Updating and regenerating documentation

CohortMethod.Rproj

@@ -16,6 +16,7 @@ AutoAppendNewline: Yes
 StripTrailingWhitespace: Yes
 
 BuildType: Package
+PackageCleanBeforeInstall: No
 PackageInstallArgs: --no-multiarch --with-keep.source
 PackageBuildArgs: --resave-data
 PackageRoxygenize: rd,collate,namespace

DESCRIPTION

@@ -2,7 +2,7 @@ Package: CohortMethod
 Type: Package
 Title: New-User Cohort Method with Large Scale Propensity and Outcome Models
 Version: 5.0.0
-Date: 2022-06-21
+Date: 2023-04-17
 Authors@R: c(
 	person("Martijn", "Schuemie", , "[email protected]", role = c("aut", "cre")),
 	person("Marc", "Suchard", role = c("aut")),

R/Export.R

@@ -59,6 +59,13 @@ createCmDiagnosticThresholds <- function(mdrrThreshold = 10,
                                          sdmThreshold = 0.1,
                                          equipoiseThreshold = 0.2,
                                          attritionFractionThreshold = 1) {
+  errorMessages <- checkmate::makeAssertCollection()
+  checkmate::assertNumeric(mdrrThreshold, len = 1, lower = 0, add = errorMessages)
+  checkmate::assertNumeric(easeThreshold, len = 1, lower = 0, add = errorMessages)
+  checkmate::assertNumeric(sdmThreshold, len = 1, lower = 0, add = errorMessages)
+  checkmate::assertNumeric(equipoiseThreshold, len = 1, lower = 0, add = errorMessages)
+  checkmate::assertNumeric(attritionFractionThreshold, len = 1, lower = 0, add = errorMessages)
+  checkmate::reportAssertions(collection = errorMessages)
   thresholds <- list()
   for (name in names(formals(createCmDiagnosticThresholds))) {
     thresholds[[name]] <- get(name)

R/KaplanMeier.R

@@ -16,7 +16,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
 #' Plot the Kaplan-Meier curve
 #'
 #' @description

R/RunAnalyses.R

@@ -215,11 +215,7 @@ runCmAnalyses <- function(connectionDetails,
                           refitPsForEveryStudyPopulation = TRUE,
                           multiThreadingSettings = createMultiThreadingSettings()) {
   errorMessages <- checkmate::makeAssertCollection()
-  if (is(connectionDetails, "connectionDetails")) {
-    checkmate::assertClass(connectionDetails, "connectionDetails", add = errorMessages)
-  } else {
-    checkmate::assertClass(connectionDetails, "ConnectionDetails", add = errorMessages)
-  }
+  checkmate::assertClass(connectionDetails, "ConnectionDetails", add = errorMessages)
   checkmate::assertCharacter(cdmDatabaseSchema, len = 1, add = errorMessages)
   checkmate::assertCharacter(tempEmulationSchema, len = 1, null.ok = TRUE, add = errorMessages)
   checkmate::assertCharacter(exposureDatabaseSchema, len = 1, add = errorMessages)
@@ -1623,7 +1619,7 @@ createReferenceTable <- function(cmAnalysisList,
 #' @param outputFolder       Name of the folder where all the outputs have been written to.
 #'
 #' @return
-#' A tibble containing file nams of artifacts generated for each target-comparator-outcome-analysis combination.
+#' A tibble containing file names of artifacts generated for each target-comparator-outcome-analysis combination.
 #'
 #' @export
 getFileReference <- function(outputFolder) {

R/Simulation.R

@@ -62,9 +62,6 @@ createCohortMethodDataSimulationProfile <- function(cohortMethodData) {
     select("covariateId", "prevalence") %>%
     collect()
 
-  # covariatePrevalence <- sums$prevalence
-  # names(covariatePrevalence) <- sums$covariateId
-
   message("Computing propensity model")
   propensityScore <- createPs(cohortMethodData,
                               maxCohortSizeForFitting = 25000,
@@ -84,7 +81,6 @@ createCohortMethodDataSimulationProfile <- function(cohortMethodData) {
       removeSubjectsWithPriorOutcome = FALSE,
       outcomeId = outcomeId
     )
-    # studyPop <- trimByPsToEquipoise(studyPop)
     studyPop <- matchOnPs(studyPop, caliper = 0.25, caliperScale = "standardized", maxRatio = 1)
     outcomeModel <- fitOutcomeModel(
       population = studyPop,
@@ -172,7 +168,6 @@ simulateCohortMethodData <- function(profile, n = 10000) {
   checkmate::assertInt(n, lower = 1, add = errorMessages)
   checkmate::reportAssertions(collection = errorMessages)
 
-
   message("Generating covariates")
   # Treatment variable is generated elsewhere:
   covariatePrevalence <- profile$covariatePrevalence[names(profile$covariatePrevalence) != "1"]

R/Viewer.R

@@ -13,6 +13,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
 # exportFolder = file.path(folder, "export")
 # library(dplyr)
 
@@ -31,6 +32,13 @@
 #'
 #' @export
 insertExportedResultsInSqlite <- function(sqliteFileName, exportFolder, cohorts) {
+  errorMessages <- checkmate::makeAssertCollection()
+  checkmate::assertCharacter(sqliteFileName, len = 1, add = errorMessages)
+  checkmate::assertCharacter(exportFolder, len = 1, add = errorMessages)
+  checkmate::assertDataFrame(cohorts, add = errorMessages)
+  checkmate::assertNames(colnames(cohorts), must.include = c("cohortId", "cohortName"), add = errorMessages)
+  checkmate::reportAssertions(collection = errorMessages)
+
   ensureInstalled("RSQLite")
   connectionDetails <- DatabaseConnector::createConnectionDetails(
     dbms = "sqlite",
@@ -62,7 +70,6 @@ insertExportedResultsInSqlite <- function(sqliteFileName, exportFolder, cohorts)
 #'                          using the [exportToCsv()] function.
 #' @template Cohorts
 #'
-#'
 #' @return
 #' Does not return anything. Is called for the side-effect of having the results uploaded
 #' to the server.
@@ -73,15 +80,22 @@ uploadExportedResults <- function(connectionDetails,
                                   append = FALSE,
                                   exportFolder,
                                   cohorts) {
-  # ensureInstalled("CohortGenerator")
+  errorMessages <- checkmate::makeAssertCollection()
+  checkmate::assertClass(connectionDetails, "ConnectionDetails", add = errorMessages)
+  checkmate::assertCharacter(databaseSchema, len = 1, add = errorMessages)
+  checkmate::assertLogical(append, len = 1, add = errorMessages)
+  checkmate::assertCharacter(exportFolder, len = 1, add = errorMessages)
+  checkmate::assertDataFrame(cohorts, add = errorMessages)
+  checkmate::assertNames(colnames(cohorts), must.include = c("cohortId", "cohortName"), add = errorMessages)
+  checkmate::reportAssertions(collection = errorMessages)
+
   ensureInstalled("ResultModelManager")
   connection <- DatabaseConnector::connect(connectionDetails)
   on.exit(DatabaseConnector::disconnect(connection))
 
   if (!append) {
     # Create tables
     rdmsFile <- system.file("csv", "resultsDataModelSpecification.csv", package = "CohortMethod")
-    # specification <- CohortGenerator::readCsv(file = rdmsFile)
     specification <- readr::read_csv(file = rdmsFile, show_col_types = FALSE) %>%
       SqlRender::snakeCaseToCamelCaseNames()
     sql <- ResultModelManager::generateSqlSchema(csvFilepath = rdmsFile)
@@ -147,6 +161,10 @@ uploadExportedResults <- function(connectionDetails,
 #'
 #' @export
 launchResultsViewerUsingSqlite <- function(sqliteFileName) {
+  errorMessages <- checkmate::makeAssertCollection()
+  checkmate::assertCharacter(sqliteFileName, len = 1, add = errorMessages)
+  checkmate::reportAssertions(collection = errorMessages)
+
   ensureInstalled("RSQLite")
   connectionDetails <- DatabaseConnector::createConnectionDetails(
     dbms = "sqlite",
@@ -171,6 +189,11 @@ launchResultsViewerUsingSqlite <- function(sqliteFileName) {
 #'
 #' @export
 launchResultsViewer <- function(connectionDetails, databaseSchema) {
+  errorMessages <- checkmate::makeAssertCollection()
+  checkmate::assertClass(connectionDetails, "ConnectionDetails", add = errorMessages)
+  checkmate::assertCharacter(databaseSchema, len = 1, add = errorMessages)
+  checkmate::reportAssertions(collection = errorMessages)
+
   ensureInstalled("ShinyAppBuilder")
   ensureInstalled("markdown")
   aboutModule <- ShinyAppBuilder::createDefaultAboutConfig(

README.md

@@ -81,6 +81,7 @@ User Documentation
 Documentation can be found on the [package website](https://ohdsi.github.io/CohortMethod).
 
 PDF versions of the documentation are also available:
+
 * Vignette: [Single studies using the CohortMethod package](https://raw.githubusercontent.com/OHDSI/CohortMethod/main/inst/doc/SingleStudies.pdf)
 * Vignette: [Running multiple analyses at once using the CohortMethod package](https://raw.githubusercontent.com/OHDSI/CohortMethod/main/inst/doc/MultipleAnalyses.pdf)
 * Package manual: [CohortMethod.pdf](https://raw.githubusercontent.com/OHDSI/CohortMethod/main/extras/CohortMethod.pdf)

_pkgdown.yml

@@ -74,11 +74,20 @@ reference:
       - createMultiThreadingSettings
       - createDefaultMultiThreadingSettings
       - runCmAnalyses
+  - title: "Results of multiple analyses"
+    desc: >
+      Functions for viewing results of multiple analyses.
+    contents:
       - getFileReference
       - getResultsSummary
       - getInteractionResultsSummary
       - createCmDiagnosticThresholds
       - exportToCsv
+      - getResultsDataModel
+      - insertExportedResultsInSqlite
+      - launchResultsViewerUsingSqlite
+      - uploadExportedResults
+      - launchResultsViewer
   - title: "Simulation"
     desc: >
       Functions for simulating cohort method data objects.