Clean up CI errors

NAMESPACE

@@ -38,6 +38,7 @@ S3method(full_join,PKNCAresults)
 S3method(getDataName,PKNCAconc)
 S3method(getDataName,PKNCAdose)
 S3method(getDataName,PKNCAresults)
+S3method(getDataName,default)
 S3method(getDepVar,PKNCAconc)
 S3method(getDepVar,PKNCAdose)
 S3method(getGroups,PKNCAconc)

R/001-add.interval.col.R

@@ -175,10 +175,10 @@ add.interval.col <- function(name,
   assign("interval.cols", current, envir=.PKNCAEnv)
 }
 
-#' Sort the interval columns by dependencies.
-#'
-#' Columns are always to the right of columns that they depend on.
-sort.interval.cols <- function() {
+# Sort the interval columns by dependencies.
+#
+# Columns are always to the right of columns that they depend on.
+sort_interval_cols <- function() {
   current <- get("interval.cols", envir=.PKNCAEnv)
   # Only sort if necessary
   sort_order <- get0("interval.cols_sorted", envir=.PKNCAEnv)
@@ -232,7 +232,7 @@ sort.interval.cols <- function() {
 #' @family Interval specifications
 #' @export
 get.interval.cols <- function() {
-  sort.interval.cols()
+  sort_interval_cols()
   get("interval.cols", envir=.PKNCAEnv)
 }
 

R/AIC.list.R

@@ -9,7 +9,7 @@
 #'   columns for degrees of freedom (`df`) and `AIC`.  If `assess.best` is true,
 #'   then there will be another column `isBest`.
 #' @noRd
-AIC.list <- function(object, ..., assess.best=TRUE) {
+AIC_list <- function(object, ..., assess.best=TRUE) {
   allAICs <-
     lapply(object, FUN=function(subobject, ...) {
       # Return the AIC of the new model relative to the reference model
@@ -63,11 +63,10 @@ AIC.list <- function(object, ..., assess.best=TRUE) {
 #' Extract the best model from a list of models using the AIC.
 #'
 #' @param object the list of models
-#' @param \dots Parameters passed to AIC.list
+#' @param \dots Parameters passed to AIC_list
 #' @returns The model which is assessed as best.  If more than one are equal,
 #'   the first is chosen.
 #' @export
 get.best.model <- function(object, ...) {
   object[stats::AIC(object, ...)$isBest %in% "Best Model"][[1]]
 }
-

R/class-general.R

@@ -65,6 +65,7 @@ getDataName <- function(object) {
 }
 
 #' @describeIn getDataName If no data name exists, returns NULL.
+#' @export
 getDataName.default <- function(object) {
   NULL
 }

R/tss.monoexponential.R

@@ -225,7 +225,7 @@ pk.tss.monoexponential.population <- function(data,
     }
   }
   # Find the best model of the set and return the output from that one.
-  all.model.summary <- AIC.list(lapply(models, function(x) x$model))
+  all.model.summary <- AIC_list(lapply(models, function(x) x$model))
   rownames(all.model.summary) <-
     vapply(
       X = models,

tests/testthat/test-001-add.interval.col.R

@@ -24,7 +24,7 @@ test_that("add.interval.col", {
     regexp="FUN must be a character string or NA",
     info="interval column function must be a character string or NA"
   )
-  
+
   expect_error(
     add.interval.col(name="a", FUN=NA, datatype="interval", desc="test addition"),
     regexp='argument "unit_type" is missing, with no default'
@@ -33,7 +33,7 @@ test_that("add.interval.col", {
     add.interval.col(name="a", FUN=NA, unit_type="foo", datatype="interval", desc="test addition"),
     regexp="should be one of .*inverse_time"
   )
-  
+
   # pretty_name checks
   expect_error(
     add.interval.col(name="a", FUN=NA, unit_type="conc", pretty_name=1:2, datatype="interval", desc=1),
@@ -47,13 +47,13 @@ test_that("add.interval.col", {
     add.interval.col(name="a", FUN=NA, unit_type="conc", pretty_name="", datatype="interval", desc=1),
     regexp="pretty_name must not be an empty string"
   )
-  
+
   expect_error(
     add.interval.col(name="a", FUN=NA, unit_type="conc", pretty_name="a", datatype="individual"),
     regexp="Only the 'interval' datatype is currently supported.",
     info="interval column datatype must be 'interval'"
   )
-  
+
   expect_error(
     add.interval.col(name="a", FUN=NA, unit_type="conc", pretty_name="a", datatype="interval", desc=1:2),
     regexp="desc must have length == 1",
@@ -69,7 +69,7 @@ test_that("add.interval.col", {
     regexp="'depends' must be NULL or a character vector",
     info="depends column must be a NULL or a character string"
   )
-  
+
   expect_error(
     add.interval.col(name="a", FUN="this function does not exist", unit_type="conc", pretty_name="foo", datatype="interval", desc="test addition"),
     regexp="The function named '.*' is not defined.  Please define the function before calling add.interval.col.",
@@ -99,7 +99,7 @@ test_that("add.interval.col", {
     regexp="All names for the formalsmap list must be arguments to the function",
     info="formalsmap arguments must map to function arguments"
   )
-  
+
   expect_equal(
     {
       add.interval.col(name="a", FUN=NA, unit_type="conc", pretty_name="a", datatype="interval", desc="test addition")
@@ -170,7 +170,7 @@ test_that("fake parameters", {
     depends="does_not_exist"
   )
   expect_error(
-    sort.interval.cols(),
+    sort_interval_cols(),
     regexp="Invalid dependencies for interval column (please report this as a bug): fake_parameter The following dependencies are missing: does_not_exist",
     fixed=TRUE
   )

tests/testthat/test-AIC.list.R → tests/testthat/test-AIC_list.R

@@ -1,4 +1,4 @@
-test_that("AIC.list", {
+test_that("AIC_list", {
   tmpdat <- data.frame(x=c(1, 2, 3), y=c(1, 2.02, 3), z=c(0, 0.01, 0))
   mod1 <- glm(y~x, data=tmpdat)
   mod2 <- glm(y~x+z, data=tmpdat)
@@ -8,7 +8,7 @@ test_that("AIC.list", {
                         indentation=c(1, 0),
                         isBest=c("", "Best Model"),
                         stringsAsFactors=FALSE)
-  expect_equal(AIC.list(d1), result1)
+  expect_equal(AIC_list(d1), result1)
 
   # Check that simple names apply correctly
   d2 <- list(list("A"=mod1), "B"=mod2)
@@ -18,7 +18,7 @@ test_that("AIC.list", {
                         isBest=c("", "Best Model"),
                         stringsAsFactors=FALSE,
                         row.names=c("A", "B"))
-  expect_equal(AIC.list(d2), result2)
+  expect_equal(AIC_list(d2), result2)
 
   d3 <- list("A"=mod1, "B"=mod2)
   result3 <- data.frame(AIC=c(as.numeric(AIC(mod1)), as.numeric(AIC(mod2))),
@@ -27,16 +27,16 @@ test_that("AIC.list", {
                         isBest=c("", "Best Model"),
                         stringsAsFactors=FALSE,
                         row.names=c("A", "B"))
-  expect_equal(AIC.list(d3), result3)
-  
+  expect_equal(AIC_list(d3), result3)
+
   d4 <- list("C"=list("A"=mod1), "B"=mod2)
   result4 <- data.frame(AIC=c(as.numeric(AIC(mod1)), as.numeric(AIC(mod2))),
                         df=c(3, 4),
                         indentation=c(1, 0),
                         isBest=c("", "Best Model"),
                         stringsAsFactors=FALSE,
                         row.names=c("C A", "B"))
-  expect_equal(AIC.list(d4), result4)
+  expect_equal(AIC_list(d4), result4)
 
   d5 <- list("C"=list("A"=mod1), "B"=mod2, C=NA)
   result5 <- data.frame(AIC=c(as.numeric(AIC(mod1)), as.numeric(AIC(mod2)), NA),
@@ -45,8 +45,7 @@ test_that("AIC.list", {
                         isBest=c("", "Best Model", ""),
                         stringsAsFactors=FALSE,
                         row.names=c("C A", "B", "C"))
-  expect_equal(AIC.list(d5), result5)
-
+  expect_equal(AIC_list(d5), result5)
 })
 
 test_that("get.best.model", {