diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml
index 77ee09f2..07c7735e 100644
--- a/.github/workflows/test-coverage.yaml
+++ b/.github/workflows/test-coverage.yaml
@@ -49,7 +49,7 @@ jobs:
           covr::to_cobertura(cov)
         shell: Rscript {0}
 
-      - uses: codecov/codecov-action@v6
+      - uses: codecov/codecov-action@v7
         with:
           # Fail if error if not on PR, or if on PR and token is given
           fail_ci_if_error: ${{ github.event_name != 'pull_request' || secrets.CODECOV_TOKEN }}
diff --git a/DESCRIPTION b/DESCRIPTION
index 000d8749..03200344 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: TwoSampleMR
 Title: Two Sample MR Functions and Interface to MRC Integrative
     Epidemiology Unit OpenGWAS Database
-Version: 0.7.7
+Version: 0.7.8
 Authors@R: c(
     person("Gibran", "Hemani", , "g.hemani@bristol.ac.uk", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-0920-1055")),
diff --git a/NEWS.md b/NEWS.md
index 100dbb4c..ad040606 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,20 @@
+# TwoSampleMR v0.7.8
+
+(Release date 2026-06-10)
+
+* Fixed a copy-paste bug in `ldsc_rg()` which passed `d1$l2` instead of `d2$l2` as the weight vector to the trait-2 `ldsc_h2_internal()` call
+* Fixed `extract_split()` chunking which gave `nsplit = 0` for SNP lists smaller than half the `splitsize`; now uses `ceiling()` with contiguous chunk ids
+* Fixed the penalised mode in `mr_mode()` for vector-valued `phi`: each SNP is now penalised against the weighted-mode estimate for the same `phi` rather than recycling the mode vector across SNPs (results unchanged for the default scalar `phi = 1`)
+* Fixed row order restoration in `add_metadata()` to use `dat[order(dat[[order_col]]), ]`
+* Qualified a bare `aes()` call as `ggplot2::aes()` in `test_r_from_pn()`
+* Used `isTRUE(all.equal())` instead of `all.equal() == TRUE` inside `ifelse()` for null-line detection in the forest plot functions, which could otherwise return a multi-element vector
+* Simplified the group `Index` assignment in `forest_plot_1_to_many()` using `Letters[match()]`, which is also robust to non-contiguous group rows
+* Collapsed a duplicated `||` condition in `extract_outcome_data_internal()`
+* Removed dead `TAUsq` accumulation in `PM()` in `rucker.R`
+* Fixed an error message typo in `combine_data()` and switched its `sapply()` to `vapply()`
+* Replaced superseded `tidyr::gather()` with `tidyr::pivot_longer()` in `test_r_from_pn()` and dropped a redundant `requireNamespace("tidyr")` guard
+* Lint package with Jarl
+
 # TwoSampleMR v0.7.7
 
 (Release date 2026-06-07)
diff --git a/R/add_metadata.r b/R/add_metadata.r
index c50acf5b..c9b04923 100644
--- a/R/add_metadata.r
+++ b/R/add_metadata.r
@@ -75,7 +75,7 @@ add_metadata <- function(dat, cols = c("sample_size", "ncase", "ncontrol", "unit
   names(dat)[names(dat) == "unit.exposure"] <- "units.exposure"
   names(dat)[names(dat) == "unit.outcome"] <- "units.outcome"
 
-  dat <- dat[dat[[order_col]], ]
+  dat <- dat[order(dat[[order_col]]), ]
   dat <- dat[, !names(dat) %in% order_col]
   # dat <- fix_ukb_d(dat)
   return(dat)
diff --git a/R/add_rsq.r b/R/add_rsq.r
index ec333821..818b5eae 100644
--- a/R/add_rsq.r
+++ b/R/add_rsq.r
@@ -73,7 +73,7 @@ add_rsq_one <- function(dat, what = "exposure") {
         message("Try adding metadata with add_metadata()")
       }
     } else if (
-      all(grepl("SD", dat[[paste0("units.", what)]])) && all(!is.na(dat[[paste0("eaf.", what)]]))
+      all(grepl("SD", dat[[paste0("units.", what)]])) && !anyNA(dat[[paste0("eaf.", what)]])
     ) {
       dat[[paste0("rsq.", what)]] <- NA
       dat[[paste0("rsq.", what)]] <- 2 *
@@ -115,13 +115,6 @@ get_r_from_pn_less_accurate <- function(p, n) {
 }
 
 test_r_from_pn <- function() {
-  if (!requireNamespace("tidyr", quietly = TRUE)) {
-    stop(
-      "Package \"tidyr\" must be installed to use this function.",
-      call. = FALSE
-    )
-  }
-
   param <- expand.grid(
     n = c(10, 100, 1000, 10000, 100000),
     rsq = 10^seq(-4, -0.5, length.out = 30)
@@ -137,11 +130,16 @@ test_r_from_pn <- function() {
     param$rsq2[i] <- (get_r_from_pn(param$pval[i], param$n[i]))^2
   }
 
-  param <- tidyr::gather(param, key = out, value = value, rsq1, rsq2)
+  param <- tidyr::pivot_longer(
+    param,
+    cols = c("rsq1", "rsq2"),
+    names_to = "out",
+    values_to = "value"
+  )
 
   p <- ggplot2::ggplot(param, ggplot2::aes(x = rsq_emp, value)) +
     ggplot2::geom_abline(slope = 1, linetype = "dotted") +
-    ggplot2::geom_line(aes(colour = out)) +
+    ggplot2::geom_line(ggplot2::aes(colour = out)) +
     ggplot2::facet_grid(. ~ n) +
     ggplot2::scale_x_log10() +
     ggplot2::scale_y_log10()
diff --git a/R/forest_plot.R b/R/forest_plot.R
index 2523d76d..676da589 100644
--- a/R/forest_plot.R
+++ b/R/forest_plot.R
@@ -323,7 +323,7 @@ mr_forest_plot_grouped <-
     }
 
     ## act on debug flag
-    if (debug == FALSE) {
+    if (!debug) {
       options(warn = -1)
     }
 
@@ -372,7 +372,7 @@ mr_forest_plot_grouped <-
     ## draw and export the annotated forest plot
     grid.newpage()
     grid.draw(group)
-    if (returnRobj == FALSE) {
+    if (!returnRobj) {
       grDevices::pdf(outfile_Name, width = 23.4, height = 16.5)
       grid.draw(group)
       grDevices::dev.off()
diff --git a/R/forest_plot2.R b/R/forest_plot2.R
index 90200c00..645b5e7c 100644
--- a/R/forest_plot2.R
+++ b/R/forest_plot2.R
@@ -242,7 +242,7 @@ forest_plot_basic <- function(
   # OR or log(OR)?
   # If CI are symmetric then log(OR)
   # Use this to guess where to put the null line
-  null_line <- ifelse(all.equal(dat$effect - dat$lo_ci, dat$up_ci - dat$effect) == TRUE, 0, 1)
+  null_line <- if (isTRUE(all.equal(dat$effect - dat$lo_ci, dat$up_ci - dat$effect))) 0 else 1
 
   # Change lab
   if (!is.null(xlim)) {
@@ -435,7 +435,7 @@ forest_plot_names <- function(dat, section = NULL, bottom = TRUE) {
   # OR or log(OR)?
   # If CI are symmetric then log(OR)
   # Use this to guess where to put the null line
-  null_line <- ifelse(all.equal(dat$effect - dat$lo_ci, dat$up_ci - dat$effect) == TRUE, 0, 1)
+  null_line <- if (isTRUE(all.equal(dat$effect - dat$lo_ci, dat$up_ci - dat$effect))) 0 else 1
 
   # up <- max(dat$up_ci, na.rm=TRUE)
   # lo <- min(dat$lo_ci, na.rm=TRUE)
diff --git a/R/forest_plot_1-to-many.R b/R/forest_plot_1-to-many.R
index c4e98568..18918059 100644
--- a/R/forest_plot_1-to-many.R
+++ b/R/forest_plot_1-to-many.R
@@ -213,9 +213,9 @@ sort_1_to_many <- function(
     )
     Letters <- sort(c(paste0("A", Letters), paste0("B", Letters), paste0("C", Letters)))
     groups <- unique(mr_res[, group])
-    mr_res$Index <- unlist(lapply(seq_along(unique(mr_res[, group])), FUN = function(x) {
-      rep(Letters[Letters == Letters[x]], length(which(mr_res[, group] == groups[x])))
-    }))
+    # Assign each row the letter code for its group; match() makes this robust
+    # to groups whose rows are not contiguous in mr_res.
+    mr_res$Index <- Letters[match(mr_res[, group], groups)]
     mr_res <- mr_res[order(mr_res[, b], decreasing = TRUE), ]
     mr_res$Index2 <- Letters[seq_len(nrow(mr_res))]
     mr_res$Index3 <- paste(mr_res$Index, mr_res$Index2, sep = "")
@@ -329,7 +329,7 @@ forest_plot_basic2 <- function(
   # OR or log(OR)?
   # If CI are symmetric then log(OR)
   # Use this to guess where to put the null line
-  null_line <- ifelse(all.equal(dat$effect - dat$lo_ci, dat$up_ci - dat$effect) == TRUE, 0, 1)
+  null_line <- if (isTRUE(all.equal(dat$effect - dat$lo_ci, dat$up_ci - dat$effect))) 0 else 1
 
   # Change lab
   if (!is.null(xlim)) {
@@ -565,7 +565,7 @@ forest_plot_names2 <- function(
   # OR or log(OR)?
   # If CI are symmetric then log(OR)
   # Use this to guess where to put the null line
-  null_line <- ifelse(all.equal(dat$effect - dat$lo_ci, dat$up_ci - dat$effect) == TRUE, 0, 1)
+  null_line <- if (isTRUE(all.equal(dat$effect - dat$lo_ci, dat$up_ci - dat$effect))) 0 else 1
 
   # up <- max(dat$up_ci, na.rm=TRUE)
   # lo <- min(dat$lo_ci, na.rm=TRUE)
@@ -683,7 +683,7 @@ forest_plot_addcol <- function(
   # OR or log(OR)?
   # If CI are symmetric then log(OR)
   # Use this to guess where to put the null line
-  null_line <- ifelse(all.equal(dat$effect - dat$lo_ci, dat$up_ci - dat$effect) == TRUE, 0, 1)
+  null_line <- if (isTRUE(all.equal(dat$effect - dat$lo_ci, dat$up_ci - dat$effect))) 0 else 1
 
   lo <- 0
   up <- 1
diff --git a/R/format_mr_results2.R b/R/format_mr_results2.R
index 683e0457..1ca73613 100644
--- a/R/format_mr_results2.R
+++ b/R/format_mr_results2.R
@@ -306,7 +306,7 @@ power_prune <- function(dat, method = 1, dist.outcome = "binary") {
       if (is.null(ncase)) {
         ncase <- NA
       }
-      if (any(is.na(ncase))) {
+      if (anyNA(ncase)) {
         ncase <- dat1$samplesize.outcome
         if (dist.outcome == "binary") {
           warning(paste(
@@ -314,18 +314,22 @@ power_prune <- function(dat, method = 1, dist.outcome = "binary") {
           ))
         }
       }
-      if (any(is.na(ncase))) {
+      if (anyNA(ncase)) {
         stop("sample size missing for at least 1 summary set")
       }
-      dat1 <- dat1[order(ncase, decreasing = TRUE), ]
+      # Permute dat1 and ncase identically; sort() would drop NA values and
+      # misalign ncase from dat1's rows.
+      ncase_ord <- order(ncase, decreasing = TRUE)
+      dat1 <- dat1[ncase_ord, ]
       # id.expout<-paste(split_exposure(dat)$exposure,split_outcome(dat)$outcome)
-      ncase <- ncase[order(ncase, decreasing = TRUE)]
+      ncase <- ncase[ncase_ord]
       # dat1$power.prune.ncase<-"drop"
       # dat1$power.prune.ncase[ncase==ncase[1]]<-"keep"
       dat1 <- dat1[ncase == ncase[1], ]
       nexp <- dat1$samplesize.exposure
-      dat1 <- dat1[order(nexp, decreasing = TRUE), ]
-      nexp <- nexp[order(nexp, decreasing = TRUE)]
+      nexp_ord <- order(nexp, decreasing = TRUE)
+      dat1 <- dat1[nexp_ord, ]
+      nexp <- nexp[nexp_ord]
       # dat1$power.prune.nexp<-"drop"
       # dat1$power.prune.nexp[nexp==nexp[1]]<-"keep"
       # dat1$power.prune<-"drop"
@@ -365,14 +369,14 @@ power_prune <- function(dat, method = 1, dist.outcome = "binary") {
         z <- dat2$beta.exposure / dat2$se.exposure
         n <- dat2$samplesize.exposure
         b <- z / sqrt(2 * p * (1 - p) * (n + z^2))
-        if (any(is.na(dat2$ncase.outcome))) {
+        if (anyNA(dat2$ncase.outcome)) {
           stop(paste("number of cases missing for summary set: ", id.subset.unique[j], sep = ""))
         }
         n.cas <- dat2$ncase.outcome
         n.con <- dat2$ncontrol.outcome
         var <- 1 # variance of risk factor assumed to be 1
         r2 <- 2 * b^2 * p * (1 - p) / var
-        if (any(is.na(r2))) {
+        if (anyNA(r2)) {
           warning(
             "beta or allele frequency missing for some SNPs, which could affect accuracy of power pruning"
           )
@@ -386,7 +390,7 @@ power_prune <- function(dat, method = 1, dist.outcome = "binary") {
           iv.se <- 1 / sqrt(mean(dat2$samplesize.outcome, na.rm = TRUE) * r2sum) #standard error of the IV should be proportional to this
         }
         if (dist.outcome == "binary") {
-          if (any(is.na(n.cas)) || any(is.na(n.con))) {
+          if (anyNA(n.cas) || anyNA(n.con)) {
             warning(
               "dist.outcome set to binary but number of cases or controls is missing. Will try using total sample size instead but power pruning will be less accurate"
             )
diff --git a/R/ldsc.r b/R/ldsc.r
index d6841bb1..a781cfa3 100644
--- a/R/ldsc.r
+++ b/R/ldsc.r
@@ -191,7 +191,7 @@ ldsc_rg <- function(id1, id2, ancestry = "infer", snpinfo = NULL, splitsize = 20
     dplyr::inner_join(., d2, by = "rsid")
 
   h1 <- ldsc_h2_internal(d1$z1, d1$l2, d1$n1, d1$l2)
-  h2 <- ldsc_h2_internal(d2$z2, d2$l2, d2$n2, d1$l2)
+  h2 <- ldsc_h2_internal(d2$z2, d2$l2, d2$n2, d2$l2)
 
   dat <- dplyr::inner_join(d1, d2, by = "rsid") %>%
     dplyr::mutate(
@@ -222,8 +222,9 @@ ldsc_rg <- function(id1, id2, ancestry = "infer", snpinfo = NULL, splitsize = 20
 
 
 extract_split <- function(snplist, id, splitsize = 20000) {
-  nsplit <- round(length(snplist) / splitsize)
-  split(snplist, 1:nsplit) %>%
+  n <- length(snplist)
+  chunk_id <- rep(seq_len(ceiling(n / splitsize)), each = splitsize)[seq_len(n)]
+  split(snplist, chunk_id) %>%
     pbapply::pblapply(., function(x) {
       ieugwasr::associations(x, id, proxies = FALSE, x_api_source = x_api_source_header())
     }) %>%
diff --git a/R/leaveoneout.R b/R/leaveoneout.R
index 706a048b..bc5f23f0 100644
--- a/R/leaveoneout.R
+++ b/R/leaveoneout.R
@@ -25,7 +25,7 @@ mr_leaveoneout <- function(dat, parameters = default_parameters(), method = mr_i
     exp_id <- combos$id.exposure[i]
     out_id <- combos$id.outcome[i]
     X <- dat_dt[id.exposure == exp_id & id.outcome == out_id]
-    x <- X[mr_keep == TRUE]
+    x <- X[X$mr_keep]
     nsnp <- nrow(x)
     if (nsnp == 0) {
       x <- X[1, ]
diff --git a/R/mr.R b/R/mr.R
index 7148d70c..f9c27f9b 100644
--- a/R/mr.R
+++ b/R/mr.R
@@ -58,7 +58,7 @@ mr <- function(
     exp_id <- combos$id.exposure[i]
     out_id <- combos$id.outcome[i]
     x1 <- dat_dt[id.exposure == exp_id & id.outcome == out_id]
-    x <- x1[mr_keep == TRUE]
+    x <- x1[x1$mr_keep]
 
     if (nrow(x) == 0) {
       message(
@@ -818,8 +818,11 @@ mr_simple_median <- function(b_exp, b_out, se_exp, se_out, parameters = default_
 #' @export
 #' @return MR estimate
 weighted_median <- function(b_iv, weights) {
-  betaIV.order <- b_iv[order(b_iv)]
-  weights.order <- weights[order(b_iv)]
+  # Order b_iv and weights by the same permutation so they stay aligned; sort()
+  # would drop NA b_iv values and misalign the two vectors.
+  ord <- order(b_iv)
+  betaIV.order <- b_iv[ord]
+  weights.order <- weights[ord]
   weights.sum <- cumsum(weights.order) - 0.5 * weights.order
   weights.sum <- weights.sum / sum(weights.order)
   below <- max(which(weights.sum < 0.5))
diff --git a/R/mr_mode.R b/R/mr_mode.R
index 344841d0..6f6d2988 100644
--- a/R/mr_mode.R
+++ b/R/mr_mode.R
@@ -95,17 +95,20 @@ mr_mode <- function(dat, parameters = default_parameters(), mode_method = "all")
         phi = phi
       )
       #Penalised mode, not assuming NOME
-      penalty <- stats::pchisq(
-        weights * (BetaIV.boot - beta.boot[i, (nphi + 1):(2 * nphi)])^2,
-        df = 1,
-        lower.tail = FALSE
-      )
-      pen.weights <- weights * pmin(1, penalty * parameters$penk)
-
-      beta.boot[i, (2 * nphi + 1):(3 * nphi)] <- beta(
-        BetaIV.in = BetaIV.boot,
-        seBetaIV.in = sqrt(1 / pen.weights),
-        phi = phi
+      #Penalise each SNP against the weighted-mode estimate for the same phi
+      #value, so the per-SNP penalty is not recycled across phi when nphi > 1.
+      beta.boot[i, (2 * nphi + 1):(3 * nphi)] <- vapply(
+        seq_len(nphi),
+        function(j) {
+          penalty <- stats::pchisq(
+            weights * (BetaIV.boot - beta.boot[i, nphi + j])^2,
+            df = 1,
+            lower.tail = FALSE
+          )
+          pen.weights <- weights * pmin(1, penalty * parameters$penk)
+          beta(BetaIV.in = BetaIV.boot, seBetaIV.in = sqrt(1 / pen.weights), phi = phi[j])
+        },
+        numeric(1)
       )
 
       #Simple mode, assuming NOME
@@ -144,10 +147,21 @@ mr_mode <- function(dat, parameters = default_parameters(), mode_method = "all")
   #Point causal effect estimate using the weighted mode (not asusming NOME)
   beta_WeightedMode <- beta(BetaIV.in = BetaIV, seBetaIV.in = seBetaIV[, 1], phi = phi)
   weights <- 1 / seBetaIV[, 1]^2
-  penalty <- stats::pchisq(weights * (BetaIV - beta_WeightedMode)^2, df = 1, lower.tail = FALSE)
-  pen.weights <- weights * pmin(1, penalty * parameters$penk) # penalized
-
-  beta_PenalisedMode <- beta(BetaIV.in = BetaIV, seBetaIV.in = sqrt(1 / pen.weights), phi = phi)
+  # Penalise each SNP against the weighted-mode estimate for the same phi value,
+  # so the per-SNP penalty is not recycled across phi when length(phi) > 1.
+  beta_PenalisedMode <- vapply(
+    seq_along(phi),
+    function(j) {
+      penalty <- stats::pchisq(
+        weights * (BetaIV - beta_WeightedMode[j])^2,
+        df = 1,
+        lower.tail = FALSE
+      )
+      pen.weights <- weights * pmin(1, penalty * parameters$penk) # penalized
+      beta(BetaIV.in = BetaIV, seBetaIV.in = sqrt(1 / pen.weights), phi = phi[j])
+    },
+    numeric(1)
+  )
 
   #Point causal effect estimate using the weighted mode (asusming NOME)
   beta_WeightedMode_NOME <- beta(BetaIV.in = BetaIV, seBetaIV.in = seBetaIV[, 2], phi = phi)
diff --git a/R/other_formats.R b/R/other_formats.R
index ab48b67c..1c3a2122 100644
--- a/R/other_formats.R
+++ b/R/other_formats.R
@@ -104,7 +104,7 @@ harmonise_ld_dat <- function(x, ld) {
     return(NULL)
   }
 
-  if (any(!snpnames$keep)) {
+  if (!all(snpnames$keep)) {
     message(
       " - the following SNPs could not be aligned to the LD reference panel: \n- ",
       paste(subset(snpnames, !keep)$SNP, collapse = "\n - ")
@@ -123,7 +123,7 @@ harmonise_ld_dat <- function(x, ld) {
   rownames(ld) <- snpnames$SNP
   colnames(ld) <- snpnames$SNP
 
-  if (any(!snpnames$keep)) {
+  if (!all(snpnames$keep)) {
     message("Removing ", sum(!snpnames$keep), " variants due to harmonisation issues")
     ld <- ld[snpnames$keep, snpnames$keep]
     x <- x[snpnames$keep, ]
diff --git a/R/query.R b/R/query.R
index 2f7471a2..36cc3f2d 100644
--- a/R/query.R
+++ b/R/query.R
@@ -115,18 +115,15 @@ extract_outcome_data_internal <- function(
   )
   outcomes <- unique(outcomes)
 
-  if (proxies == FALSE) {
+  if (proxies %in% c(FALSE, 0)) {
     proxies <- 0
-  } else if (proxies == TRUE) {
+  } else if (proxies %in% c(TRUE, 1)) {
     proxies <- 1
   } else {
     stop("'proxies' argument should be TRUE or FALSE")
   }
 
-  if (
-    (length(snps) < splitsize && length(outcomes) < splitsize) ||
-      (length(outcomes) < splitsize && length(snps) < splitsize)
-  ) {
+  if (length(snps) < splitsize && length(outcomes) < splitsize) {
     d <- ieugwasr::associations(
       variants = snps,
       id = outcomes,
@@ -318,14 +315,14 @@ format_d <- function(d) {
 
   mrcols <- c("beta.outcome", "se.outcome", "effect_allele.outcome")
   d$mr_keep.outcome <- complete.cases(d[, mrcols])
-  if (any(!d$mr_keep.outcome)) {
+  if (!all(d$mr_keep.outcome)) {
     missinginfosnps <- paste(subset(d, !mr_keep.outcome)$SNP, collapse = " ")
     warning(
       "The following SNP(s) are missing required information for the MR tests and will be excluded: ",
       missinginfosnps
     )
   }
-  if (all(!d$mr_keep.outcome)) {
+  if (!any(d$mr_keep.outcome)) {
     warning(
       "None of the provided SNPs can be used for MR analysis, they are missing required information."
     )
diff --git a/R/read_data.R b/R/read_data.R
index fa64fe1e..628930f4 100644
--- a/R/read_data.R
+++ b/R/read_data.R
@@ -448,7 +448,7 @@ format_data <- function(
     dat$pval.outcome[index] <- min_pval
 
     dat$pval_origin.outcome <- "reported"
-    if (any(is.na(dat$pval.outcome))) {
+    if (anyNA(dat$pval.outcome)) {
       if ("beta.outcome" %in% names(dat) && "se.outcome" %in% names(dat)) {
         index <- is.na(dat$pval.outcome)
         dat$pval.outcome[index] <- 2 *
@@ -551,7 +551,7 @@ format_data <- function(
     mrcols_present <- mrcols[mrcols %in% names(dat)]
     dat$mr_keep.outcome <- dat$mr_keep.outcome &
       complete.cases(dat[, mrcols_present])
-    if (any(!dat$mr_keep.outcome)) {
+    if (!all(dat$mr_keep.outcome)) {
       missinginfosnps <- paste(subset(dat, !mr_keep.outcome)$SNP, collapse = " ")
       warning(
         "The following SNP(s) are missing required information for the MR tests and will be excluded: ",
@@ -559,7 +559,7 @@ format_data <- function(
       )
     }
   }
-  if (all(!dat$mr_keep.outcome)) {
+  if (!any(dat$mr_keep.outcome)) {
     warning(
       "None of the provided SNPs can be used for MR analysis, they are missing required information."
     )
@@ -748,7 +748,7 @@ random_string <- function(n = 1, len = 6) {
 
 create_ids <- function(x) {
   a <- as.factor(x)
-  levels(a) <- random_string(length(levels(a)))
+  levels(a) <- random_string(nlevels(a))
   a <- as.character(a)
   return(a)
 }
@@ -775,12 +775,12 @@ combine_data <- function(x) {
     stop("Datasets must be generated from format_data")
   }
 
-  check <- all(sapply(x, function(i) {
+  check <- all(vapply(x, function(i) {
     type %in% names(i)
-  }))
+  }, logical(1)))
 
   if (!check) {
-    stop("Not all datasets or of type '", type, "'")
+    stop("Not all datasets are of type '", type, "'")
   }
 
   id_col <- paste0("id.", type)
diff --git a/R/rucker.R b/R/rucker.R
index d964b6a6..b9970a47 100644
--- a/R/rucker.R
+++ b/R/rucker.R
@@ -40,9 +40,7 @@ PM <- function(y = y, s = s, Alpha = 0.1) {
   for (j in seq_len(L)) {
     tausq <- 0
     Fstat <- 1
-    TAUsq <- NULL
     while (Fstat > 0) {
-      TAUsq <- c(TAUsq, tausq)
       w <- 1 / (s^2 + tausq)
       sum.w <- sum(w)
       w2 <- w^2
diff --git a/R/scatterplot.R b/R/scatterplot.R
index abc6dc88..f70a78d6 100644
--- a/R/scatterplot.R
+++ b/R/scatterplot.R
@@ -11,7 +11,7 @@ mr_scatter_plot <- function(mr_results, dat) {
   combos <- unique(dat[, c("id.exposure", "id.outcome")])
   mrres <- lapply(seq_len(nrow(combos)), function(i) {
     d <- dat[dat$id.exposure == combos$id.exposure[i] & dat$id.outcome == combos$id.outcome[i], ]
-    if (nrow(d) < 2 | sum(d$mr_keep) == 0) {
+    if (nrow(d) < 2 || sum(d$mr_keep) == 0) {
       return(blank_plot("Insufficient number of SNPs"))
     }
     d <- subset(d, mr_keep)
diff --git a/R/singlesnp.R b/R/singlesnp.R
index 23ea9622..08a54707 100644
--- a/R/singlesnp.R
+++ b/R/singlesnp.R
@@ -41,7 +41,7 @@ mr_singlesnp <- function(
     exp_id <- combos$id.exposure[i]
     out_id <- combos$id.outcome[i]
     X <- dat_dt[id.exposure == exp_id & id.outcome == out_id]
-    x <- X[mr_keep == TRUE]
+    x <- X[X$mr_keep]
     nsnp <- nrow(x)
     if (nsnp == 0) {
       x <- X[1, ]
diff --git a/R/steiger.R b/R/steiger.R
index 429311f9..d2505066 100644
--- a/R/steiger.R
+++ b/R/steiger.R
@@ -249,7 +249,7 @@ directionality_test <- function(dat) {
 #' \item{sensitivity_plot}{Plot of parameter space of causal directions and measurement error}
 #' }
 mr_steiger2 <- function(r_exp, r_out, n_exp, n_out, r_xxo = 1, r_yyo = 1, ...) {
-  index <- any(is.na(r_exp)) | any(is.na(r_out)) | any(is.na(n_exp)) | any(is.na(n_out))
+  index <- anyNA(r_exp) | anyNA(r_out) | anyNA(n_exp) | anyNA(n_out)
   n_exp <- n_exp[!index]
   n_out <- n_out[!index]
 
diff --git a/tests/testthat/test_add_metadata.r b/tests/testthat/test_add_metadata.r
index 4eb0e0ab..2acd4777 100644
--- a/tests/testthat/test_add_metadata.r
+++ b/tests/testthat/test_add_metadata.r
@@ -24,7 +24,7 @@ load(system.file("extdata", "test_add_metadata.RData", package = "TwoSampleMR"))
 
 test_that("exposure data 1", {
   d1 <- try(d1 %>% add_metadata())
-  if (class(d1) == "try-error") {
+  if (inherits(d1, "try-error")) {
     skip("Server issues")
   }
   expect_true("units.exposure" %in% names(d1))
@@ -32,7 +32,7 @@ test_that("exposure data 1", {
 
 test_that("exposure data 2", {
   d2 <- try(d2 %>% add_metadata())
-  if (class(d2) == "try-error") {
+  if (inherits(d2, "try-error")) {
     skip("Server issues")
   }
   expect_true("units.exposure" %in% names(d2))
@@ -40,7 +40,7 @@ test_that("exposure data 2", {
 
 test_that("outcome data 1", {
   d3 <- try(d3 %>% add_metadata())
-  if (class(d3) == "try-error") {
+  if (inherits(d3, "try-error")) {
     skip("Server issues")
   }
   expect_true("units.outcome" %in% names(d3))
@@ -48,7 +48,7 @@ test_that("outcome data 1", {
 
 test_that("outcome data 2", {
   d4 <- try(d4 %>% add_metadata())
-  if (class(d4) == "try-error") {
+  if (inherits(d4, "try-error")) {
     skip("Server issues")
   }
   expect_true("units.outcome" %in% names(d4))
@@ -56,7 +56,7 @@ test_that("outcome data 2", {
 
 test_that("dat 2", {
   d5 <- try(d5 %>% add_metadata())
-  if (class(d5) == "try-error") {
+  if (inherits(d5, "try-error")) {
     skip("Server issues")
   }
   expect_true("units.outcome" %in% names(d5) & "units.exposure" %in% names(d5))
@@ -65,7 +65,7 @@ test_that("dat 2", {
 test_that("no id1", {
   d6$id.exposure <- "not a real id"
   d6 <- try(add_metadata(d6))
-  if (class(d6) == "try-error") {
+  if (inherits(d6, "try-error")) {
     skip("Server issues")
   }
   expect_true(!"units.exposure" %in% names(d6))
@@ -74,7 +74,7 @@ test_that("no id1", {
 test_that("no id2", {
   d7$id.outcome <- "not a real id"
   d7 <- try(add_metadata(d7))
-  if (class(d7) == "try-error") {
+  if (inherits(d7, "try-error")) {
     skip("Server issues")
   }
   expect_true(!"units.outcome" %in% names(d7))
@@ -82,7 +82,7 @@ test_that("no id2", {
 
 test_that("ukb-d", {
   d8 <- try(add_metadata(d8))
-  if (class(d8) == "try-error") {
+  if (inherits(d8, "try-error")) {
     skip("Server issues")
   }
   expect_true("units.outcome" %in% names(d8))
@@ -90,18 +90,18 @@ test_that("ukb-d", {
 
 test_that("bbj-a-1", {
   d9 <- try(d9 %>% add_metadata())
-  if (class(d9) == "try-error") {
+  if (inherits(d9, "try-error")) {
     skip("Server issues")
   }
   expect_true("samplesize.exposure" %in% names(d9))
-  expect_true(all(!is.na(d9$samplesize.exposure)))
+  expect_true(!anyNA(d9$samplesize.exposure))
 })
 
 test_that("ieu-b-109", {
   d10 <- try(d10 %>% add_metadata())
-  if (class(d10) == "try-error") {
+  if (inherits(d10, "try-error")) {
     skip("Server issues")
   }
   expect_true("samplesize.exposure" %in% names(d10))
-  expect_true(all(!is.na(d10$samplesize.exposure)))
+  expect_true(!anyNA(d10$samplesize.exposure))
 })
diff --git a/tests/testthat/test_instruments.R b/tests/testthat/test_instruments.R
index 4264692e..9509a201 100644
--- a/tests/testthat/test_instruments.R
+++ b/tests/testthat/test_instruments.R
@@ -8,7 +8,7 @@ skip_on_cran()
 test_that("server and mrinstruments 1", {
   # no no
   exp_dat <- try(extract_instruments(outcomes = c("ieu-a-1032")))
-  if (class(exp_dat) == "try-error") {
+  if (inherits(exp_dat, "try-error")) {
     skip("Server issues")
   }
   expect_true(length(unique(exp_dat$id)) == 0)
@@ -18,7 +18,7 @@ test_that("server and mrinstruments 1", {
 test_that("server and mrinstruments 2", {
   # no yes
   exp_dat <- try(extract_instruments(outcomes = c("ebi-a-GCST004634")))
-  if (class(exp_dat) == "try-error") {
+  if (inherits(exp_dat, "try-error")) {
     skip("Server issues")
   }
   expect_true(length(unique(exp_dat$id)) == 1)
@@ -27,7 +27,7 @@ test_that("server and mrinstruments 2", {
 test_that("server and mrinstruments 3", {
   # yes no
   exp_dat <- try(extract_instruments(outcomes = c("ieu-a-2", "ieu-a-1032")))
-  if (class(exp_dat) == "try-error") {
+  if (inherits(exp_dat, "try-error")) {
     skip("Server issues")
   }
   expect_true(length(unique(exp_dat$id)) == 1)
@@ -36,7 +36,7 @@ test_that("server and mrinstruments 3", {
 test_that("server and mrinstruments 4", {
   # yes yes
   exp_dat <- try(extract_instruments(outcomes = c("ieu-a-2", "ebi-a-GCST004634")))
-  if (class(exp_dat) == "try-error") {
+  if (inherits(exp_dat, "try-error")) {
     skip("Server issues")
   }
   expect_true(length(unique(exp_dat$id)) == 2)
@@ -44,7 +44,7 @@ test_that("server and mrinstruments 4", {
 
 test_that("server and mrinstruments 5", {
   exp_dat <- try(extract_instruments(outcomes = c("ieu-a-1032", "ebi-a-GCST004634")))
-  if (class(exp_dat) == "try-error") {
+  if (inherits(exp_dat, "try-error")) {
     skip("Server issues")
   }
   expect_true(length(unique(exp_dat$id)) == 1)
@@ -52,7 +52,7 @@ test_that("server and mrinstruments 5", {
 
 test_that("server and mrinstruments 6", {
   exp_dat <- try(extract_instruments(outcomes = c(2, 100, "ieu-a-1032", 104, 72, 999)))
-  if (class(exp_dat) == "try-error") {
+  if (inherits(exp_dat, "try-error")) {
     skip("Server issues")
   }
   expect_true(length(unique(exp_dat$id)) == 5)
@@ -62,7 +62,7 @@ test_that("server and mrinstruments 7", {
   exp_dat <- try(extract_instruments(
     outcomes = c(2, 100, "ieu-a-1032", 104, 72, 999, "ebi-a-GCST004634")
   ))
-  if (class(exp_dat) == "try-error") {
+  if (inherits(exp_dat, "try-error")) {
     skip("Server issues")
   }
   expect_true(length(unique(exp_dat$id)) == 6)
@@ -72,7 +72,7 @@ load(system.file("extdata", "test_commondata.RData", package = "TwoSampleMR"))
 
 test_that("read data", {
   exp_dat <- try(extract_instruments("ieu-a-2"))
-  if (class(exp_dat) == "try-error") {
+  if (inherits(exp_dat, "try-error")) {
     skip("Server issues")
   }
   names(exp_dat) <- gsub(".exposure", "", names(exp_dat))
diff --git a/tests/testthat/test_ld.R b/tests/testthat/test_ld.R
index fa44a46e..4ed44bf3 100644
--- a/tests/testthat/test_ld.R
+++ b/tests/testthat/test_ld.R
@@ -7,11 +7,11 @@ skip_on_cran()
 
 # extract some data
 a <- try(extract_instruments("ieu-a-2", clump = 0))
-if (class(a) == "try-error") {
+if (inherits(a, "try-error")) {
   skip("Server issues")
 }
 out <- try(clump_data(a))
-if (class(out) == "try-error") {
+if (inherits(out, "try-error")) {
   skip("Server issues")
 }
 
@@ -38,7 +38,7 @@ test_that("matrix", {
 
 test_that("clump multiple", {
   a <- try(extract_instruments(c("ieu-a-2", "ieu-a-1001"), clump = 0))
-  if (class(a) == "try-error") {
+  if (inherits(a, "try-error")) {
     skip("Server issues")
   }
   out <- clump_data(a)
diff --git a/tests/testthat/test_plots.R b/tests/testthat/test_plots.R
index 00a1fd34..3255167e 100644
--- a/tests/testthat/test_plots.R
+++ b/tests/testthat/test_plots.R
@@ -5,7 +5,7 @@ load(system.file("extdata", "test_commondata.RData", package = "TwoSampleMR"))
 test_that("MR scatter plot for mr_ivw", {
   # dat <- make_dat("ieu-a-2", "ieu-a-7")
   m <- mr(dat, method_list = "mr_ivw")
-  expect_no_error(p <- mr_scatter_plot(m, dat))
+  p <- expect_no_error(mr_scatter_plot(m, dat))
   expect_true(is.list(p))
   expect_true(length(p) == 1L)
 })
@@ -13,21 +13,21 @@ test_that("MR scatter plot for mr_ivw", {
 test_that("Scatter plot for default set of estimates", {
   # dat <- make_dat("ieu-a-2", "ieu-a-7")
   m2 <- mr(dat)
-  expect_no_error(p2 <- mr_scatter_plot(m2, dat))
+  p2 <- expect_no_error(mr_scatter_plot(m2, dat))
   expect_true(is.list(p2))
   expect_true(length(p2) == 1L)
 })
 
 test_that("Scatter plot for mr_grip", {
   m3 <- mr(dat, method_list = "mr_grip")
-  expect_no_error(p3 <- mr_scatter_plot(m3, dat))
+  p3 <- expect_no_error(mr_scatter_plot(m3, dat))
   expect_true(is.list(p3))
   expect_true(length(p3) == 1L)
 })
 
 test_that("A second scatter plot for mr_grip", {
   m4 <- mr(dat2, method_list = "mr_grip")
-  expect_no_error(p4 <- mr_scatter_plot(m4, dat2))
+  p4 <- expect_no_error(mr_scatter_plot(m4, dat2))
   expect_true(is.list(p4))
   expect_true(length(p4) == 4L)
 })
@@ -36,17 +36,17 @@ res_single <- mr_singlesnp(dat)
 res_loo <- mr_leaveoneout(dat)
 
 test_that("Forest plot", {
-  expect_no_error(p5 <- mr_forest_plot(res_single))
+  p5 <- expect_no_error(mr_forest_plot(res_single))
   expect_true(length(p5) == 1L)
 })
 
 test_that("Leave one out plot", {
-  expect_no_error(p6 <- mr_leaveoneout_plot(res_loo))
+  p6 <- expect_no_error(mr_leaveoneout_plot(res_loo))
   expect_true(length(p6) == 1L)
 })
 
 test_that("Funnel plot", {
-  expect_no_error(p7 <- mr_funnel_plot(res_single))
+  p7 <- expect_no_error(mr_funnel_plot(res_single))
   expect_true(length(p7) == 1L)
 })
 
@@ -78,7 +78,7 @@ test_that("Forest plot 1 to many", {
 test_that("Forest plot 1 to many test 2", {
   res$pval <- formatC(res$pval, format = "e", digits = 2)
   expect_warning(utils::capture.output(
-    p9 <- forest_plot_1_to_many(
+    forest_plot_1_to_many(
       res,
       b = "b",
       se = "se",
diff --git a/tests/testthat/test_rsq.r b/tests/testthat/test_rsq.r
index bef89585..511e04b6 100644
--- a/tests/testthat/test_rsq.r
+++ b/tests/testthat/test_rsq.r
@@ -130,7 +130,7 @@ test_that("bbj-a-1", {
   if (inherits(d, "try-error")) {
     skip("Server issues")
   }
-  expect_true(all(!is.na(d$rsq.exposure)))
+  expect_true(!anyNA(d$rsq.exposure))
 })
 
 test_that("bsen vs pn", {
diff --git a/vignettes/perform_mr.Rmd b/vignettes/perform_mr.Rmd
index 637b0485..a7b94fdd 100644
--- a/vignettes/perform_mr.Rmd
+++ b/vignettes/perform_mr.Rmd
@@ -868,7 +868,7 @@ Alternatively, convert to the `MRInput` format but also obtaining the LD matrix
 
 ```{r eval=evalinr44}
 dat2 <- try(dat_to_MRInput(dat, get_correlation = TRUE))
-if (class(dat2) != "try-error") MendelianRandomization::mr_ivw(dat2[[1]], correl = TRUE)
+if (!inherits(dat2, "try-error")) MendelianRandomization::mr_ivw(dat2[[1]], correl = TRUE)
 ```
 
 ## MR-MoE: Using a mixture of experts machine learning approach