From 7f9a4e8dcaf79011e124ea9e7a89d93c34d19a29 Mon Sep 17 00:00:00 2001
From: Yihui Xie <xie@yihui.name>
Date: Fri, 10 Jul 2026 00:56:39 -0400
Subject: [PATCH] Replace gt with lt for table output

Replace the gt package dependency with the lightweight lt package for all
table output, mirroring Merck/gsDesign2#629.

- Add lt.gsBinomialExactTable() S3 method (R/as_lt.R) and deprecate as_gt()
  in favor of lt::lt().
- Swap gt for lt in DESCRIPTION Imports; add Remotes: yihui/lt pending the
  next lt release on CRAN.
- Migrate all table-bearing vignettes to lt, dropping the bundled
  html_vignette CSS in favor of a minimal vignettes/vignette.css.
- Update tests, docs, and _pkgdown.yml accordingly.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---
 DESCRIPTION                                   |   4 +-
 NAMESPACE                                     |   9 +-
 NEWS.md                                       |   9 ++
 R/as_gt.R                                     |  83 ++-----------
 R/as_lt.R                                     |  72 +++++++++++
 R/as_table.R                                  |   2 +-
 R/globals.R                                   |   2 +-
 _pkgdown.yml                                  |   2 +-
 man/as_gt.Rd                                  |  66 +---------
 man/as_table.Rd                               |   2 +-
 man/lt-methods.Rd                             |  76 ++++++++++++
 .../testthat/_snaps/independent-test-as_gt.md |  26 ----
 tests/testthat/test-independent-test-as_gt.R  |  76 ------------
 tests/testthat/test-independent-test-lt.R     |  48 ++++++++
 vignettes/ConditionalErrorSpending.Rmd        |  75 +++++------
 vignettes/MultiSeasonRareEvents.Rmd           |  90 +++++++-------
 vignettes/PoissonMixtureModel.Rmd             |  10 +-
 vignettes/VaccineEfficacy.Rmd                 | 116 +++++++++---------
 vignettes/binomialSPRTExample.Rmd             |  10 +-
 vignettes/binomialTwoSample.Rmd               |  66 +++++-----
 vignettes/gsSurvBasicExamples.Rmd             |  46 +++----
 vignettes/vignette.css                        |  20 +++
 22 files changed, 461 insertions(+), 449 deletions(-)
 create mode 100644 R/as_lt.R
 create mode 100644 man/lt-methods.Rd
 delete mode 100644 tests/testthat/_snaps/independent-test-as_gt.md
 delete mode 100644 tests/testthat/test-independent-test-as_gt.R
 create mode 100644 tests/testthat/test-independent-test-lt.R
 create mode 100644 vignettes/vignette.css

diff --git a/DESCRIPTION b/DESCRIPTION
index c96d6bdb..29d52db4 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -22,7 +22,7 @@ Imports:
     dplyr (>= 1.1.0),
     ggplot2 (>= 3.1.1),
     graphics,
-    gt,
+    lt,
     methods,
     r2rtf,
     rlang,
@@ -44,6 +44,8 @@ Suggests:
     testthat,
     utils,
     vdiffr
+Remotes:
+    yihui/lt
 VignetteBuilder:
     knitr
 Config/roxygen2/version: 8.0.0
diff --git a/NAMESPACE b/NAMESPACE
index ff9aba52..38cac007 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,9 +1,9 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(as_gt,gsBinomialExactTable)
 S3method(as_rtf,gsBinomialExactTable)
 S3method(as_rtf,gsBoundSummary)
 S3method(as_table,gsBinomialExact)
+S3method(lt::lt,gsBinomialExactTable)
 S3method(plot,binomialSPRT)
 S3method(plot,gsBinomialExact)
 S3method(plot,gsDesign)
@@ -140,13 +140,6 @@ importFrom(graphics,plot)
 importFrom(graphics,points)
 importFrom(graphics,strwidth)
 importFrom(graphics,text)
-importFrom(gt,cols_label)
-importFrom(gt,fmt_number)
-importFrom(gt,fmt_percent)
-importFrom(gt,gt)
-importFrom(gt,html)
-importFrom(gt,tab_header)
-importFrom(gt,tab_spanner)
 importFrom(methods,is)
 importFrom(r2rtf,rtf_body)
 importFrom(r2rtf,rtf_colheader)
diff --git a/NEWS.md b/NEWS.md
index 24f1e0ed..16b8662d 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,14 @@
 # gsDesign (development version)
 
+## Major changes
+
+- The (heavy) **gt** dependency has been replaced with the lightweight **lt**
+  package. The `as_gt()` function is deprecated in favor of `lt::lt()`. An S3
+  method for `lt()` is provided for `gsBinomialExactTable` objects, and all
+  vignettes now use lt functions (`lt()`, `lt_header()`, `lt_spanner()`,
+  `lt_format()`, `lt_footnote()`, `lt_note()`, `lt_label()`, `lt_align()`,
+  `lt_group()`). There are no significant visual changes in the HTML tables.
+
 # gsDesign 3.10.0
 
 ## New features
diff --git a/R/as_gt.R b/R/as_gt.R
index 08113cf6..6c6cad02 100644
--- a/R/as_gt.R
+++ b/R/as_gt.R
@@ -1,82 +1,17 @@
-#' Convert a summary table object to a gt object
+#' Deprecated: use lt() instead
 #'
-#' Convert a summary table object created with \code{\link{as_table}}
-#' to a \code{gt_tbl} object; currently only implemented for
-#' \code{\link{gsBinomialExact}}.
+#' \code{as_gt()} is deprecated; use \code{\link[lt]{lt}()} instead.
 #'
 #' @param x Object to be converted.
-#' @param ... Other parameters that may be specific to the object.
+#' @param ... Additional arguments passed to \code{\link[lt]{lt}()}.
 #'
-#' @return A \code{gt_tbl} object that may be extended by overloaded versions of
-#'   \code{\link{as_gt}}.
+#' @return An \code{lt_tbl} object.
 #'
-#' @seealso \code{vignette("binomialSPRTExample")}
-#'
-#' @details
-#' Currently only implemented for \code{\link{gsBinomialExact}} objects.
-#' Creates a table to summarize an object.
-#' For \code{\link{gsBinomialExact}}, this summarized operating characteristics
-#' across a range of effect sizes.
-#'
-#' @export
-#'
-#' @examples
-#' safety_design <- binomialSPRT(
-#'   p0 = .04, p1 = .1, alpha = .04, beta = .2, minn = 4, maxn = 75
-#' )
-#' safety_power <- gsBinomialExact(
-#'   k = length(safety_design$n.I),
-#'   theta = seq(.02, .16, .02),
-#'   n.I = safety_design$n.I,
-#'   a = safety_design$lower$bound,
-#'   b = safety_design$upper$bound
-#' )
-#' safety_power |>
-#'   as_table() |>
-#'   as_gt(
-#'     theta_label = gt::html("Underlying<br>AE rate"),
-#'     prob_decimals = 3,
-#'     bound_label = c("low rate", "high rate")
-#'   )
-as_gt <- function(x, ...) UseMethod("as_gt")
-
-#' @rdname as_gt
-#'
-#' @param title Table title.
-#' @param subtitle Table subtitle.
-#' @param theta_label Label for theta.
-#' @param bound_label Label for bounds.
-#' @param prob_decimals Number of decimal places for probability of crossing.
-#' @param en_decimals Number of decimal places for expected number of
-#'   observations when bound is crossed or when trial ends without crossing.
-#' @param rr_decimals Number of decimal places for response rates.
-#'
-#' @importFrom gt gt tab_spanner cols_label html fmt_number fmt_percent tab_header
+#' @seealso \code{\link{lt-methods}}
 #'
 #' @export
-as_gt.gsBinomialExactTable <- function(
-    x,
-    ...,
-    title = "Operating Characteristics for the Truncated SPRT Design",
-    subtitle = "Assumes trial evaluated sequentially after each response",
-    theta_label = html("Underlying<br>response rate"),
-    bound_label = c("Futility bound", "Efficacy bound"),
-    prob_decimals = 2,
-    en_decimals = 1,
-    rr_decimals = 0) {
-  out_gt <- x |>
-    gt() |>
-    tab_spanner(label = "Probability of crossing", columns = c(Lower, Upper)) |>
-    cols_label(
-      theta = theta_label,
-      Lower = bound_label[1],
-      Upper = bound_label[2],
-      en = html("Average<br>sample size")
-    ) |>
-    fmt_number(columns = c(Lower, Upper), decimals = prob_decimals) |>
-    fmt_number(columns = en, decimals = en_decimals) |>
-    fmt_percent(columns = theta, decimals = rr_decimals) |>
-    tab_header(title = title, subtitle = subtitle)
-
-  out_gt
+as_gt <- function(x, ...) {
+  .Deprecated("lt", package = "lt",
+    msg = "as_gt() is deprecated; please use lt::lt() instead.")
+  lt::lt(x, ...)
 }
diff --git a/R/as_lt.R b/R/as_lt.R
new file mode 100644
index 00000000..b5c393de
--- /dev/null
+++ b/R/as_lt.R
@@ -0,0 +1,72 @@
+#' Convert a summary table object to an lt table
+#'
+#' Convert a summary table object created with \code{\link{as_table}}
+#' to an \code{lt_tbl} object; currently only implemented for
+#' \code{\link{gsBinomialExact}}.
+#'
+#' @param data Object to be converted.
+#' @param ... Other parameters that may be specific to the object.
+#' @param title Table title.
+#' @param subtitle Table subtitle.
+#' @param theta_label Label for theta.
+#' @param bound_label Label for bounds.
+#' @param prob_decimals Number of decimal places for probability of crossing.
+#' @param en_decimals Number of decimal places for expected number of
+#'   observations when bound is crossed or when trial ends without crossing.
+#' @param rr_decimals Number of decimal places for response rates.
+#'
+#' @return An \code{lt_tbl} object.
+#'
+#' @seealso \code{vignette("binomialSPRTExample")}
+#'
+#' @details
+#' Currently only implemented for \code{\link{gsBinomialExact}} objects.
+#' Creates a table to summarize an object.
+#' For \code{\link{gsBinomialExact}}, this summarized operating characteristics
+#' across a range of effect sizes.
+#'
+#' @name lt-methods
+#'
+#' @examples
+#' safety_design <- binomialSPRT(
+#'   p0 = .04, p1 = .1, alpha = .04, beta = .2, minn = 4, maxn = 75
+#' )
+#' safety_power <- gsBinomialExact(
+#'   k = length(safety_design$n.I),
+#'   theta = seq(.02, .16, .02),
+#'   n.I = safety_design$n.I,
+#'   a = safety_design$lower$bound,
+#'   b = safety_design$upper$bound
+#' )
+#' safety_power |>
+#'   as_table() |>
+#'   lt::lt(
+#'     theta_label = I("Underlying<br>AE rate"),
+#'     prob_decimals = 3,
+#'     bound_label = c("low rate", "high rate")
+#'   )
+#'
+#' @exportS3Method lt::lt
+lt.gsBinomialExactTable <- function(
+    data,
+    ...,
+    title = "Operating Characteristics for the Truncated SPRT Design",
+    subtitle = "Assumes trial evaluated sequentially after each response",
+    theta_label = I("Underlying<br>response rate"),
+    bound_label = c("Futility bound", "Efficacy bound"),
+    prob_decimals = 2,
+    en_decimals = 1,
+    rr_decimals = 0) {
+  lt::lt(as.data.frame(data), ...) |>
+    lt::lt_spanner(label = "Probability of crossing", columns = c("Lower", "Upper")) |>
+    lt::lt_label(
+      theta = theta_label,
+      Lower = bound_label[1],
+      Upper = bound_label[2],
+      en = I("Average<br>sample size")
+    ) |>
+    lt::lt_format(columns = c("Lower", "Upper"), decimals = prob_decimals) |>
+    lt::lt_format(columns = "en", decimals = en_decimals) |>
+    lt::lt_format(columns = "theta", decimals = rr_decimals, percent = TRUE) |>
+    lt::lt_header(title = title, subtitle = subtitle)
+}
diff --git a/R/as_table.R b/R/as_table.R
index d37b94b2..3b48a379 100644
--- a/R/as_table.R
+++ b/R/as_table.R
@@ -30,7 +30,7 @@
 #' )
 #' b_power |>
 #'   as_table() |>
-#'   as_gt()
+#'   lt::lt()
 as_table <- function(x, ...) UseMethod("as_table")
 
 #' @rdname as_table
diff --git a/R/globals.R b/R/globals.R
index 08605416..6df05d46 100644
--- a/R/globals.R
+++ b/R/globals.R
@@ -1,7 +1,7 @@
 utils::globalVariables(
   unique(
     c(
-      # From `as_gt.gsBinomialExactTable()`
+      # From `lt.gsBinomialExactTable()`
       c("Lower", "Upper", "en", "theta"),
       # From `binomialPowerTable()`
       c("pE")
diff --git a/_pkgdown.yml b/_pkgdown.yml
index e798df10..71cfdb6b 100755
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -131,8 +131,8 @@ reference:
   contents:
   - as_table
   - as_table.gsBinomialExact
+  - lt-methods
   - as_gt
-  - as_gt.gsBinomialExactTable
   - as_rtf
   - as_rtf.gsBinomialExactTable
 
diff --git a/man/as_gt.Rd b/man/as_gt.Rd
index 09e13150..00b1b2ee 100644
--- a/man/as_gt.Rd
+++ b/man/as_gt.Rd
@@ -2,77 +2,21 @@
 % Please edit documentation in R/as_gt.R
 \name{as_gt}
 \alias{as_gt}
-\alias{as_gt.gsBinomialExactTable}
-\title{Convert a summary table object to a gt object}
+\title{Deprecated: use lt() instead}
 \usage{
 as_gt(x, ...)
-
-\method{as_gt}{gsBinomialExactTable}(
-  x,
-  ...,
-  title = "Operating Characteristics for the Truncated SPRT Design",
-  subtitle = "Assumes trial evaluated sequentially after each response",
-  theta_label = html("Underlying<br>response rate"),
-  bound_label = c("Futility bound", "Efficacy bound"),
-  prob_decimals = 2,
-  en_decimals = 1,
-  rr_decimals = 0
-)
 }
 \arguments{
 \item{x}{Object to be converted.}
 
-\item{...}{Other parameters that may be specific to the object.}
-
-\item{title}{Table title.}
-
-\item{subtitle}{Table subtitle.}
-
-\item{theta_label}{Label for theta.}
-
-\item{bound_label}{Label for bounds.}
-
-\item{prob_decimals}{Number of decimal places for probability of crossing.}
-
-\item{en_decimals}{Number of decimal places for expected number of
-observations when bound is crossed or when trial ends without crossing.}
-
-\item{rr_decimals}{Number of decimal places for response rates.}
+\item{...}{Additional arguments passed to \code{\link[lt]{lt}()}.}
 }
 \value{
-A \code{gt_tbl} object that may be extended by overloaded versions of
-  \code{\link{as_gt}}.
+An \code{lt_tbl} object.
 }
 \description{
-Convert a summary table object created with \code{\link{as_table}}
-to a \code{gt_tbl} object; currently only implemented for
-\code{\link{gsBinomialExact}}.
-}
-\details{
-Currently only implemented for \code{\link{gsBinomialExact}} objects.
-Creates a table to summarize an object.
-For \code{\link{gsBinomialExact}}, this summarized operating characteristics
-across a range of effect sizes.
-}
-\examples{
-safety_design <- binomialSPRT(
-  p0 = .04, p1 = .1, alpha = .04, beta = .2, minn = 4, maxn = 75
-)
-safety_power <- gsBinomialExact(
-  k = length(safety_design$n.I),
-  theta = seq(.02, .16, .02),
-  n.I = safety_design$n.I,
-  a = safety_design$lower$bound,
-  b = safety_design$upper$bound
-)
-safety_power |>
-  as_table() |>
-  as_gt(
-    theta_label = gt::html("Underlying<br>AE rate"),
-    prob_decimals = 3,
-    bound_label = c("low rate", "high rate")
-  )
+\code{as_gt()} is deprecated; use \code{\link[lt]{lt}()} instead.
 }
 \seealso{
-\code{vignette("binomialSPRTExample")}
+\code{\link{lt-methods}}
 }
diff --git a/man/as_table.Rd b/man/as_table.Rd
index 57a8564f..c486ebe5 100644
--- a/man/as_table.Rd
+++ b/man/as_table.Rd
@@ -35,7 +35,7 @@ b_power <- gsBinomialExact(
 )
 b_power |>
   as_table() |>
-  as_gt()
+  lt::lt()
 }
 \seealso{
 \code{vignette("binomialSPRTExample")}
diff --git a/man/lt-methods.Rd b/man/lt-methods.Rd
new file mode 100644
index 00000000..c0f25fe4
--- /dev/null
+++ b/man/lt-methods.Rd
@@ -0,0 +1,76 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/as_lt.R
+\name{lt-methods}
+\alias{lt-methods}
+\alias{lt.gsBinomialExactTable}
+\title{Convert a summary table object to an lt table}
+\usage{
+\method{lt}{gsBinomialExactTable}(
+  data,
+  ...,
+  title = "Operating Characteristics for the Truncated SPRT Design",
+  subtitle = "Assumes trial evaluated sequentially after each response",
+  theta_label = I("Underlying<br>response rate"),
+  bound_label = c("Futility bound", "Efficacy bound"),
+  prob_decimals = 2,
+  en_decimals = 1,
+  rr_decimals = 0
+)
+}
+\arguments{
+\item{data}{Object to be converted.}
+
+\item{...}{Other parameters that may be specific to the object.}
+
+\item{title}{Table title.}
+
+\item{subtitle}{Table subtitle.}
+
+\item{theta_label}{Label for theta.}
+
+\item{bound_label}{Label for bounds.}
+
+\item{prob_decimals}{Number of decimal places for probability of crossing.}
+
+\item{en_decimals}{Number of decimal places for expected number of
+observations when bound is crossed or when trial ends without crossing.}
+
+\item{rr_decimals}{Number of decimal places for response rates.}
+}
+\value{
+An \code{lt_tbl} object.
+}
+\description{
+Convert a summary table object created with \code{\link{as_table}}
+to an \code{lt_tbl} object; currently only implemented for
+\code{\link{gsBinomialExact}}.
+}
+\details{
+Currently only implemented for \code{\link{gsBinomialExact}} objects.
+Creates a table to summarize an object.
+For \code{\link{gsBinomialExact}}, this summarized operating characteristics
+across a range of effect sizes.
+}
+\examples{
+safety_design <- binomialSPRT(
+  p0 = .04, p1 = .1, alpha = .04, beta = .2, minn = 4, maxn = 75
+)
+safety_power <- gsBinomialExact(
+  k = length(safety_design$n.I),
+  theta = seq(.02, .16, .02),
+  n.I = safety_design$n.I,
+  a = safety_design$lower$bound,
+  b = safety_design$upper$bound
+)
+safety_power |>
+  as_table() |>
+  lt::lt(
+    theta_label = I("Underlying<br>AE rate"),
+    prob_decimals = 3,
+    bound_label = c("low rate", "high rate")
+  )
+
+}
+\seealso{
+\code{vignette("binomialSPRTExample")}
+}
diff --git a/tests/testthat/_snaps/independent-test-as_gt.md b/tests/testthat/_snaps/independent-test-as_gt.md
deleted file mode 100644
index a9dfc5d7..00000000
--- a/tests/testthat/_snaps/independent-test-as_gt.md
+++ /dev/null
@@ -1,26 +0,0 @@
-# Snapshot test for gsBinomialExact safety table
-
-    \begin{table}[t]
-    \caption*{
-    {\fontsize{20}{25}\selectfont  Operating Characteristics for the Truncated SPRT Design\fontsize{12}{15}\selectfont } \\ 
-    {\fontsize{14}{17}\selectfont  Assumes trial evaluated sequentially after each response\fontsize{12}{15}\selectfont }
-    } 
-    \fontsize{12.0pt}{14.0pt}\selectfont
-    \begin{tabular*}{\linewidth}{@{\extracolsep{\fill}}rrrr}
-    \toprule
-     & \multicolumn{2}{c}{{Probability of crossing}} &  \\ 
-    \cmidrule(lr){2-3}
-    Underlying<br>AE rate & low rate & high rate & Average<br>sample size \\ 
-    \midrule\addlinespace[2.5pt]
-    2\% & 0.964 & 0.001 & 34.8 \\ 
-    4\% & 0.769 & 0.019 & 46.4 \\ 
-    6\% & 0.506 & 0.108 & 54.3 \\ 
-    8\% & 0.291 & 0.290 & 56.1 \\ 
-    10\% & 0.155 & 0.516 & 52.8 \\ 
-    12\% & 0.079 & 0.714 & 46.8 \\ 
-    14\% & 0.039 & 0.851 & 40.2 \\ 
-    16\% & 0.020 & 0.930 & 34.2 \\ 
-    \bottomrule
-    \end{tabular*}
-    \end{table}
-
diff --git a/tests/testthat/test-independent-test-as_gt.R b/tests/testthat/test-independent-test-as_gt.R
deleted file mode 100644
index 0aa53165..00000000
--- a/tests/testthat/test-independent-test-as_gt.R
+++ /dev/null
@@ -1,76 +0,0 @@
-test_that("Snapshot test for gsBinomialExact safety table", {
-  skip_on_cran()
-
-  gt_to_latex <- function(data) cat(as.character(gt::as_latex(data)))
-
-  safety_design <- binomialSPRT(p0 = 0.04, p1 = 0.1, alpha = 0.04, beta = 0.2, minn = 4, maxn = 75)
-  safety_power <- gsBinomialExact(
-    k = length(safety_design$n.I),
-    theta = seq(0.02, 0.16, 0.02),
-    n.I = safety_design$n.I,
-    a = safety_design$lower$bound,
-    b = safety_design$upper$bound
-  )
-  output <- safety_power |>
-    as_table() |>
-    as_gt(
-      theta_label = gt::html("Underlying<br>AE rate"),
-      prob_decimals = 3,
-      bound_label = c("low rate", "high rate")
-    )
-
-  expect_s3_class(output, "gt_tbl")
-
-  local_edition(3)
-  expect_snapshot_output(gt_to_latex(output))
-})
-
-test_that("Number of set decimals for `Probability of crossing` are respected", {
-  safety_design <- binomialSPRT(p0 = 0.04, p1 = 0.1, alpha = 0.04, beta = 0.2, minn = 4, maxn = 75)
-  safety_power <- gsBinomialExact(
-    k = length(safety_design$n.I),
-    theta = seq(0.02, 0.16, 0.02),
-    n.I = safety_design$n.I,
-    a = safety_design$lower$bound,
-    b = safety_design$upper$bound
-  )
-  output <- safety_power |>
-    as_table() |>
-    as_gt(
-      theta_label = gt::html("Underlying<br>AE rate"),
-      prob_decimals = 3,
-      bound_label = c("low rate", "high rate")
-    )
-
-  # Get formats where columns include "Lower" or "Upper"
-  prob_format <- Filter(function(x) any(x$columns %in% c("Lower", "Upper")), output[["_formats"]])
-
-  # Get formats where columns include "theta"
-  rr_format <- Filter(function(x) any(x$columns == "theta"), output[["_formats"]])
-
-  expect_true(all(sapply(prob_format, function(x) x$decimals == 3)))
-  expect_true(all(sapply(rr_format, function(x) x$decimals == 1)))
-})
-
-test_that("We can set custom title and subtitles", {
-  safety_design <- binomialSPRT(p0 = 0.04, p1 = 0.1, alpha = 0.04, beta = 0.2, minn = 4, maxn = 75)
-  safety_power <- gsBinomialExact(
-    k = length(safety_design$n.I),
-    theta = seq(0.02, 0.16, 0.02),
-    n.I = safety_design$n.I,
-    a = safety_design$lower$bound,
-    b = safety_design$upper$bound
-  )
-  output <- safety_power |>
-    as_table() |>
-    as_gt(
-      title = "Custom Title",
-      subtitle = "Custom Subtitle",
-      theta_label = gt::html("Underlying<br>AE rate"),
-      prob_decimals = 3,
-      bound_label = c("low rate", "high rate")
-    )
-
-  expect_equal(output[["_heading"]]$title, "Custom Title")
-  expect_equal(output[["_heading"]]$subtitle, "Custom Subtitle")
-})
diff --git a/tests/testthat/test-independent-test-lt.R b/tests/testthat/test-independent-test-lt.R
new file mode 100644
index 00000000..862fe255
--- /dev/null
+++ b/tests/testthat/test-independent-test-lt.R
@@ -0,0 +1,48 @@
+safety_lt <- function(...) {
+  safety_design <- binomialSPRT(p0 = 0.04, p1 = 0.1, alpha = 0.04, beta = 0.2, minn = 4, maxn = 75)
+  safety_power <- gsBinomialExact(
+    k = length(safety_design$n.I),
+    theta = seq(0.02, 0.16, 0.02),
+    n.I = safety_design$n.I,
+    a = safety_design$lower$bound,
+    b = safety_design$upper$bound
+  )
+  safety_power |>
+    as_table() |>
+    lt::lt(...)
+}
+
+# Number formatting operations recorded on an lt_tbl for the given columns.
+fmt_for <- function(x, cols) {
+  Filter(
+    function(op) op$type == "fmt_number" && any(op$columns %in% cols),
+    x$ops
+  )
+}
+
+test_that("Number of set decimals for `Probability of crossing` are respected", {
+  output <- safety_lt(
+    theta_label = I("Underlying<br>AE rate"),
+    prob_decimals = 3,
+    bound_label = c("low rate", "high rate")
+  )
+
+  prob_format <- fmt_for(output, c("Lower", "Upper"))
+  rr_format <- fmt_for(output, "theta")
+
+  expect_true(all(vapply(prob_format, function(x) x$decimals == 3, logical(1))))
+  expect_true(all(vapply(rr_format, function(x) x$decimals == 0, logical(1))))
+})
+
+test_that("We can set custom title and subtitles", {
+  output <- safety_lt(
+    title = "Custom Title",
+    subtitle = "Custom Subtitle",
+    theta_label = I("Underlying<br>AE rate"),
+    prob_decimals = 3,
+    bound_label = c("low rate", "high rate")
+  )
+
+  expect_equal(output$header$title, "Custom Title")
+  expect_equal(output$header$subtitle, "Custom Subtitle")
+})
diff --git a/vignettes/ConditionalErrorSpending.Rmd b/vignettes/ConditionalErrorSpending.Rmd
index eef6875b..65b20a43 100755
--- a/vignettes/ConditionalErrorSpending.Rmd
+++ b/vignettes/ConditionalErrorSpending.Rmd
@@ -1,6 +1,8 @@
 ---
 title: "Conditional error spending functions"
-output: rmarkdown::html_vignette
+output:
+  rmarkdown::html_vignette:
+    css: vignette.css
 bibliography: gsDesign.bib
 vignette: >
   %\VignetteIndexEntry{Conditional error spending functions}
@@ -53,7 +55,7 @@ We also compare results to other commonly used spending functions.
 library(gsDesign)
 library(tibble)
 library(dplyr)
-library(gt)
+library(lt)
 ```
 
 ### Method 1
@@ -247,21 +249,22 @@ xx <- rbind(
   transpose_df(ce(x1.8)) |> mutate(gamma = "gamma = 0.8")
 )
 xx |>
-  gt(groupname_col = "gamma") |>
-  tab_spanner(label = "Analysis", columns = 2:5) |>
-  fmt_number(columns = 2:5, decimals = 3) |>
-  tab_options(data_row.padding = px(1)) |>
-  tab_header(
+  lt() |>
+  lt_group(~ gamma, sep = TRUE, sort = FALSE) |>
+  lt_spanner(label = "Analysis", columns = 2:5) |>
+  lt_format(columns = 2:5, decimals = 3) |>
+  lt_label(rowname = "") |>
+  lt_header(
     title = "Xi-Gallo, Method 1 Spending Function",
     subtitle = "Conditional Error Spending Functions"
   ) |>
-  tab_footnote(
-    footnote = "Conditional Error not accounting for future interim bounds.",
-    locations = cells_stub(rows = seq(2, 20, 3))
+  lt_footnote(
+    "Conditional Error not accounting for future interim bounds.",
+    where = "body", columns = "rowname", rows = seq(2, 20, 3)
   ) |>
-  tab_footnote(
-    footnote = "CE = Conditional Error accounting for all analyses.",
-    locations = cells_stub(rows = seq(3, 21, 3))
+  lt_footnote(
+    "CE = Conditional Error accounting for all analyses.",
+    where = "body", columns = "rowname", rows = seq(3, 21, 3)
   )
 ```
 
@@ -288,19 +291,20 @@ xx <- rbind(
   transpose_df(ce(x1.8)) |> mutate(gamma = "gamma = 0.8")
 )
 xx |>
-  gt(groupname_col = "gamma") |>
-  tab_spanner(label = "Analysis", columns = 2:5) |>
-  fmt_number(columns = 2:5, decimals = 3) |>
-  tab_options(data_row.padding = px(1)) |>
-  tab_footnote(
-    footnote = "Conditional Error not accounting for future interim bounds.",
-    locations = cells_stub(rows = seq(2, 20, 3))
+  lt() |>
+  lt_group(~ gamma, sep = TRUE, sort = FALSE) |>
+  lt_spanner(label = "Analysis", columns = 2:5) |>
+  lt_format(columns = 2:5, decimals = 3) |>
+  lt_label(rowname = "") |>
+  lt_footnote(
+    "Conditional Error not accounting for future interim bounds.",
+    where = "body", columns = "rowname", rows = seq(2, 20, 3)
   ) |>
-  tab_footnote(
-    footnote = "CE = Conditional Error accounting for all analyses.",
-    locations = cells_stub(rows = seq(3, 21, 3))
+  lt_footnote(
+    "CE = Conditional Error accounting for all analyses.",
+    where = "body", columns = "rowname", rows = seq(3, 21, 3)
   ) |>
-  tab_header(
+  lt_header(
     title = "Xi-Gallo, Method 2 Spending Function",
     subtitle = "Conditional Error Spending Functions"
   )
@@ -353,19 +357,20 @@ xx <- rbind(
   transpose_df(ce(x3.05)) |> mutate(gamma = "gamma = 0.05 ")
 )
 xx |>
-  gt(groupname_col = "gamma") |>
-  tab_spanner(label = "Analysis", columns = 2:5) |>
-  fmt_number(columns = 2:5, decimals = 3) |>
-  tab_options(data_row.padding = px(1)) |>
-  tab_footnote(
-    footnote = "Conditional Error not accounting for future interim bounds.",
-    locations = cells_stub(rows = seq(2, 11, 3))
+  lt() |>
+  lt_group(~ gamma, sep = TRUE, sort = FALSE) |>
+  lt_spanner(label = "Analysis", columns = 2:5) |>
+  lt_format(columns = 2:5, decimals = 3) |>
+  lt_label(rowname = "") |>
+  lt_footnote(
+    "Conditional Error not accounting for future interim bounds.",
+    where = "body", columns = "rowname", rows = seq(2, 11, 3)
   ) |>
-  tab_footnote(
-    footnote = "CE = Conditional Error accounting for all analyses.",
-    locations = cells_stub(rows = seq(3, 12, 3))
+  lt_footnote(
+    "CE = Conditional Error accounting for all analyses.",
+    where = "body", columns = "rowname", rows = seq(3, 12, 3)
   ) |>
-  tab_header(
+  lt_header(
     title = "Xi-Gallo, Method 3 Spending Function",
     subtitle = "Conditional Error Spending Functions"
   )
diff --git a/vignettes/MultiSeasonRareEvents.Rmd b/vignettes/MultiSeasonRareEvents.Rmd
index 2e05ea04..897a0ce2 100644
--- a/vignettes/MultiSeasonRareEvents.Rmd
+++ b/vignettes/MultiSeasonRareEvents.Rmd
@@ -1,6 +1,8 @@
 ---
 title: "Multi-season studies for rare events"
-output: rmarkdown::html_vignette
+output:
+  rmarkdown::html_vignette:
+    css: vignette.css
 bibliography: gsDesign.bib
 vignette: >
   %\VignetteIndexEntry{Multi-season studies for rare events}
@@ -25,7 +27,7 @@ options(width = 58)
 
 ```{r, message=FALSE, warning=FALSE}
 library(gsDesign)
-library(gt)
+library(lt)
 library(tibble)
 ```
 
@@ -249,46 +251,46 @@ tibble(
   `Achieved cumulative alpha spend` = achieved_alpha_spend,
   `Cumulative power under H1` = achieved_power_h1
 ) |>
-  gt() |>
-  fmt_number(columns = 2, decimals = 3) |>
-  fmt_percent(columns = c(`VE at bound (efficacy)`, `VE at bound (futility)`), decimals = 1) |>
-  fmt_number(
+  lt() |>
+  lt_format(columns = 2, decimals = 3) |>
+  lt_format(columns = c("VE at bound (efficacy)", "VE at bound (futility)"), decimals = 1, percent = TRUE) |>
+  lt_format(
     columns = c(
-      `Nominal 1-sided p at bound (efficacy)`,
-      `Nominal 1-sided p at bound (futility)`,
-      `Target cumulative alpha spend`,
-      `Achieved cumulative alpha spend`,
-      `Cumulative power under H1`
+      "Nominal 1-sided p at bound (efficacy)",
+      "Nominal 1-sided p at bound (futility)",
+      "Target cumulative alpha spend",
+      "Achieved cumulative alpha spend",
+      "Cumulative power under H1"
     ),
     decimals = 4
   ) |>
-  tab_spanner(
+  lt_spanner(
     label = "Efficacy",
     columns = c(
-      `Exact efficacy bound (x <= a)`,
-      `VE at bound (efficacy)`,
-      `Nominal 1-sided p at bound (efficacy)`
+      "Exact efficacy bound (x <= a)",
+      "VE at bound (efficacy)",
+      "Nominal 1-sided p at bound (efficacy)"
     )
   ) |>
-  tab_spanner(
+  lt_spanner(
     label = "Futility",
     columns = c(
-      `Exact futility bound (x >= b)`,
-      `VE at bound (futility)`,
-      `Nominal 1-sided p at bound (futility)`
+      "Exact futility bound (x >= b)",
+      "VE at bound (futility)",
+      "Nominal 1-sided p at bound (futility)"
     )
   ) |>
-  tab_header(
+  lt_header(
     title = "Planned exact binomial seasonal monitoring",
     subtitle = "Super-superiority example with Pocock-like efficacy spending"
   ) |>
-  tab_footnote(
-    footnote = "x denotes cumulative observed events in the experimental arm; efficacy is established when x is at or below the listed efficacy bound.",
-    locations = cells_column_labels(columns = `Exact efficacy bound (x <= a)`)
+  lt_footnote(
+    "x denotes cumulative observed events in the experimental arm; efficacy is established when x is at or below the listed efficacy bound.",
+    where = "column", columns = "Exact efficacy bound (x <= a)"
   ) |>
-  tab_footnote(
-    footnote = "Blank futility entries indicate no futility stopping boundary at that analysis.",
-    locations = cells_column_labels(columns = `Exact futility bound (x >= b)`)
+  lt_footnote(
+    "Blank futility entries indicate no futility stopping boundary at that analysis.",
+    where = "column", columns = "Exact futility bound (x >= b)"
   )
 ```
 
@@ -315,8 +317,8 @@ dplyr::bind_rows(
     `Cumulative planned enrollment` = sum(enrollment_table$`Total planned enrollment`)
   )
 ) |>
-  gt() |>
-  tab_header(title = "Planned enrollment by season and overall")
+  lt() |>
+  lt_header(title = "Planned enrollment by season and overall")
 ```
 
 ## Example repeated and sequential p-values
@@ -390,8 +392,8 @@ tibble(
   `Updated futility bound, default spending` = update_exact$upper$bound,
   `Updated futility bound, maxSpend=TRUE` = update_exact_full$upper$bound
 ) |>
-  gt() |>
-  tab_header(title = "Updated exact bounds using observedEvents")
+  lt() |>
+  lt_header(title = "Updated exact bounds using observedEvents")
 ```
 
 ## Lightweight runnable simulation
@@ -439,16 +441,16 @@ oc <- sim_light$summary |>
   )
 
 oc |>
-  gt() |>
-  fmt_number(columns = 2:5, decimals = 4) |>
-  fmt_number(columns = 6:8, decimals = 2) |>
-  tab_header(
+  lt() |>
+  lt_format(columns = 2:5, decimals = 4) |>
+  lt_format(columns = 6:8, decimals = 2) |>
+  lt_header(
     title = "Lightweight simulation results",
     subtitle = "Exact-binomial monitoring with seasonal analyses"
   ) |>
-  tab_footnote(
-    footnote = "For VE=30% scenarios, efficacy crossing probability is Type I error under the non-binding futility convention (futility crossings do not block later efficacy crossings).",
-    locations = cells_column_labels(columns = `Efficacy crossing probability`)
+  lt_footnote(
+    "For VE=30% scenarios, efficacy crossing probability is Type I error under the non-binding futility convention (futility crossings do not block later efficacy crossings).",
+    where = "column", columns = "Efficacy crossing probability"
   )
 ```
 
@@ -516,16 +518,16 @@ tibble(
     low$mean_looks[low$adaptive & low$scenario == "H1 (VE=80%)"]
   )
 ) |>
-  gt() |>
-  fmt_number(columns = 2:3, decimals = 4) |>
-  fmt_number(columns = 4:6, decimals = 2) |>
-  tab_header(
+  lt() |>
+  lt_format(columns = 2:3, decimals = 4) |>
+  lt_format(columns = 4:6, decimals = 2) |>
+  lt_header(
     title = "Lower-than-planned event rate illustration",
     subtitle = "Adaptive approach increases enrollment to recover information"
   ) |>
-  tab_footnote(
-    footnote = "Type I error rows use non-binding futility for efficacy crossing probability; futility stopping probability is shown separately.",
-    locations = cells_column_labels(columns = `Efficacy crossing probability`)
+  lt_footnote(
+    "Type I error rows use non-binding futility for efficacy crossing probability; futility stopping probability is shown separately.",
+    where = "column", columns = "Efficacy crossing probability"
   )
 ```
 
diff --git a/vignettes/PoissonMixtureModel.Rmd b/vignettes/PoissonMixtureModel.Rmd
index d3c3e926..f1f39f02 100644
--- a/vignettes/PoissonMixtureModel.Rmd
+++ b/vignettes/PoissonMixtureModel.Rmd
@@ -1,6 +1,8 @@
 ---
 title: "A cure model calendar-based design"
-output: rmarkdown::html_vignette
+output:
+  rmarkdown::html_vignette:
+    css: vignette.css
 bibliography: gsDesign.bib
 vignette: >
   %\VignetteIndexEntry{A cure model calendar-based design}
@@ -26,7 +28,7 @@ library(gsDesign)
 library(dplyr)
 library(tibble)
 library(ggplot2)
-library(gt)
+library(lt)
 ```
 
 ## Introduction
@@ -335,8 +337,8 @@ design_calendar <-
   )
 design_calendar |>
   gsBoundSummary(exclude = c("B-value", "CP", "CP H1", "PP")) |>
-  gt() |>
-  tab_header(
+  lt() |>
+  lt_header(
     title = "Calendar-Based Design",
     subtitle = "Calendar Spending"
   )
diff --git a/vignettes/VaccineEfficacy.Rmd b/vignettes/VaccineEfficacy.Rmd
index beb7ae99..df81ffa4 100644
--- a/vignettes/VaccineEfficacy.Rmd
+++ b/vignettes/VaccineEfficacy.Rmd
@@ -1,6 +1,8 @@
 ---
 title: "Vaccine efficacy trial design"
-output: rmarkdown::html_vignette
+output:
+  rmarkdown::html_vignette:
+    css: vignette.css
 bibliography: gsDesign.bib
 vignette: >
   %\VignetteIndexEntry{Vaccine efficacy trial design}
@@ -25,7 +27,7 @@ options(width = 58)
 
 ```{r, message=FALSE, warning=FALSE}
 library(gsDesign)
-library(gt)
+library(lt)
 ```
 
 ## Introduction
@@ -95,9 +97,8 @@ We also translate several vaccine efficacy values to proportion of events in the
 ve <- c(.5, .6, .65, .7, .75, .8)
 prob_experimental <- ratio / (ratio + 1 / (1 - ve))
 tibble::tibble(VE = ve, "P(Experimental)" = prob_experimental) |>
-  gt() |>
-  tab_options(data_row.padding = px(1)) |>
-  fmt_number(columns = 2, decimals = 3)
+  lt() |>
+  lt_format(columns = 2, decimals = 3)
 ```
 
 
@@ -178,12 +179,11 @@ gsBoundSummary(xx,
   tdigits = 1, logdelta = TRUE, deltaname = "HR", Nname = "Events",
   exclude = c("B-value", "CP", "CP H1", "PP")
 ) |>
-  gt() |>
-  tab_header(
+  lt() |>
+  lt_header(
     title = "Initial group sequential approximation",
     subtitle = "Integer event counts at analyses"
-  ) |>
-  tab_options(data_row.padding = px(1))
+  )
 ```
 
 A textual summary for the design is:
@@ -247,39 +247,39 @@ veTable <- function(xbDesign, tteDesign, ve) {
 
 ```{r, echo=FALSE}
 veTable(xb, x, ve) |>
-  gt() |>
-  fmt_number(columns = 2, decimals = 1) |>
-  fmt_number(columns = c(7:8, 11:16), decimals = 2) |>
-  fmt_number(columns = 9:10, decimals = 4) |>
-  tab_spanner(label = "Experimental Cases at Bound", columns = 5:6, id = "cases") |>
-  tab_spanner(label = "Power by Vaccine Efficacy", columns = 11:16, id = "power") |>
-  tab_spanner(label = "Error Spending", columns = 9:10, id = "spend") |>
-  tab_spanner(label = "Vaccine Efficacy at Bound", columns = 7:8, id = "vebound") |>
-  cols_label(
+  lt() |>
+  lt_format(columns = 2, decimals = 1) |>
+  lt_format(columns = c(7:8, 11:16), decimals = 2) |>
+  lt_format(columns = 9:10, decimals = 4) |>
+  lt_spanner(label = "Experimental Cases at Bound", columns = c("Success", "Futility")) |>
+  lt_spanner(label = "Power by Vaccine Efficacy", columns = paste0(ve * 100, "%")) |>
+  lt_spanner(label = "Error Spending", columns = c("alpha", "beta")) |>
+  lt_spanner(label = "Vaccine Efficacy at Bound", columns = c("ve_efficacy", "ve_futility")) |>
+  lt_label(
     ve_efficacy = "Efficacy",
     ve_futility = "Futility"
   ) |>
-  tab_footnote(
-    footnote = "Cumulative spending at each analysis",
-    locations = cells_column_spanners(spanners = "spend")
+  lt_footnote(
+    "Cumulative spending at each analysis",
+    where = "spanner", columns = "Error Spending"
   ) |>
-  tab_footnote(
-    footnote = "Experimental case counts to cross between success and futility counts do not stop trial",
-    locations = cells_column_spanners(spanners = "cases")
+  lt_footnote(
+    "Experimental case counts to cross between success and futility counts do not stop trial",
+    where = "spanner", columns = "Experimental Cases at Bound"
   ) |>
-  tab_footnote(
-    footnote = "Exact vaccine efficacy required to cross bound",
-    locations = cells_column_spanners(spanners = "vebound")
+  lt_footnote(
+    "Exact vaccine efficacy required to cross bound",
+    where = "spanner", columns = "Vaccine Efficacy at Bound"
   ) |>
-  tab_footnote(
-    footnote = "Cumulative power at each analysis by underlying vaccine efficacy",
-    locations = cells_column_spanners(spanners = "power")
+  lt_footnote(
+    "Cumulative power at each analysis by underlying vaccine efficacy",
+    where = "spanner", columns = "Power by Vaccine Efficacy"
   ) |>
-  tab_footnote(
-    footnote = "alpha-spending for efficacy ignores non-binding futility bound",
-    location = cells_column_labels(columns = alpha)
+  lt_footnote(
+    "alpha-spending for efficacy ignores non-binding futility bound",
+    where = "column", columns = "alpha"
   ) |>
-  tab_header("Design Bounds and Operating Characteristics")
+  lt_header("Design Bounds and Operating Characteristics")
 ```
 The initial approximation of bounds for the exact binomial design was generated from the time-to-event design as follows.
 First, we computed nominal p-value 1-sided bounds under the null hypothesis for the efficacy bounds using the normal approximation that the time-to-event design used:
@@ -423,38 +423,38 @@ We hide the code to produce the table; this is available in package vignette cod
     beta = as.vector(cumsum(ebUpdate$upper$prob[, 2]))
   )
   out_tab <- cbind(out_tab, power_table)
-  out_tab |> gt() |>
-  fmt_number(columns = c(5:6, 9:11), decimals = 2) |>
-  fmt_number(columns = 7:8, decimals = 4) |>
-  tab_spanner(label = "Cases at Bound", columns = 3:4, id = "cases") |>
-  tab_spanner(label = "Power by VE", columns = 9:11, id = "power") |>
-  tab_spanner(label = "Error Spending", columns = 7:8, id = "spend") |>
-  tab_spanner(label = "VE at Bound", columns = 5:6, id = "vebound") |>
-  cols_label(
+  out_tab |> lt() |>
+  lt_format(columns = c(5:6, 9:11), decimals = 2) |>
+  lt_format(columns = 7:8, decimals = 4) |>
+  lt_spanner(label = "Cases at Bound", columns = c("Success", "Futility")) |>
+  lt_spanner(label = "Power by VE", columns = paste0(ve * 100, "%")) |>
+  lt_spanner(label = "Error Spending", columns = c("alpha", "beta")) |>
+  lt_spanner(label = "VE at Bound", columns = c("ve_efficacy", "ve_futility")) |>
+  lt_label(
     ve_efficacy = "Efficacy",
     ve_futility = "Futility"
   ) |>
-  tab_footnote(
-    footnote = "Cumulative spending at each analysis",
-    locations = cells_column_spanners(spanners = "spend")
+  lt_footnote(
+    "Cumulative spending at each analysis",
+    where = "spanner", columns = "Error Spending"
   ) |>
-  tab_footnote(
-    footnote = "Experimental case counts; counts between success and futility bounds do not stop trial",
-    locations = cells_column_spanners(spanners = "cases")
+  lt_footnote(
+    "Experimental case counts; counts between success and futility bounds do not stop trial",
+    where = "spanner", columns = "Cases at Bound"
   ) |>
-  tab_footnote(
-    footnote = "Exact vaccine efficacy required to cross bound",
-    locations = cells_column_spanners(spanners = "vebound")
+  lt_footnote(
+    "Exact vaccine efficacy required to cross bound",
+    where = "spanner", columns = "VE at Bound"
   ) |>
-  tab_footnote(
-    footnote = "Cumulative power at each analysis by underlying vaccine efficacy",
-    locations = cells_column_spanners(spanners = "power")
+  lt_footnote(
+    "Cumulative power at each analysis by underlying vaccine efficacy",
+    where = "spanner", columns = "Power by VE"
   ) |>
-  tab_footnote(
-    footnote = "Efficacy spending ignores non-binding futility bound",
-    location = cells_column_labels(columns = alpha)
+  lt_footnote(
+    "Efficacy spending ignores non-binding futility bound",
+    where = "column", columns = "alpha"
   ) |>
-  tab_header(title = "Updated Bounds for Actual Analyses from SPUTNIK trial")
+  lt_header(title = "Updated Bounds for Actual Analyses from SPUTNIK trial")
 ```
 
 ## Summary
diff --git a/vignettes/binomialSPRTExample.Rmd b/vignettes/binomialSPRTExample.Rmd
index deed99a7..72037d47 100644
--- a/vignettes/binomialSPRTExample.Rmd
+++ b/vignettes/binomialSPRTExample.Rmd
@@ -1,6 +1,8 @@
 ---
 title: "Binomial SPRT"
-output: rmarkdown::html_vignette
+output:
+  rmarkdown::html_vignette:
+    css: vignette.css
 bibliography: "gsDesign.bib"
 vignette: >
   %\VignetteIndexEntry{Binomial SPRT}
@@ -91,7 +93,7 @@ b_power <- gsBinomialExact(
 ```{r}
 b_power |>
   as_table() |>
-  as_gt()
+  lt::lt()
 ```
 
 ## Safety monitoring example
@@ -131,8 +133,8 @@ safety_power <- gsBinomialExact(
 )
 safety_power |>
   as_table() |>
-  as_gt(
-    theta_label = gt::html("Underlying<br>AE rate"),
+  lt::lt(
+    theta_label = I("Underlying<br>AE rate"),
     prob_decimals = 3,
     bound_label = c("low rate", "high rate")
   )
diff --git a/vignettes/binomialTwoSample.Rmd b/vignettes/binomialTwoSample.Rmd
index 9a87124b..1c284ab8 100644
--- a/vignettes/binomialTwoSample.Rmd
+++ b/vignettes/binomialTwoSample.Rmd
@@ -1,6 +1,8 @@
 ---
 title: "Binomial two arm trial design and analysis"
-output: rmarkdown::html_vignette
+output:
+  rmarkdown::html_vignette:
+    css: vignette.css
 bibliography: "gsDesign.bib"
 vignette: >
   %\VignetteIndexEntry{Binomial two arm trial design and analysis}
@@ -41,7 +43,7 @@ The R packages we use are:
 library(gsDesign)
 library(ggplot2)
 library(tidyr)
-library(gt)
+library(lt)
 library(dplyr)
 ```
 
@@ -72,8 +74,8 @@ tibble(scale, "Sample size" = c(
   nBinomial(p1 = 0.2, p2 = 0.1, ratio = 0.5, alpha = 0.025, beta = 0.15, scale = scale[2]) |> ceiling(),
   nBinomial(p1 = 0.2, p2 = 0.1, ratio = 0.5, alpha = 0.025, beta = 0.15, scale = scale[3]) |> ceiling()
 )) |>
-  gt() |>
-  tab_header("Sample size by scale for a superiority design",
+  lt() |>
+  lt_header("Sample size by scale for a superiority design",
     subtitle = "alpha = 0.025, beta = 0.15, pE = 0.2, pC = 0.1"
   )
 ```
@@ -131,11 +133,11 @@ rbind(
     scale = c("Risk difference", "Risk-ratio", "Odds-ratio"),
     Effect = c(rd, rr, orr)
   ) |>
-  gt() |>
-  tab_header("Confidence intervals for a binomial effect size",
+  lt() |>
+  lt_header("Confidence intervals for a binomial effect size",
     subtitle = "x1 = 20, n1 = 30, x2 = 10, n2 = 30"
   ) |>
-  fmt_number(columns = c(lower, upper, Effect), n_sigfig = 3)
+  lt_format(columns = c("lower", "upper", "Effect"), decimals = 3)
 ```
 
 Again, how treatment groups are assigned makes a difference.
@@ -166,19 +168,19 @@ tibble(
     ceiling(nBinomial(p1 = 0.2, p2 = 0.1, alpha = 0.025, beta = 0.15, ratio = 0.5, delta0 = 0.02))
   )
 ) |>
-  gt() |>
-  tab_header("Sample size for binomial two arm trial design",
+  lt() |>
+  lt_header("Sample size for binomial two arm trial design",
     subtitle = "alpha = 0.025, beta = 0.15"
   ) |>
-  fmt_number(columns = c(`p1 (pE)`, `p2 (pC)`), decimals = 2) |>
-  cols_label(
+  lt_format(columns = c("p1 (pE)", "p2 (pC)"), decimals = 2) |>
+  lt_label(
     Design = "Design",
     `p1 (pE)` = "Experimental group rate",
     `p2 (pC)` = "Control group rate",
     delta0 = "Null hypothesis value of rate difference (delta0)",
     `Sample size` = "Sample size"
   ) |>
-  tab_footnote("Randomization ratio is 2:1 (Experimental:Control) with assumed control failure rate p1 = 0.2 and experimental rate 0.1.")
+  lt_note("Randomization ratio is 2:1 (Experimental:Control) with assumed control failure rate p1 = 0.2 and experimental rate 0.1.")
 ```
 
 Testing for non-inferiority and super-superiority is equivalent to whether or not the confidence intervals contain the margin `delta0`.
@@ -224,12 +226,12 @@ In any case, this produces a slightly conservative Type I error rate.
 ```{r}
 zcut <- quantile(z, 0.975)
 tibble("Z cutoff" = zcut, "p cutoff" = pnorm(zcut, lower.tail = FALSE)) |>
-  gt() |>
-  fmt_number(columns = c("Z cutoff", "p cutoff"), n_sigfig = 3) |>
-  tab_header("Exact cutoff for Type I error rate",
+  lt() |>
+  lt_format(columns = c("Z cutoff", "p cutoff"), decimals = 3) |>
+  lt_header("Exact cutoff for Type I error rate",
     subtitle = "Based on 1 million simulations"
   ) |>
-  tab_footnote("The Z cutoff is the quantile of the simulated Z-values at 0.975 using p1 = p2 = 0.15.")
+  lt_note("The Z cutoff is the quantile of the simulated Z-values at 0.975 using p1 = p2 = 0.15.")
 ```
 
 Now we examine power with the asymptotic and exact cutoffs.
@@ -254,14 +256,14 @@ ptab <- tibble(
   )
 )
 ptab |>
-  gt() |>
-  tab_header("Simulation power for sample size based on risk-difference and odds-ratio",
+  lt() |>
+  lt_header("Simulation power for sample size based on risk-difference and odds-ratio",
     subtitle = "pE = 0.2, pC = 0.1, alpha = 0.025, beta = 0.15"
   ) |>
-  fmt_number(columns = c(n, Power), n_sigfig = 3) |>
-  cols_label(Scale = "Scale", n = "Sample size", Power = "Power") |>
-  tab_footnote("Power based on 100,000 simulated trials and nominal alpha = 0.025 test; 2 x simulation error = 0.002") |>
-  tab_footnote("Power based on Z-test for risk-difference with no continuity correction.", location = cells_column_labels("Power"))
+  lt_format(columns = c("n", "Power"), decimals = 3) |>
+  lt_label(Scale = "Scale", n = "Sample size", Power = "Power") |>
+  lt_footnote("Power based on Z-test for risk-difference with no continuity correction.", where = "column", columns = "Power") |>
+  lt_note("Power based on 100,000 simulated trials and nominal alpha = 0.025 test; 2 x simulation error = 0.002")
 ```
 
 ## Power table
@@ -280,9 +282,9 @@ binomialPowerTable(
   ratio = 1, alpha = 0.025, simulation = TRUE, nsim = 1e6, adj = 0
 ) |>
   rename("Type I error" = "Power") |>
-  gt() |>
-  fmt_number(columns = "Type I error", n_sigfig = 3) |>
-  tab_header("Type I error is not controlled with nominal p = 0.025 cutoff")
+  lt() |>
+  lt_format(columns = "Type I error", decimals = 3) |>
+  lt_header("Type I error is not controlled with nominal p = 0.025 cutoff")
 ```
 
 Adding the continuity correction (`adj = 1`) helps a small amount at better controlling Type I error in this case.
@@ -295,9 +297,9 @@ binomialPowerTable(
   ratio = 1, alpha = 0.023, simulation = TRUE, nsim = 1e6, adj = 0
 ) |>
   rename("Type I error" = "Power") |>
-  gt() |>
-  fmt_number(columns = "Type I error", n_sigfig = 3) |>
-  tab_header("Type I error is controlled at 0.025 with nominal p = 0.023 cutoff")
+  lt() |>
+  lt_format(columns = "Type I error", decimals = 3) |>
+  lt_header("Type I error is controlled at 0.025 with nominal p = 0.023 cutoff")
 ```
 
 Now we look at power for a range of control rates and treatment effects.
@@ -367,13 +369,13 @@ power_table_simulation |>
     values_from = Power
   ) |>
   dplyr::rename(`Control group rate` = pC) |>
-  gt::gt() |>
-  gt::tab_spanner(
+  lt::lt() |>
+  lt::lt_spanner(
     label = "Treatment effect (delta)",
     columns = 2:7
   ) |>
-  gt::fmt_percent(decimals = 1) |>
-  gt::tab_header("Power by Control Group Rate and Treatment Effect")
+  lt::lt_format(columns = 1:7, decimals = 1, percent = TRUE) |>
+  lt::lt_header("Power by Control Group Rate and Treatment Effect")
 ```
 
 ## Summary
diff --git a/vignettes/gsSurvBasicExamples.Rmd b/vignettes/gsSurvBasicExamples.Rmd
index f0b841f9..9dad6b0f 100644
--- a/vignettes/gsSurvBasicExamples.Rmd
+++ b/vignettes/gsSurvBasicExamples.Rmd
@@ -1,6 +1,8 @@
 ---
 title: "Basic time-to-event group sequential design using gsSurv"
-output: rmarkdown::html_vignette
+output:
+  rmarkdown::html_vignette:
+    css: vignette.css
 bibliography: gsDesign.bib
 vignette: >
   %\VignetteIndexEntry{Basic time-to-event group sequential design using gsSurv}
@@ -204,15 +206,15 @@ An important addition not provided above is that the median time-to-event is ass
 Following are the enrollment rates required to power the trial.
 
 ```{r, warning=FALSE}
-library(gt)
+library(lt)
 library(tibble)
 
 tibble(
   Period = paste("Month", rownames(x$gamma)),
   Rate = as.numeric(x$gamma)
 ) |>
-  gt() |>
-  tab_header(title = "Enrollment rate requirements")
+  lt() |>
+  lt_header(title = "Enrollment rate requirements")
 ```
 
 Next we provide a tabular summary of bounds for the design.
@@ -246,15 +248,15 @@ caption <- paste(
 
 ```{r, echo=TRUE, message=FALSE}
 gsBoundSummary(x) |>
-  gt() |>
-  tab_header(title = "Time-to-event group sequential design") |>
-  cols_align("left") |>
-  tab_footnote(footnoteUS, locations = cells_column_labels(columns = 3)) |>
-  tab_footnote(footnoteLS, locations = cells_column_labels(columns = 4)) |>
-  tab_footnote(footnoteHR, locations = cells_body(columns = 2, rows = c(3, 8, 13))) |>
-  tab_footnote(footnoteM, locations = cells_body(columns = 1, rows = c(4, 9, 14))) |>
-  tab_footnote(footnote1, locations = cells_body(columns = 2, rows = c(4, 5, 9, 10, 14, 15))) |>
-  tab_footnote(footnote2, locations = cells_body(columns = 2, rows = c(4, 9, 14)))
+  lt() |>
+  lt_header(title = "Time-to-event group sequential design") |>
+  lt_align(columns = c("Analysis", "Value", "Efficacy", "Futility"), align = "left") |>
+  lt_footnote(footnoteUS, where = "column", columns = "Efficacy") |>
+  lt_footnote(footnoteLS, where = "column", columns = "Futility") |>
+  lt_footnote(footnoteHR, where = "body", columns = "Value", rows = c(3, 8, 13)) |>
+  lt_footnote(footnoteM, where = "body", columns = "Analysis", rows = c(4, 9, 14)) |>
+  lt_footnote(footnote1, where = "body", columns = "Value", rows = c(4, 5, 9, 10, 14, 15)) |>
+  lt_footnote(footnote2, where = "body", columns = "Value", rows = c(4, 9, 14))
 ```
 
 ### Summary plots
@@ -317,23 +319,23 @@ gsBoundSummary(
     "PP", "P(Cross) if HR=1", "P(Cross) if HR=0.75"
   )
 ) |>
-  gt() |>
-  cols_align("left") |>
-  tab_header(
+  lt() |>
+  lt_align(columns = c("Analysis", "Value", "Efficacy", "Futility"), align = "left") |>
+  lt_header(
     title = "Time-to-event group sequential bound guidance",
     subtitle = "Bounds updated based on event counts through IA2"
   ) |>
-  tab_footnote(
+  lt_footnote(
     "Nominal p-value required to establish statistical significance.",
-    locations = cells_body(columns = 3, rows = c(2, 5, 8))
+    where = "body", columns = "Efficacy", rows = c(2, 5, 8)
   ) |>
-  tab_footnote(
+  lt_footnote(
     "Interim futility guidance based on observed HR is non-binding.",
-    locations = cells_body(columns = 4, rows = c(3, 6))
+    where = "body", columns = "Futility", rows = c(3, 6)
   ) |>
-  tab_footnote(
+  lt_footnote(
     "HR bounds are approximations; decisions on crossing are based solely on p-values.",
-    locations = cells_body(column = 2, rows = c(3, 6, 9))
+    where = "body", columns = "Value", rows = c(3, 6, 9)
   )
 ```
 
diff --git a/vignettes/vignette.css b/vignettes/vignette.css
new file mode 100644
index 00000000..232206e6
--- /dev/null
+++ b/vignettes/vignette.css
@@ -0,0 +1,20 @@
+body {
+  font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
+  font-size: 14px;
+  line-height: 1.35;
+  max-width: 700px;
+  margin: 1.5em auto;
+  padding: 0 1em;
+}
+pre, code {
+  background-color: #f7f7f7;
+  border-radius: 3px;
+  font-size: 90%;
+}
+pre {
+  padding: 10px;
+  white-space: pre-wrap;
+}
+.lt-table {
+  min-width: 70%;
+}