matrix(x$X.f[i,], nrow=1)
       if (is.element(x$method, c("FE","EE","CE"))) {
          pred[i]  <- 0
diff --git a/R/rma.mh.r b/R/rma.mh.r
index b86ef1f9e..ee7dfc2ea 100644
--- a/R/rma.mh.r
+++ b/R/rma.mh.r
@@ -1,6 +1,6 @@
 rma.mh   <- function(ai, bi, ci, di, n1i, n2i, x1i, x2i, t1i, t2i, measure="OR",
 data, slab, subset,
-add=1/2, to="only0", drop00=TRUE, ### for add/to/drop00, 1st element for escalc(), 2nd for MH method
+add=1/2, to="only0", drop00=TRUE, # for add/to/drop00, 1st element for escalc(), 2nd for MH method
 correct=TRUE, level=95, verbose=FALSE, digits, ...) {
 
    #########################################################################
@@ -117,7 +117,7 @@ correct=TRUE, level=95, verbose=FALSE, digits, ...) {
       if (is.null(di)) di <- n2i - ci
       ni <- ai + bi + ci + di
 
-      k <- length(ai) ### number of outcomes before subsetting
+      k <- length(ai) # number of outcomes before subsetting
       k.all <- k
 
       if (length(ai)==0L || length(bi)==0L || length(ci)==0L || length(di)==0L)
@@ -179,8 +179,8 @@ correct=TRUE, level=95, verbose=FALSE, digits, ...) {
       ### calculate observed effect estimates and sampling variances
 
       dat <- .do.call(escalc, measure=measure, ai=ai, bi=bi, ci=ci, di=di, add=add[1], to=to[1], drop00=drop00[1], onlyo1=onlyo1, addyi=addyi, addvi=addvi)
-      yi <- dat$yi ### one or more yi/vi pairs may be NA/NA
-      vi <- dat$vi ### one or more yi/vi pairs may be NA/NA
+      yi <- dat$yi # one or more yi/vi pairs may be NA/NA
+      vi <- dat$vi # one or more yi/vi pairs may be NA/NA
 
       ### if drop00[2]=TRUE, set counts to NA for studies that have no events (or all events) in both arms
 
@@ -201,7 +201,7 @@ correct=TRUE, level=95, verbose=FALSE, digits, ...) {
       vi.f <- vi
       ni.f <- ni
 
-      k.f <- k ### total number of tables including all NAs
+      k.f <- k # total number of tables including all NAs
 
       ### check for NAs in table data and act accordingly
 
@@ -249,8 +249,8 @@ correct=TRUE, level=95, verbose=FALSE, digits, ...) {
             ni <- ni[not.na.yivi]
             warning(mstyle$warning("Some yi/vi values are NA."), call.=FALSE)
 
-            attr(yi, "measure") <- measure ### add measure attribute back
-            attr(yi, "ni")      <- ni      ### add ni attribute back
+            attr(yi, "measure") <- measure # add measure attribute back
+            attr(yi, "ni")      <- ni      # add ni attribute back
 
          }
 
@@ -259,7 +259,7 @@ correct=TRUE, level=95, verbose=FALSE, digits, ...) {
 
       }
 
-      k.yi <- length(yi) ### number of yi/vi pairs that are not NA (needed for QE df and fit.stats calculation)
+      k.yi <- length(yi) # number of yi/vi pairs that are not NA (needed for QE df and fit.stats calculation)
 
       ### add/to procedures for the 2x2 tables for the actual meta-analysis
       ### note: technically, nothing needs to be added, but Stata/RevMan add 1/2 by default for only0 studies (but drop studies with no/all events)
@@ -326,7 +326,7 @@ correct=TRUE, level=95, verbose=FALSE, digits, ...) {
 
       ni  <- t1i + t2i
 
-      k <- length(x1i) ### number of outcomes before subsetting
+      k <- length(x1i) # number of outcomes before subsetting
       k.all <- k
 
       if (length(x1i)==0L || length(x2i)==0L || length(t1i)==0L || length(t2i)==0L)
@@ -385,8 +385,8 @@ correct=TRUE, level=95, verbose=FALSE, digits, ...) {
       ### calculate observed effect estimates and sampling variances
 
       dat <- .do.call(escalc, measure=measure, x1i=x1i, x2i=x2i, t1i=t1i, t2i=t2i, add=add[1], to=to[1], drop00=drop00[1], onlyo1=onlyo1, addyi=addyi, addvi=addvi)
-      yi  <- dat$yi ### one or more yi/vi pairs may be NA/NA
-      vi  <- dat$vi ### one or more yi/vi pairs may be NA/NA
+      yi  <- dat$yi # one or more yi/vi pairs may be NA/NA
+      vi  <- dat$vi # one or more yi/vi pairs may be NA/NA
 
       ### if drop00[2]=TRUE, set counts to NA for studies that have no events in both arms
 
@@ -405,7 +405,7 @@ correct=TRUE, level=95, verbose=FALSE, digits, ...) {
       vi.f <- vi
       ni.f <- ni
 
-      k.f <- k ### total number of tables including all NAs
+      k.f <- k # total number of tables including all NAs
 
       ### check for NAs in table data and act accordingly
 
@@ -453,8 +453,8 @@ correct=TRUE, level=95, verbose=FALSE, digits, ...) {
             ni <- ni[not.na.yivi]
             warning(mstyle$warning("Some yi/vi values are NA."), call.=FALSE)
 
-            attr(yi, "measure") <- measure ### add measure attribute back
-            attr(yi, "ni")      <- ni      ### add ni attribute back
+            attr(yi, "measure") <- measure # add measure attribute back
+            attr(yi, "ni")      <- ni      # add ni attribute back
 
          }
 
@@ -463,7 +463,7 @@ correct=TRUE, level=95, verbose=FALSE, digits, ...) {
 
       }
 
-      k.yi <- length(yi) ### number of yi/vi pairs that are not NA (needed for QE df and fitstats calculation)
+      k.yi <- length(yi) # number of yi/vi pairs that are not NA (needed for QE df and fitstats calculation)
 
       ### add/to procedures for the 2x2 tables for the actual meta-analysis
       ### note: technically, nothing needs to be added
@@ -539,7 +539,7 @@ correct=TRUE, level=95, verbose=FALSE, digits, ...) {
       } else {
          beta.exp <- R/S
          beta     <- log(beta.exp)
-         se       <- sqrt(1/2 * (sum(Pi*Ri)/R^2 + sum(Pi*Si + Qi*Ri)/(R*S) + sum(Qi*Si)/S^2)) ### based on Robins et al. (1986)
+         se       <- sqrt(1/2 * (sum(Pi*Ri)/R^2 + sum(Pi*Si + Qi*Ri)/(R*S) + sum(Qi*Si)/S^2)) # based on Robins et al. (1986)
          zval     <- beta / se
          pval     <- 2*pnorm(abs(zval), lower.tail=FALSE)
          ci.lb    <- beta - qnorm(level/2, lower.tail=FALSE) * se
@@ -632,8 +632,8 @@ correct=TRUE, level=95, verbose=FALSE, digits, ...) {
    if (measure == "RD") {
 
       beta  <- sum(ai*(n2i/Ni) - ci*(n1i/Ni)) / sum(n1i*(n2i/Ni))
-      se    <- sqrt((beta * (sum(ci*(n1i/Ni)^2 - ai*(n2i/Ni)^2 + (n1i/Ni)*(n2i/Ni)*(n2i-n1i)/2)) + sum(ai*(n2i-ci)/Ni + ci*(n1i-ai)/Ni)/2) / sum(n1i*(n2i/Ni))^2) ### equation in: Sato, Greenland, & Robins (1989)
-      #se   <- sqrt(sum(((ai/Ni^2)*bi*(n2i^2/n1i) + (ci/Ni^2)*di*(n1i^2/n2i))) / sum(n1i*(n2i/Ni))^2) ### equation in: Greenland & Robins (1985)
+      se    <- sqrt((beta * (sum(ci*(n1i/Ni)^2 - ai*(n2i/Ni)^2 + (n1i/Ni)*(n2i/Ni)*(n2i-n1i)/2)) + sum(ai*(n2i-ci)/Ni + ci*(n1i-ai)/Ni)/2) / sum(n1i*(n2i/Ni))^2) # equation in: Sato, Greenland, & Robins (1989)
+      #se   <- sqrt(sum(((ai/Ni^2)*bi*(n2i^2/n1i) + (ci/Ni^2)*di*(n1i^2/n2i))) / sum(n1i*(n2i/Ni))^2) # equation in: Greenland & Robins (1985)
       zval  <- beta / se
       pval  <- 2*pnorm(abs(zval), lower.tail=FALSE)
       ci.lb <- beta - qnorm(level/2, lower.tail=FALSE) * se
@@ -686,7 +686,7 @@ correct=TRUE, level=95, verbose=FALSE, digits, ...) {
    if (measure == "IRD") {
 
       beta  <- sum((x1i*t2i - x2i*t1i)/Ti) / sum((t1i/Ti)*t2i)
-      se    <- sqrt(sum(((t1i/Ti)*t2i)^2*(x1i/t1i^2+x2i/t2i^2))) / sum((t1i/Ti)*t2i) ### from Rothland et al. (2008), chapter 15
+      se    <- sqrt(sum(((t1i/Ti)*t2i)^2*(x1i/t1i^2+x2i/t2i^2))) / sum((t1i/Ti)*t2i) # from Rothland et al. (2008), chapter 15
       zval  <- beta / se
       pval  <- 2*pnorm(abs(zval), lower.tail=FALSE)
       ci.lb <- beta - qnorm(level/2, lower.tail=FALSE) * se
diff --git a/R/rma.mv.r b/R/rma.mv.r
index 12ca03e8f..8bb290f81 100644
--- a/R/rma.mv.r
+++ b/R/rma.mv.r
@@ -854,11 +854,11 @@ cvvc=FALSE, sparse=FALSE, verbose=FALSE, digits, control, ...) {
    W.f    <- A
    ni.f   <- ni
    mods.f <- mods
-   #mf.g.f <- mf.g ### copied further below
-   #mf.h.f <- mf.h ### copied further below
-   #mf.s.f <- mf.s ### copied further below
+   #mf.g.f <- mf.g # copied further below
+   #mf.h.f <- mf.h # copied further below
+   #mf.s.f <- mf.s # copied further below
 
-   k.f <- k ### total number of observed outcomes including all NAs
+   k.f <- k # total number of observed outcomes including all NAs
 
    #########################################################################
    #########################################################################
@@ -1074,7 +1074,7 @@ cvvc=FALSE, sparse=FALSE, verbose=FALSE, digits, control, ...) {
 
    }
 
-   #mf.s.f <- mf.s ### not needed at the moment
+   #mf.s.f <- mf.s # not needed at the moment
 
    ### copy s.nlevels and s.levels (needed for ranef())
 
@@ -1122,7 +1122,7 @@ cvvc=FALSE, sparse=FALSE, verbose=FALSE, digits, control, ...) {
 
    }
 
-   mf.g.f <- mf.g ### needed for predict()
+   mf.g.f <- mf.g # needed for predict()
 
    #########################################################################
 
@@ -1165,7 +1165,7 @@ cvvc=FALSE, sparse=FALSE, verbose=FALSE, digits, control, ...) {
 
    }
 
-   mf.h.f <- mf.h ### needed for predict()
+   mf.h.f <- mf.h # needed for predict()
 
    # return(list(Z.G1=Z.G1, Z.G2=Z.G2, g.nlevels=g.nlevels, g.levels=g.levels, g.values=g.values, tau2=tau2, rho=rho,
    #             Z.H1=Z.H1, Z.H2=Z.H2, h.nlevels=h.nlevels, h.levels=h.levels, h.values=h.values, gamma2=gamma2, phi=phi))
diff --git a/R/rma.peto.r b/R/rma.peto.r
index 03b24a98a..5504858cb 100644
--- a/R/rma.peto.r
+++ b/R/rma.peto.r
@@ -1,6 +1,6 @@
 rma.peto <- function(ai, bi, ci, di, n1i, n2i,
 data, slab, subset,
-add=1/2, to="only0", drop00=TRUE, ### for add/to/drop00, 1st element for escalc(), 2nd for Peto's method
+add=1/2, to="only0", drop00=TRUE, # for add/to/drop00, 1st element for escalc(), 2nd for Peto's method
 level=95, verbose=FALSE, digits, ...) {
 
    #########################################################################
@@ -48,7 +48,7 @@ level=95, verbose=FALSE, digits, ...) {
 
    .chkdots(ddd, c("outlist", "time"))
 
-   measure <- "PETO" ### set measure here so that it can be added below
+   measure <- "PETO" # set measure here so that it can be added below
 
    ### set defaults for digits
 
@@ -104,7 +104,7 @@ level=95, verbose=FALSE, digits, ...) {
    if (is.null(di)) di <- n2i - ci
    ni <- ai + bi + ci + di
 
-   k <- length(ai) ### number of outcomes before subsetting
+   k <- length(ai) # number of outcomes before subsetting
    k.all <- k
 
    if (length(ai)==0L || length(bi)==0L || length(ci)==0L || length(di)==0L)
@@ -166,8 +166,8 @@ level=95, verbose=FALSE, digits, ...) {
    ### calculate observed effect estimates and sampling variances
 
    dat <- .do.call(escalc, measure="PETO", ai=ai, bi=bi, ci=ci, di=di, add=add[1], to=to[1], drop00=drop00[1])
-   yi <- dat$yi ### one or more yi/vi pairs may be NA/NA
-   vi <- dat$vi ### one or more yi/vi pairs may be NA/NA
+   yi <- dat$yi # one or more yi/vi pairs may be NA/NA
+   vi <- dat$vi # one or more yi/vi pairs may be NA/NA
 
    ### if drop00[2]=TRUE, set counts to NA for studies that have no events (or all events) in both arms
 
@@ -188,7 +188,7 @@ level=95, verbose=FALSE, digits, ...) {
    vi.f <- vi
    ni.f <- ni
 
-   k.f <- k ### total number of tables including all NAs
+   k.f <- k # total number of tables including all NAs
 
    ### check for NAs in table data and act accordingly
 
@@ -236,8 +236,8 @@ level=95, verbose=FALSE, digits, ...) {
          ni <- ni[not.na.yivi]
          warning(mstyle$warning("Some yi/vi values are NA."), call.=FALSE)
 
-         attr(yi, "measure") <- measure ### add measure attribute back
-         attr(yi, "ni")      <- ni      ### add ni attribute back
+         attr(yi, "measure") <- measure # add measure attribute back
+         attr(yi, "ni")      <- ni      # add ni attribute back
 
       }
 
@@ -246,7 +246,7 @@ level=95, verbose=FALSE, digits, ...) {
 
    }
 
-   k.yi <- length(yi) ### number of yi/vi pairs that are not NA (needed for QE df and fit.stats calculation)
+   k.yi <- length(yi) # number of yi/vi pairs that are not NA (needed for QE df and fit.stats calculation)
 
    ### add/to procedures for the 2x2 tables for the actual meta-analysis
    ### note: technically, nothing needs to be added, but Stata/RevMan add 1/2 by default for only0 studies (but drop studies with no/all events)
@@ -305,14 +305,14 @@ level=95, verbose=FALSE, digits, ...) {
    if (verbose)
       message(mstyle$message("Model fitting ..."))
 
-   xt <- ai + ci ### frequency of outcome1 in both groups combined
-   yt <- bi + di ### frequency of outcome2 in both groups combined
+   xt <- ai + ci # frequency of outcome1 in both groups combined
+   yt <- bi + di # frequency of outcome2 in both groups combined
    Ei <- xt * n1i / Ni
-   Vi <- xt * yt * (n1i/Ni) * (n2i/Ni) / (Ni - 1) ### 0 when xt = 0 or yt = 0 in a table
+   Vi <- xt * yt * (n1i/Ni) * (n2i/Ni) / (Ni - 1) # 0 when xt = 0 or yt = 0 in a table
 
    sumVi <- sum(Vi)
 
-   if (sumVi == 0L) ### sumVi = 0 when xt or yt = 0 in *all* tables
+   if (sumVi == 0L) # sumVi = 0 when xt or yt = 0 in *all* tables
       stop(mstyle$stop("One of the two outcomes never occurred in any of the tables. Peto's method cannot be used."))
 
    beta  <- sum(ai - Ei) / sumVi
@@ -332,8 +332,8 @@ level=95, verbose=FALSE, digits, ...) {
    if (verbose)
       message(mstyle$message("Heterogeneity testing ..."))
 
-   k.pos <- sum(Vi > 0)    ### number of tables with positive sampling variance
-   Vi[Vi == 0] <- NA_real_ ### set 0 sampling variances to NA
+   k.pos <- sum(Vi > 0)    # number of tables with positive sampling variance
+   Vi[Vi == 0] <- NA_real_ # set 0 sampling variances to NA
    QE <- max(0, sum((ai - Ei)^2 / Vi, na.rm=TRUE) - sum(ai - Ei)^2 / sum(Vi, na.rm=TRUE))
 
    if (k.pos > 1L) {
diff --git a/R/rma.uni.r b/R/rma.uni.r
index 8da9bb219..c5cdd950e 100644
--- a/R/rma.uni.r
+++ b/R/rma.uni.r
@@ -337,7 +337,7 @@ test="z", level=95, btt, att, tau2, verbose=FALSE, digits, control, ...) {
       ### allow easy setting of vi to a single value
 
       if (length(vi) == 1L)
-         vi <- rep(vi, k) ### note: k is number of outcomes before subsetting
+         vi <- rep(vi, k) # note: k is number of outcomes before subsetting
 
       ### check length of yi and vi
 
@@ -900,7 +900,7 @@ test="z", level=95, btt, att, tau2, verbose=FALSE, digits, control, ...) {
    mods.f    <- mods
    Z.f       <- Z
 
-   k.f <- k ### total number of observed outcomes including all NAs
+   k.f <- k # total number of observed outcomes including all NAs
 
    ### check for NAs and act accordingly
 
@@ -1017,7 +1017,7 @@ test="z", level=95, btt, att, tau2, verbose=FALSE, digits, control, ...) {
       int.incl <- FALSE
    }
 
-   p <- NCOL(X) ### number of columns in X (including the intercept if it is included)
+   p <- NCOL(X) # number of columns in X (including the intercept if it is included)
 
    ### make sure variable names in X and Z are unique
 
@@ -1052,7 +1052,7 @@ test="z", level=95, btt, att, tau2, verbose=FALSE, digits, control, ...) {
    ### set/check 'btt' argument
 
    btt <- .set.btt(btt, p, int.incl, colnames(X))
-   m <- length(btt) ### number of betas to test (m = p if all betas are tested)
+   m <- length(btt) # number of betas to test (m = p if all betas are tested)
 
    #########################################################################
 
@@ -2187,7 +2187,7 @@ test="z", level=95, btt, att, tau2, verbose=FALSE, digits, control, ...) {
       ### set/check 'att' argument
 
       att <- .set.btt(att, q, Z.int.incl, colnames(Z))
-      m.alpha <- length(att) ### number of alphas to test (m = q if all alphas are tested)
+      m.alpha <- length(att) # number of alphas to test (m = q if all alphas are tested)
 
       ### ddf calculation
 
@@ -2453,7 +2453,7 @@ test="z", level=95, btt, att, tau2, verbose=FALSE, digits, control, ...) {
       if (k > p) {
 
          wi    <- 1/vi
-         W.FE  <- diag(wi, nrow=k, ncol=k) ### note: ll.REML below involves W, so cannot overwrite W
+         W.FE  <- diag(wi, nrow=k, ncol=k) # note: ll.REML below involves W, so cannot overwrite W
          stXWX <- .invcalc(X=X, W=W.FE, k=k)
          P     <- W.FE - W.FE %*% X %*% stXWX %*% crossprod(X,W.FE) # need P below for calculation of I^2
          QE    <- max(0, c(crossprod(Ymc,P) %*% Ymc))
diff --git a/R/rstandard.rma.mh.r b/R/rstandard.rma.mh.r
index e6f9f3534..ba5542c54 100644
--- a/R/rstandard.rma.mh.r
+++ b/R/rstandard.rma.mh.r
@@ -22,7 +22,7 @@ rstandard.rma.mh <- function(model, digits, ...) {
    resid <- c(x$yi.f - x$beta)
 
    resid[abs(resid) < 100 * .Machine$double.eps] <- 0
-   #resid[abs(resid) < 100 * .Machine$double.eps * median(abs(resid), na.rm=TRUE)] <- 0 ### see lm.influence
+   #resid[abs(resid) < 100 * .Machine$double.eps * median(abs(resid), na.rm=TRUE)] <- 0 # see lm.influence
 
    ### note: these are like Pearson (or semi-standardized) residuals
 
diff --git a/R/rstandard.rma.mv.r b/R/rstandard.rma.mv.r
index b6ca93834..ea73a4284 100644
--- a/R/rstandard.rma.mv.r
+++ b/R/rstandard.rma.mv.r
@@ -65,7 +65,7 @@ rstandard.rma.mv <- function(model, digits, cluster, ...) {
    ei <- c(x$yi - x$X %*% x$beta)
 
    ei[abs(ei) < 100 * .Machine$double.eps] <- 0
-   #ei[abs(ei) < 100 * .Machine$double.eps * median(abs(ei), na.rm=TRUE)] <- 0 ### see lm.influence
+   #ei[abs(ei) < 100 * .Machine$double.eps * median(abs(ei), na.rm=TRUE)] <- 0 # see lm.influence
 
    ### don't allow this; the SEs of the residuals cannot be estimated consistently for "robust.rma" objects
    #if (inherits(x, "robust.rma")) {
diff --git a/R/rstandard.rma.peto.r b/R/rstandard.rma.peto.r
index 8fcd665d4..484510328 100644
--- a/R/rstandard.rma.peto.r
+++ b/R/rstandard.rma.peto.r
@@ -22,7 +22,7 @@ rstandard.rma.peto <- function(model, digits, ...) {
    resid <- c(x$yi.f - x$beta)
 
    resid[abs(resid) < 100 * .Machine$double.eps] <- 0
-   #resid[abs(resid) < 100 * .Machine$double.eps * median(abs(resid), na.rm=TRUE)] <- 0 ### see lm.influence
+   #resid[abs(resid) < 100 * .Machine$double.eps * median(abs(resid), na.rm=TRUE)] <- 0 # see lm.influence
 
    ### note: these are like Pearson (or semi-standardized) residuals
 
diff --git a/R/rstandard.rma.uni.r b/R/rstandard.rma.uni.r
index c6e942e89..06d3c3197 100644
--- a/R/rstandard.rma.uni.r
+++ b/R/rstandard.rma.uni.r
@@ -42,7 +42,7 @@ rstandard.rma.uni <- function(model, digits, type="marginal", ...) {
       ei <- c(x$yi - x$X %*% x$beta)
 
       ei[abs(ei) < 100 * .Machine$double.eps] <- 0
-      #ei[abs(ei) < 100 * .Machine$double.eps * median(abs(ei), na.rm=TRUE)] <- 0 ### see lm.influence
+      #ei[abs(ei) < 100 * .Machine$double.eps * median(abs(ei), na.rm=TRUE)] <- 0 # see lm.influence
 
       ### don't allow this; the SEs of the residuals cannot be estimated consistently for "robust.rma" objects
       #if (inherits(x, "robust.rma")) {
diff --git a/R/rstudent.rma.mh.r b/R/rstudent.rma.mh.r
index 4f8618ba2..0d73bf71f 100644
--- a/R/rstudent.rma.mh.r
+++ b/R/rstudent.rma.mh.r
@@ -68,7 +68,7 @@ rstudent.rma.mh <- function(model, digits, progbar=FALSE, ...) {
 
    resid <- x$yi.f - delpred
    resid[abs(resid) < 100 * .Machine$double.eps] <- 0
-   #resid[abs(resid) < 100 * .Machine$double.eps * median(abs(resid), na.rm=TRUE)] <- 0 ### see lm.influence
+   #resid[abs(resid) < 100 * .Machine$double.eps * median(abs(resid), na.rm=TRUE)] <- 0 # see lm.influence
    seresid <- sqrt(x$vi.f + vdelpred)
    stresid <- resid / seresid
 
diff --git a/R/rstudent.rma.peto.r b/R/rstudent.rma.peto.r
index 810dd993d..f41aef070 100644
--- a/R/rstudent.rma.peto.r
+++ b/R/rstudent.rma.peto.r
@@ -62,7 +62,7 @@ rstudent.rma.peto <- function(model, digits, progbar=FALSE, ...) {
 
    resid <- x$yi.f - delpred
    resid[abs(resid) < 100 * .Machine$double.eps] <- 0
-   #resid[abs(resid) < 100 * .Machine$double.eps * median(abs(resid), na.rm=TRUE)] <- 0 ### see lm.influence
+   #resid[abs(resid) < 100 * .Machine$double.eps * median(abs(resid), na.rm=TRUE)] <- 0 # see lm.influence
    seresid <- sqrt(x$vi.f + vdelpred)
    stresid <- resid / seresid
 
diff --git a/R/to.long.r b/R/to.long.r
index eac809f27..aba5936d7 100644
--- a/R/to.long.r
+++ b/R/to.long.r
@@ -116,7 +116,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, vlong=FALSE, append=TRUE,
       if (!.all.specified(ai, bi, ci, di))
          stop(mstyle$stop("Cannot compute outcomes. Check that all of the required information is specified\n  via the appropriate arguments (i.e., ai, bi, ci, di or ai, n1i, ci, n2i)."))
 
-      k <- length(ai) ### number of outcomes before subsetting
+      k <- length(ai) # number of outcomes before subsetting
 
       if (!is.null(subset)) {
          subset <- .chksubset(subset, k)
@@ -138,7 +138,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, vlong=FALSE, append=TRUE,
       if (any(c(n1i < 0, n2i < 0), na.rm=TRUE))
          stop(mstyle$stop("One or more group sizes are negative."))
 
-      ni.u <- ai + bi + ci + di ### unadjusted total sample sizes
+      ni.u <- ai + bi + ci + di # unadjusted total sample sizes
 
       ### if drop00=TRUE, set counts to NA for studies that have no events (or all events) in both arms
 
@@ -211,7 +211,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, vlong=FALSE, append=TRUE,
       if (!.equal.length(x1i, x2i, t1i, t2i))
          stop(mstyle$stop("Supplied data vectors are not all of the same length."))
 
-      k <- length(x1i) ### number of outcomes before subsetting
+      k <- length(x1i) # number of outcomes before subsetting
 
       if (!is.null(subset)) {
          subset <- .chksubset(subset, k)
@@ -227,7 +227,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, vlong=FALSE, append=TRUE,
       if (any(c(t1i, t2i) <= 0, na.rm=TRUE))
          stop(mstyle$stop("One or more person-times are <= 0."))
 
-      ni.u <- t1i + t2i ### unadjusted total sample sizes
+      ni.u <- t1i + t2i # unadjusted total sample sizes
 
       ### if drop00=TRUE, set counts to NA for studies that have no events in both arms
 
@@ -294,7 +294,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, vlong=FALSE, append=TRUE,
       if (!.equal.length(m1i, m2i, sd1i, sd2i, n1i, n2i))
          stop(mstyle$stop("Supplied data vectors are not all of the same length."))
 
-      k <- length(n1i) ### number of outcomes before subsetting
+      k <- length(n1i) # number of outcomes before subsetting
 
       if (!is.null(subset)) {
          subset <- .chksubset(subset, k)
@@ -312,7 +312,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, vlong=FALSE, append=TRUE,
       if (any(c(n1i, n2i) <= 0, na.rm=TRUE))
          stop(mstyle$stop("One or more group sizes are <= 0."))
 
-      ni.u <- n1i + n2i ### unadjusted total sample sizes
+      ni.u <- n1i + n2i # unadjusted total sample sizes
 
    }
 
@@ -332,7 +332,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, vlong=FALSE, append=TRUE,
       if (!.all.specified(ri, ni))
          stop(mstyle$stop("Cannot compute outcomes. Check that all of the required information is specified\n  via the appropriate arguments (i.e., ri, ni)."))
 
-      k <- length(ri) ### number of outcomes before subsetting
+      k <- length(ri) # number of outcomes before subsetting
 
       if (!is.null(subset)) {
          subset <- .chksubset(subset, k=k)
@@ -346,7 +346,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, vlong=FALSE, append=TRUE,
       if (any(ni <= 0, na.rm=TRUE))
          stop(mstyle$stop("One or more sample sizes are <= 0."))
 
-      ni.u <- ni ### unadjusted total sample sizes
+      ni.u <- ni # unadjusted total sample sizes
 
    }
 
@@ -371,7 +371,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, vlong=FALSE, append=TRUE,
       if (!.all.specified(xi, mi))
          stop(mstyle$stop("Cannot compute outcomes. Check that all of the required information is specified\n  via the appropriate arguments (i.e., xi, mi or xi, ni)."))
 
-      k <- length(xi) ### number of outcomes before subsetting
+      k <- length(xi) # number of outcomes before subsetting
 
       if (!is.null(subset)) {
          subset <- .chksubset(subset, k)
@@ -390,7 +390,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, vlong=FALSE, append=TRUE,
       if (any(ni <= 0, na.rm=TRUE))
          stop(mstyle$stop("One or more group sizes are <= 0."))
 
-      ni.u <- ni ### unadjusted total sample sizes
+      ni.u <- ni # unadjusted total sample sizes
 
       if (to == "all") {
 
@@ -444,7 +444,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, vlong=FALSE, append=TRUE,
       if (!.equal.length(xi, ti))
          stop(mstyle$stop("Supplied data vectors are not all of the same length."))
 
-      k <- length(xi) ### number of outcomes before subsetting
+      k <- length(xi) # number of outcomes before subsetting
 
       if (!is.null(subset)) {
          subset <- .chksubset(subset, k)
@@ -458,7 +458,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, vlong=FALSE, append=TRUE,
       if (any(ti <= 0, na.rm=TRUE))
          stop(mstyle$stop("One or more person-times are <= 0."))
 
-      ni.u <- ti ### unadjusted total sample sizes
+      ni.u <- ti # unadjusted total sample sizes
 
       if (to == "all") {
 
@@ -510,7 +510,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, vlong=FALSE, append=TRUE,
       if (!.equal.length(mi, sdi, ni))
          stop(mstyle$stop("Supplied data vectors are not all of the same length."))
 
-      k <- length(ni) ### number of outcomes before subsetting
+      k <- length(ni) # number of outcomes before subsetting
 
       if (!is.null(subset)) {
          subset <- .chksubset(subset, k)
@@ -528,7 +528,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, vlong=FALSE, append=TRUE,
       if (is.element(measure, c("MNLN","CVLN")) && any(mi < 0, na.rm=TRUE))
          stop(mstyle$stop("One or more means are negative."))
 
-      ni.u <- ni ### unadjusted total sample sizes
+      ni.u <- ni # unadjusted total sample sizes
 
   }
 
@@ -540,10 +540,10 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, vlong=FALSE, append=TRUE,
       m2i  <- .getx("m2i",  mf=mf, data=data, checknumeric=TRUE)
       sd1i <- .getx("sd1i", mf=mf, data=data, checknumeric=TRUE)
       sd2i <- .getx("sd2i", mf=mf, data=data, checknumeric=TRUE)
-      ri   <- .getx("ri",   mf=mf, data=data, checknumeric=TRUE) ### for SMCR, do not need to supply this
+      ri   <- .getx("ri",   mf=mf, data=data, checknumeric=TRUE) # for SMCR, do not need to supply this
       ni   <- .getx("ni",   mf=mf, data=data, checknumeric=TRUE)
 
-      k <- length(m1i) ### number of outcomes before subsetting
+      k <- length(m1i) # number of outcomes before subsetting
 
       if (is.element(measure, c("MC","SMCC","SMCRH","ROMC","CVRC"))) {
 
@@ -587,7 +587,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, vlong=FALSE, append=TRUE,
       if (any(ni <= 0, na.rm=TRUE))
          stop(mstyle$stop("One or more sample sizes are <= 0."))
 
-      ni.u <- ni ### unadjusted total sample sizes
+      ni.u <- ni # unadjusted total sample sizes
 
    }
 
@@ -605,7 +605,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, vlong=FALSE, append=TRUE,
       if (!.equal.length(ai, mi, ni))
          stop(mstyle$stop("Supplied data vectors are not all of the same length."))
 
-      k <- length(ai) ### number of outcomes before subsetting
+      k <- length(ai) # number of outcomes before subsetting
 
       if (!is.null(subset)) {
          subset <- .chksubset(subset, k)
@@ -623,7 +623,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, vlong=FALSE, append=TRUE,
       if (any(ni <= 0, na.rm=TRUE))
          stop(mstyle$stop("One or more sample sizes are <= 0."))
 
-      ni.u <- ni ### unadjusted total sample sizes
+      ni.u <- ni # unadjusted total sample sizes
 
    }
 
diff --git a/R/to.table.r b/R/to.table.r
index b565ffc13..f7e08afdd 100644
--- a/R/to.table.r
+++ b/R/to.table.r
@@ -88,7 +88,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, rows, cols) {
       if (!.all.specified(ai, bi, ci, di))
          stop(mstyle$stop("Cannot compute outcomes. Check that all of the required information is specified\n  via the appropriate arguments (i.e., ai, bi, ci, di or ai, n1i, ci, n2i)."))
 
-      k <- length(ai) ### number of outcomes before subsetting
+      k <- length(ai) # number of outcomes before subsetting
 
       if (!is.null(subset)) {
          subset <- .chksubset(subset, k)
@@ -110,7 +110,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, rows, cols) {
       if (any(c(n1i < 0, n2i < 0), na.rm=TRUE))
          stop(mstyle$stop("One or more group sizes are negative."))
 
-      ni.u <- ai + bi + ci + di ### unadjusted total sample sizes
+      ni.u <- ai + bi + ci + di # unadjusted total sample sizes
 
       ### if drop00=TRUE, set counts to NA for studies that have no events (or all events) in both arms
 
@@ -183,7 +183,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, rows, cols) {
       if (!.equal.length(x1i, x2i, t1i, t2i))
          stop(mstyle$stop("Supplied data vectors are not all of the same length."))
 
-      k <- length(x1i) ### number of outcomes before subsetting
+      k <- length(x1i) # number of outcomes before subsetting
 
       if (!is.null(subset)) {
          subset <- .chksubset(subset, k)
@@ -199,7 +199,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, rows, cols) {
       if (any(c(t1i, t2i) <= 0, na.rm=TRUE))
          stop(mstyle$stop("One or more person-times are <= 0."))
 
-      ni.u <- t1i + t2i ### unadjusted total sample sizes
+      ni.u <- t1i + t2i # unadjusted total sample sizes
 
       ### if drop00=TRUE, set counts to NA for studies that have no events in both arms
 
@@ -266,7 +266,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, rows, cols) {
       if (!.equal.length(m1i, m2i, sd1i, sd2i, n1i, n2i))
          stop(mstyle$stop("Supplied data vectors are not all of the same length."))
 
-      k <- length(n1i) ### number of outcomes before subsetting
+      k <- length(n1i) # number of outcomes before subsetting
 
       if (!is.null(subset)) {
          subset <- .chksubset(subset, k)
@@ -284,7 +284,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, rows, cols) {
       if (any(c(n1i, n2i) <= 0, na.rm=TRUE))
          stop(mstyle$stop("One or more group sizes are <= 0."))
 
-      ni.u <- n1i + n2i ### unadjusted total sample sizes
+      ni.u <- n1i + n2i # unadjusted total sample sizes
 
    }
 
@@ -304,7 +304,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, rows, cols) {
       if (!.all.specified(ri, ni))
          stop(mstyle$stop("Cannot compute outcomes. Check that all of the required information is specified\n  via the appropriate arguments (i.e., ri, ni)."))
 
-      k <- length(ri) ### number of outcomes before subsetting
+      k <- length(ri) # number of outcomes before subsetting
 
       if (!is.null(subset)) {
          subset <- .chksubset(subset, k)
@@ -318,7 +318,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, rows, cols) {
       if (any(ni <= 0, na.rm=TRUE))
          stop(mstyle$stop("One or more sample sizes are <= 0."))
 
-      ni.u <- ni ### unadjusted total sample sizes
+      ni.u <- ni # unadjusted total sample sizes
 
    }
 
@@ -343,7 +343,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, rows, cols) {
       if (!.all.specified(xi, mi))
          stop(mstyle$stop("Cannot compute outcomes. Check that all of the required information is specified\n  via the appropriate arguments (i.e., xi, mi or xi, ni)."))
 
-      k <- length(xi) ### number of outcomes before subsetting
+      k <- length(xi) # number of outcomes before subsetting
 
       if (!is.null(subset)) {
          subset <- .chksubset(subset, k)
@@ -362,7 +362,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, rows, cols) {
       if (any(ni <= 0, na.rm=TRUE))
          stop(mstyle$stop("One or more group sizes are <= 0."))
 
-      ni.u <- ni ### unadjusted total sample sizes
+      ni.u <- ni # unadjusted total sample sizes
 
       if (to == "all") {
 
@@ -416,7 +416,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, rows, cols) {
       if (!.equal.length(xi, ti))
          stop(mstyle$stop("Supplied data vectors are not all of the same length."))
 
-      k <- length(xi) ### number of outcomes before subsetting
+      k <- length(xi) # number of outcomes before subsetting
 
       if (!is.null(subset)) {
          subset <- .chksubset(subset, k)
@@ -430,7 +430,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, rows, cols) {
       if (any(ti <= 0, na.rm=TRUE))
          stop(mstyle$stop("One or more person-times are <= 0."))
 
-      ni.u <- ti ### unadjusted total sample sizes
+      ni.u <- ti # unadjusted total sample sizes
 
       if (to == "all") {
 
@@ -482,7 +482,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, rows, cols) {
       if (!.equal.length(mi, sdi, ni))
          stop(mstyle$stop("Supplied data vectors are not all of the same length."))
 
-      k <- length(ni) ### number of outcomes before subsetting
+      k <- length(ni) # number of outcomes before subsetting
 
       if (!is.null(subset)) {
          subset <- .chksubset(subset, k)
@@ -500,7 +500,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, rows, cols) {
       if (is.element(measure, c("MNLN","CVLN")) && any(mi < 0, na.rm=TRUE))
          stop(mstyle$stop("One or more means are negative."))
 
-      ni.u <- ni ### unadjusted total sample sizes
+      ni.u <- ni # unadjusted total sample sizes
 
   }
 
@@ -512,10 +512,10 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, rows, cols) {
       m2i  <- .getx("m2i",  mf=mf, data=data, checknumeric=TRUE)
       sd1i <- .getx("sd1i", mf=mf, data=data, checknumeric=TRUE)
       sd2i <- .getx("sd2i", mf=mf, data=data, checknumeric=TRUE)
-      ri   <- .getx("ri",   mf=mf, data=data, checknumeric=TRUE) ### for SMCR, do not need to supply this
+      ri   <- .getx("ri",   mf=mf, data=data, checknumeric=TRUE) # for SMCR, do not need to supply this
       ni   <- .getx("ni",   mf=mf, data=data, checknumeric=TRUE)
 
-      k <- length(m1i) ### number of outcomes before subsetting
+      k <- length(m1i) # number of outcomes before subsetting
 
       if (is.element(measure, c("MC","SMCC","SMCRH","ROMC","CVRC"))) {
 
@@ -559,7 +559,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, rows, cols) {
       if (any(ni <= 0, na.rm=TRUE))
          stop(mstyle$stop("One or more sample sizes are <= 0."))
 
-      ni.u <- ni ### unadjusted total sample sizes
+      ni.u <- ni # unadjusted total sample sizes
 
    }
 
@@ -577,7 +577,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, rows, cols) {
       if (!.equal.length(ai, mi, ni))
          stop(mstyle$stop("Supplied data vectors are not all of the same length."))
 
-      k <- length(ai) ### number of outcomes before subsetting
+      k <- length(ai) # number of outcomes before subsetting
 
       if (!is.null(subset)) {
          subset <- .chksubset(subset, k)
@@ -595,7 +595,7 @@ data, slab, subset, add=1/2, to="none", drop00=FALSE, rows, cols) {
       if (any(ni <= 0, na.rm=TRUE))
          stop(mstyle$stop("One or more sample sizes are <= 0."))
 
-      ni.u <- ni ### unadjusted total sample sizes
+      ni.u <- ni # unadjusted total sample sizes
 
    }
 
diff --git a/R/trimfill.rma.uni.r b/R/trimfill.rma.uni.r
index 3aa93519b..f8dca6070 100644
--- a/R/trimfill.rma.uni.r
+++ b/R/trimfill.rma.uni.r
@@ -57,15 +57,15 @@ trimfill.rma.uni <- function(x, side, estimator="L0", maxiter=100, verbose=FALSE
    k <- length(yi)
 
    k0.sav <- -1
-   k0     <-  0 ### estimated number of missing studies
-   iter   <-  0 ### iteration counter
+   k0     <-  0 # estimated number of missing studies
+   iter   <-  0 # iteration counter
 
    if (verbose)
       cat("\n")
 
    while (abs(k0 - k0.sav) > 0) {
 
-      k0.sav <- k0 ### save current value of k0
+      k0.sav <- k0 # save current value of k0
 
       iter <- iter + 1
 
@@ -85,9 +85,9 @@ trimfill.rma.uni <- function(x, side, estimator="L0", maxiter=100, verbose=FALSE
 
       beta <- c(res$beta)
 
-      yi.c     <- yi - beta                            ### centered values
-      yi.c.r   <- rank(abs(yi.c), ties.method="first") ### ranked absolute centered values
-      yi.c.r.s <- sign(yi.c) * yi.c.r                  ### signed ranked centered values
+      yi.c     <- yi - beta                            # centered values
+      yi.c.r   <- rank(abs(yi.c), ties.method="first") # ranked absolute centered values
+      yi.c.r.s <- sign(yi.c) * yi.c.r                  # signed ranked centered values
 
       ### estimate the number of missing studies with the R0 estimator
 
diff --git a/R/zzz.r b/R/zzz.r
index fa631dca9..b4de64050 100644
--- a/R/zzz.r
+++ b/R/zzz.r
@@ -1,6 +1,6 @@
 .onAttach <- function(libname, pkgname) {
 
-   ver <- "4.5-13"
+   ver <- "4.6-0"
 
    loadmsg <- paste0("\nLoading the 'metafor' package (version ", ver, "). For an\nintroduction to the package please type: help(metafor)\n")