njekin · Sep 13, 2026 · Sep 13, 2026
diff --git a/.Rbuildignore b/.Rbuildignore
@@ -4,5 +4,5 @@
 ^README\.Rmd$
 ^README\.md$
 ^README-.*\.png$
-^NEWS\.md$
 ^cran-comments\.md$
+^\.github$
diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
@@ -0,0 +1,53 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+  workflow_dispatch:
+
+name: R-CMD-check.yaml
+
+permissions:
+  contents: read
+
+jobs:
+  R-CMD-check:
+    runs-on: ${{ matrix.config.os }}
+
+    name: ${{ matrix.config.os }} (${{ matrix.config.r }})
+
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - {os: macos-latest,   r: 'release'}
+          - {os: windows-latest, r: 'release'}
+          - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}
+          - {os: ubuntu-latest,   r: 'release'}
+          - {os: ubuntu-latest,   r: 'oldrel-1'}
+
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+      R_KEEP_PKG_SOURCE: yes
+
+    steps:
+      - uses: actions/checkout@v6
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          r-version: ${{ matrix.config.r }}
+          http-user-agent: ${{ matrix.config.http-user-agent }}
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::rcmdcheck
+          needs: check
+
+      - uses: r-lib/actions/check-r-package@v2
+        with:
+          upload-snapshots: true
+          error-on: '"warning"'
+          build_args: 'c("--no-manual","--compact-vignettes=gs+qpdf")'
diff --git a/.gitignore b/.gitignore
@@ -3,4 +3,3 @@ inst/doc
 .Rhistory
 .pdf
 excluded/
-tests/
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,24 +1,25 @@
 Package: toxplot
 Type: Package
 Title: Batch Processing, Modeling and Visualizing the Dose-Response of High-Throughput Screening Bioassay
-Version: 0.1.1
-Author: Jun Wang
-Maintainer: Jun Wang <njekin@gmail.com>
+Version: 0.1.2
+Authors@R: person("Jun", "Wang", email = "njekin@gmail.com",
+    role = c("aut", "cre"))
 Description: A convenient interface to batch process high-throughput toxicology bioassay screening data. It's designed specifically for screening experiment that features a primary inhibition-type assay and a companion cytotoxicity assay. This package provides functions for data normalization, quality-control analysis, dose-response curve fitting (using the Hill model provided in the 'tcpl' package), visualization, and a unique toxicity-adjusted potency ranking system.  
-URL: http://github.com/njekin/ToxPlot-R-Package
-License: GPL (>=2)
+URL: https://github.com/njekin/ToxPlot-R-Package
+BugReports: https://github.com/njekin/ToxPlot-R-Package/issues
+License: GPL (>= 2)
 Encoding: UTF-8
 LazyData: true
 Depends: R (>= 3.3.0)
 Imports:
     dplyr (>= 0.5.0),
-    tidyr (>= 0.5.0),
     tcpl (>= 1.0.0),
-    ggplot2 (>= 2.2.0),
+    ggplot2 (>= 3.4.0),
     ggthemes (>= 3.4.0)
 Suggests: knitr,
     rmarkdown,
-    testthat,
-    plotly,
-    devtools,
-RoxygenNote: 6.0.1
+    testthat (>= 3.0.0),
+    plotly
+VignetteBuilder: knitr
+Config/testthat/edition: 3
+RoxygenNote: 7.3.1
diff --git a/NEWS.md b/NEWS.md
@@ -1,3 +1,17 @@
+# toxplot 0.1.2
+
+* Fix the CRAN archive-blocking example failure when `tcplFit()` returns
+  vectors and NULL predictions: model tables now contain one row per fit,
+  with list columns for concentration and prediction vectors.
+* Correct the plate identifier extracted from assay data and use the exact
+  primary assay name when checking the assay configuration.
+* Use `linewidth` for ggplot2 lines and a proper margin object for legends.
+* Close the PDF graphics device even when plot printing fails.
+* Remove the unused tidyr import and development-only devtools suggestion.
+* Build the vignette using the installed package and declare knitr as its builder.
+* Add regression coverage for the complete archived failing example, inactive
+  curves, insufficient concentrations, ranking, plotting and PDF cleanup.
+
 # toxplot v0.1.1
 ------------------
 ## Bug fixes

diff --git a/R/data.R b/R/data.R
@@ -17,3 +17,11 @@
 #'
 
 "demo_mc_norm"
+
+#' Normalized example multi-concentration screening data
+#'
+#' A dataset containing normalized example data for a primary inhibition assay
+#' and the parallel cytotoxicity assay, retained for compatibility.
+#'
+#' @format See vignette for description of the demo data.
+"mc_norm"
diff --git a/R/functions_tcpl_based.R b/R/functions_tcpl_based.R
@@ -82,7 +82,10 @@ auc_hill_tcpl <- function(p, lower, upper) {
 #' @param prim_cutoff significance cutoff for primary assay (eg. 3sigma or 3bMAD)
 #' @param toxi_cutoff significance cutoff for cytotoxicity assay (eg. 3sigma or 3bMAD)
 #'
-#' @return A list object containing modeling results, the corresponding data for each chemical.
+#' @return A list object containing modeling results and the corresponding data
+#' for each chemical. Each model is a one-row data.frame. Vector outputs from
+#' tcpl (logc, hill_modl and gnls_modl) are retained as list columns, including
+#' NULL predictions when a model was not fitted.
 #'
 #' @examples
 #' ## fit curve with default significant threshold 20
@@ -120,7 +123,7 @@ fit_curve_tcpl <- function(df, assay_info, prim_cutoff = 20, toxi_cutoff = 20) {
   # get log concentration
   df <- df %>% dplyr::mutate(logc = log10(conc))
   spid_list <- unique(df$spid)
-  if (is.null(assay_info$prim) & is.null(assay_info$toxi_assay)) {
+  if (is.null(assay_info$prim_assay) & is.null(assay_info$toxi_assay)) {
     stop("assay_info cannot be NULL for both primary and cytotoxicity assay")
   }
   n <- 1
@@ -138,17 +141,17 @@ fit_curve_tcpl <- function(df, assay_info, prim_cutoff = 20, toxi_cutoff = 20) {
     } else {
 
       prim_dt <- df %>% dplyr::filter(spid == id, assay == assay_info$prim_assay)
-      m <- tcpl::tcplFit(logc = prim_dt$logc, resp = 100 - prim_dt$nval_median, bmad_prim)
+      m <- tcpl::tcplFit(logc = prim_dt$logc, resp = 100 - prim_dt$nval_median, bmad = bmad_prim)
       absIC50 <- log_abs_ec(c(m$hill_tp, m$hill_ga, m$hill_gw), 50)
       absIC20 <- log_abs_ec(c(m$hill_tp, m$hill_ga, m$hill_gw), 20)
       m[["absIC20"]] <- absIC20
       m[["absIC50"]] <- absIC50
-      m[["apid"]] <- prim_dt[[1,1]]
+      m[["apid"]] <- prim_dt$apid[1]
       m[["assay"]] <- assay_info$prim_assay
       m[["spid"]] <- id
       #prim_md <- dplyr::bind_rows(prim_md, m)
       # print(m)
-      prim_md <- data.frame(m) %>% dplyr::select(apid, assay, spid, everything())
+      prim_md <- tcpl_fit_row(m) %>% dplyr::select(apid, assay, spid, everything())
     }
 
     # model cytotox data
@@ -158,17 +161,17 @@ fit_curve_tcpl <- function(df, assay_info, prim_cutoff = 20, toxi_cutoff = 20) {
       toxi_dt <- NA
     } else {
       toxi_dt <- df %>% dplyr::filter(spid == id, assay==assay_info$toxi_assay)
-      m <- tcpl::tcplFit(logc = toxi_dt$logc, resp = 100 - toxi_dt$nval_median, bmad_toxi)
+      m <- tcpl::tcplFit(logc = toxi_dt$logc, resp = 100 - toxi_dt$nval_median, bmad = bmad_toxi)
       absIC50 <- log_abs_ec(c(m$hill_tp, m$hill_ga, m$hill_gw), 50)
       absIC20 <- log_abs_ec(c(m$hill_tp, m$hill_ga, m$hill_gw), 20)
       m[["absIC20"]] <- absIC20
       m[["absIC50"]] <- absIC50
-      m[["apid"]] <- toxi_dt[[1,1]]
+      m[["apid"]] <- toxi_dt$apid[1]
       m[["assay"]] <- assay_info$toxi_assay
       m[["spid"]] <- id
       #toxi_md <- dplyr::bind_rows(toxi_md, m)
 
-      toxi_md <- data.frame(m) %>% dplyr::select(apid, assay, spid, everything())
+      toxi_md <- tcpl_fit_row(m) %>% dplyr::select(apid, assay, spid, everything())
 
     }
 
@@ -701,13 +704,13 @@ plot_tcpl <-
       #test and plot cytotox model
       if (!is.na(m_toxi$hill) & m_toxi$hill == 1) {
         para_cyto <- c(m_toxi$hill_tp, m_toxi$hill_ga, m_toxi$hill_gw)
-        p1 <- 100 - hill_model(para_cyto, s)
+        p1 <- 100 - hill_model(para_cyto, s$logc)
         p1 <- dplyr::bind_cols(s, data.frame(p1))
         names(p1) <- c("logc", "pred")
         g <- g + geom_line(
           data = p1,
           aes(x = logc, y = pred),
-          size = 2,
+          linewidth = 2,
           alpha = 0.9,
           color = "#e02929"
         )
@@ -720,13 +723,13 @@ plot_tcpl <-
       if (!is.na(m_prim$hill) & m_prim$hill == 1) {
         para_raiu <- c(m_prim$hill_tp, m_prim$hill_ga, m_prim$hill_gw)
 
-        p2 <- 100 - hill_model(para_raiu, s)
+        p2 <- 100 - hill_model(para_raiu, s$logc)
         p2 <- bind_cols(s, data.frame(p2))
         names(p2) <- c("logc", "pred")
         g <- g + geom_line(
           data = p2,
           aes(x = logc, y = pred),
-          size = 2,
+          linewidth = 2,
           alpha = 0.9,
           color = "#377eb8"
         )
@@ -738,7 +741,7 @@ plot_tcpl <-
         geom_hline(
           yintercept = 100 - cutoff_toxi,
           alpha = 0.5,
-          size = 0.5,
+          linewidth = 0.5,
           linetype = "dashed",
           color = "#e02929"
         )
@@ -747,7 +750,7 @@ plot_tcpl <-
         geom_hline(
           yintercept = 100 - cutoff_prim,
           alpha = 0.5,
-          size = 0.5,
+          linewidth = 0.5,
           linetype = "dashed",
           color = "#377eb8"
         )
@@ -948,13 +951,13 @@ plot_tcpl_minimal <-
       #test and plot cytotox model
       if (!is.na(m_toxi$hill) & m_toxi$hill == 1) {
         para_cyto <- c(m_toxi$hill_tp, m_toxi$hill_ga, m_toxi$hill_gw)
-        p1 <- 100 - hill_model(para_cyto, s)
+        p1 <- 100 - hill_model(para_cyto, s$logc)
         p1 <- dplyr::bind_cols(s, data.frame(p1))
         names(p1) <- c("logc", "pred")
         g <- g + geom_line(
           data = p1,
           aes(x = logc, y = pred),
-          size = 2,
+          linewidth = 2,
           alpha = 0.9,
           color = "#e02929"
         )
@@ -967,13 +970,13 @@ plot_tcpl_minimal <-
       if (!is.na(m_prim$hill) & m_prim$hill == 1) {
         para_raiu <- c(m_prim$hill_tp, m_prim$hill_ga, m_prim$hill_gw)
 
-        p2 <- 100 - hill_model(para_raiu, s)
+        p2 <- 100 - hill_model(para_raiu, s$logc)
         p2 <- bind_cols(s, data.frame(p2))
         names(p2) <- c("logc", "pred")
         g <- g + geom_line(
           data = p2,
           aes(x = logc, y = pred),
-          size = 2,
+          linewidth = 2,
           alpha = 0.9,
           color = "#377eb8"
         )
@@ -985,7 +988,7 @@ plot_tcpl_minimal <-
         geom_hline(
           yintercept = 100 - cutoff_toxi,
           alpha = 0.5,
-          size = 0.5,
+          linewidth = 0.5,
           linetype = "dashed",
           color = "#e02929"
         )
@@ -994,7 +997,7 @@ plot_tcpl_minimal <-
         geom_hline(
           yintercept = 100 - cutoff_prim,
           alpha = 0.5,
-          size = 0.5,
+          linewidth = 0.5,
           linetype = "dashed",
           color = "#377eb8"
         )
@@ -1028,7 +1031,7 @@ plot_tcpl_minimal <-
         theme(axis.title.y = element_blank()) +
         theme(legend.title = element_blank(),
               legend.position = "none") +
-        theme(legend.margin=unit(0, "null")) +
+        theme(legend.margin = margin(0, 0, 0, 0)) +
         scale_color_manual(values=c("#e02929", "#377eb8"))+
         theme(plot.title=element_text(hjust=0.5, size = 16))
 

diff --git a/R/utilities.R b/R/utilities.R
@@ -37,9 +37,9 @@ save_plot_pdf <- function(plot_list, filename) {
   assay <- spid <- apid <- rval <- NULL
 
   grDevices::pdf(filename)
+  on.exit(grDevices::dev.off(), add = TRUE)
   cat("Preparing file...\n")
   invisible(lapply(plot_list, print))
-  grDevices::dev.off()
   cat("Finished!")
 }
 
@@ -82,3 +82,14 @@ generate_index <- function(x) {
   index <- rank(uls)[match(x, uls)]
   return(as.integer(index))
 }
+
+# Preserve vector and NULL components without recycling scalar fit statistics.
+tcpl_fit_row <- function(x) {
+  vector_fields <- c("logc", "hill_modl", "gnls_modl")
+  for (nm in names(x)) {
+    if (nm %in% vector_fields || length(x[[nm]]) != 1L) {
+      x[nm] <- list(I(list(x[[nm]])))
+    }
+  }
+  as.data.frame(x, optional = TRUE, stringsAsFactors = FALSE)
+}
diff --git a/README.Rmd b/README.Rmd
@@ -30,12 +30,18 @@ Below you will find a brief demo usage of the package.
 
 # Installation
 
+Version 0.1.2 is a maintenance candidate for resubmission to CRAN.
+The previous release was archived on 2018-12-10; CRAN installation will
+be available again only after the resubmission is accepted.
+The published demo output below is historical and may differ with current tcpl.
+
+
 You can install the package in R from github using *devtools*. 
 ```{r, eval = FALSE}
 devtools::install_github('njekin/ToxPlot-R-Package')
 library(toxplot)
 ```
-or install from CRAN.
+After CRAN reinstatement, install from CRAN.
 ```{r, eval = FALSE}
 install.packages("toxplot")
 library(toxplot)
@@ -71,9 +77,8 @@ pc: positive control for celltiter-glo cytotixicity assay (DCNQ)
 # Demo data included in the package
 Load the demo dataset included in the package. Below is the head of the dataframe.
 ```{r, message=FALSE, warning=FALSE}
-library(devtools)
-library(tidyverse)
-load_all()
+library(toxplot)
+library(dplyr)
 
 knitr::kable(head(demo_mc), caption = "Head rows of demo data")
 ```

diff --git a/README.md b/README.md
@@ -18,14 +18,21 @@ Below you will find a brief demo usage of the package.
 Installation
 ============
 
+Version 0.1.2 is a maintenance candidate for resubmission to CRAN.
+The previous release was archived on 2018-12-10; CRAN installation will
+be available again only after the resubmission is accepted.
+The published demo output below is historical and may differ with current tcpl.
+
+
+
 You can install the package in R from github using *devtools*.
 
 ``` r
 devtools::install_github('njekin/ToxPlot-R-Package')
 library(toxplot)
 ```
 
-or install from CRAN.
+After CRAN reinstatement, install from CRAN.
 
 ``` r
 install.packages("toxplot")
@@ -61,9 +68,8 @@ Demo data included in the package
 Load the demo dataset included in the package. Below is the head of the dataframe.
 
 ``` r
-library(devtools)
-library(tidyverse)
-load_all()
+library(toxplot)
+library(dplyr)
 
 knitr::kable(head(demo_mc), caption = "Head rows of demo data")
 ```
-Original file line number
+Diff line change
@@ Expand Up / @@ -3,4 +3,3 @@ inst/doc @@
     .Rhistory
     .pdf
     excluded/
-    tests/