version 1.0.0

DESCRIPTION

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+Package: dtComb
+Title: Statistical Combination of Diagnostic Tests
+Description: A system for combining two diagnostic tests using various approaches
+              that include statistical and machine-learning-based methodologies. 
+              These approaches are divided into four groups: linear combination 
+              methods, non-linear combination methods, mathematical operators, 
+              and machine learning algorithms. See 
+              the <http://biosoft.erciyes.edu.tr/app/dtComb> website 
+              for more information, documentation, and examples.
+Version: 1.0.0
+Authors@R: c(person("Serra Ilayda","Yerlitas", role = c("aut", "ctb"),email = "ilaydayerlitas340@gmail.com"),
+            person("Serra Bersan","Gengec", role = c("aut", "ctb"),email = "serrabersan@gmail.com"),
+            person("Necla","Kochan", role = c("aut", "ctb"),email = "necla.kayaalp@gmail.com"),
+            person("Gozde Erturk","Zararsiz", role = c("aut", "ctb"),email = "gozdeerturk9@gmail.com"),
+            person("Selcuk","Korkmaz", role = c("aut", "ctb"),email = "selcukorkmaz@gmail.com"),
+            person("Gokmen","Zararsiz", role = c("aut", "ctb", "cre"),email = "gokmen.zararsiz@gmail.com"))
+URL: https://github.com/gokmenzararsiz/dtComb
+Language: en-US
+Depends: R (>= 3.5.0)
+Imports: pROC (>= 1.18.0), caret, epiR, gam, ggplot2, ggpubr, glmnet,
+        OptimalCutpoints
+License: MIT + file LICENSE
+Encoding: UTF-8
+LazyData: true
+RoxygenNote: 7.2.3
+NeedsCompilation: no
+Packaged: 2023-12-11 21:07:48 UTC; serrailaydayerlitas
+Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
+Config/testthat/edition: 3
+VignetteBuilder: knitr
+Author: Serra Ilayda Yerlitas [aut, ctb],
+  Serra Bersan Gengec [aut, ctb],
+  Necla Kochan [aut, ctb],
+  Gozde Erturk Zararsiz [aut, ctb],
+  Selcuk Korkmaz [aut, ctb],
+  Gokmen Zararsiz [aut, ctb, cre]
+Maintainer: Gokmen Zararsiz <gokmen.zararsiz@gmail.com>
+Repository: CRAN
+Date/Publication: 2023-12-12 19:00:02 UTC

LICENSE

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+YEAR: 2023
+COPYRIGHT HOLDER: dtComb authors

MD5

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+257d91a76c92393dabfed15fd965c1ac *DESCRIPTION
+6c0ed5ad4223da2c9d76f39bd5ddd34d *LICENSE
+62697ae45e904370065381da01449f7a *NAMESPACE
+ca71064c02566607b9a44b99694613e1 *R/data.R
+9f7ffa675295fef965d8fd2bbbc2052b *R/dtComb.R
+c1844a0395e990f2c2803daed4d61e64 *R/globals.R
+69807733a19b0515a2febdc170ca3429 *R/linComb.R
+c36b8a886140f59bc9c899d205302574 *R/mathComb.R
+5864721274b6005efda7cd824dbf71db *R/mlComb.R
+97247152446a484f5b4e23004d1637b4 *R/nonlinComb.R
+2d2ade6e9296c1785811089b503b9c59 *R/plotComb.R
+976f3080dd8abc56c353b9b51405575c *R/predict.dtComb.R
+04932b34758bb2e2ae74d0b434a7751d *R/printtrain.R
+5d632bd5d27f1ad8131f21bcd5aacaa7 *R/rocsum.R
+975b1831eabfc4690e36bfb085011621 *R/standardization.R
+d86694958306984e46a0d3053bf860da *README.md
+980e7ba2a0beca1b73b97a308711ab9a *build/vignette.rds
+4abdef1bb907fcd39fbb6529ca557654 *data/allMethods.rda
+234b189196f7a3ce194cae3e9244573f *data/exampleData1.rda
+443219caefd91c87e765f58471eb21c0 *data/exampleData2.rda
+528f5bae4a16f0235d6af1bd46e6ae2d *data/exampleData3.rda
+aaa7d8a1a2814fd808a3f5775b999376 *inst/doc/vignettedtComb.R
+7234c958249fbadc3b1ea8faff5e2416 *inst/doc/vignettedtComb.Rnw
+25e5f404cc80db43cfe6b39941967a00 *inst/doc/vignettedtComb.pdf
+7dbffdd9477fea05ef77ded83592ed5f *man/allMethods.Rd
+a4342261ea48290ad70eb3f8750a7059 *man/availableMethods.Rd
+a6767928fd8457d148792c4cbffa43ba *man/dtComb.Rd
+0f01346ee89a0f65d4e91df94ebcbe59 *man/exampleData1.Rd
+30bd23a3f76f77b8f1917446473ff43a *man/exampleData2.Rd
+00e670939573d82dfc52cf8bc86cda04 *man/exampleData3.Rd
+edd0d6402ebb9116d93020ba8ec9cece *man/helper_PCL.Rd
+bb79c6465216d827bec292168a62640e *man/helper_PT.Rd
+4deb5c55b8467b2279478091338c8aa5 *man/helper_TS.Rd
+e5137597070079d496e945e1c1e8774a *man/helper_minimax.Rd
+5492d24e32bb916a1bc41f1872ed4948 *man/helper_minmax.Rd
+9137acfbe4d46e54a737d78bbcbf7258 *man/kappa.accuracy.Rd
+482c1f94e4a56c6a16e9e5efcaf34ccd *man/linComb.Rd
+3b24ad250709b995ec9de8b85179965e *man/mathComb.Rd
+d4a2bca1e8838767ffd08c785605bb1e *man/mlComb.Rd
+693c7d88f7324829dbb900f1c5f1504c *man/nonlinComb.Rd
+aacd5edb797f624ae3278861d227c47a *man/plotComb.Rd
+6dfdadf0700b9575a5eed2afb4fd6adc *man/predict.dtComb.Rd
+e31b9b6a4fff032fedbacbb1884a8781 *man/print_train.Rd
+0380148d9afd76506a9eb83f881f69c2 *man/rocsum.Rd
+6b9b4c7333043a76cc3fa0c65deb68ef *man/std.test.Rd
+a044ae282fbdda10e4f88fbcf376c526 *man/std.train.Rd
+893667e253e7767d34bf4a18ab19e466 *man/transform_math.Rd
+031482eb15265c2885cd4cdf976da76f *tests/testthat.R
+23d7f29169fdf42bff5341e51e6b2520 *tests/testthat/result_data/mayo.rda
+f2756b14d8f883518ebef75cecb6791d *tests/testthat/result_data/test_linComb.rda
+cc7d121dfb91d955d56e13dda05adc47 *tests/testthat/result_data/test_mathComb.rda
+f8edc0dcb62c3a7586fef0bfe7c852ab *tests/testthat/result_data/test_nonlinComb.rda
+6e04a05bc2372bf78cd1ba1339323780 *tests/testthat/result_data/test_predComb.rda
+c4f7ec0f24b08bd57e962f51b5311447 *tests/testthat/result_data/test_std.test.rda
+b44401cb234475d064949d078415c061 *tests/testthat/result_data/test_std.train.rda
+1b2b7e25d675e8743c91ff6d714234cd *tests/testthat/test-linComb.R
+f6743bf95787ccfef89030ea55c2ebff *tests/testthat/test-mathComb.R
+531bcde330ca32c6dcd70bf5ca231d15 *tests/testthat/test-mlComb.R
+1df8413bf5906006183bcbb5ca916b88 *tests/testthat/test-nonlinComb.R
+c584e5ef5d07a17367b40d9647922025 *tests/testthat/test-predComb.R
+fad180f9527284cafbe0ea112b037a03 *tests/testthat/test-standardize.R
+e9ad92c8ad27596efef608ab9e405471 *vignettes/dtComb.bib
+7234c958249fbadc3b1ea8faff5e2416 *vignettes/vignettedtComb.Rnw

NAMESPACE

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+# Generated by roxygen2: do not edit by hand
+
+S3method(predict,dtComb)
+export(availableMethods)
+export(helper_PCL)
+export(helper_PT)
+export(helper_TS)
+export(helper_minimax)
+export(helper_minmax)
+export(linComb)
+export(mathComb)
+export(mlComb)
+export(nonlinComb)
+export(plotComb)
+export(std.train)
+export(transform_math)
+importFrom(pROC,auc)
+importFrom(stats,'predict')

R/data.R

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+#' Examples data for the dtComb package
+#'
+#' A data set containing the results of diagnostic laparoscopy procedures for 225
+#' patients.
+#'
+#' @docType data
+#'
+#' @usage data(exampleData1)
+#'
+#' @name exampleData1
+#'
+#' @format A data frame with 225 rows and 3 variables:
+#' \describe{
+#'   \item{group}{Indicator if the procedure was needed, values needed and
+#'   not_needed}
+#'   \item{ddimer}{Biomarker 1, D-Dimer protein level in blood, ng/mL}
+#'   \item{log_leukocyte}{Biomarker 2, Logarithm of Leukocyte count in blood,
+#'   per mcL}
+#' }
+#'
+#' @examples
+#' data(exampleData1)
+#' exampleData1$group <- factor(exampleData1$group)
+#' gcol <- c("#E69F00", "#56B4E9")
+#' plot(exampleData1$ddimer, exampleData1$log_leukocyte,
+#'   col = gcol[as.numeric(exampleData1$group)]
+#' )
+#'
+"exampleData1"
+
+###############################################################################
+#'
+#' A data set containing the carriers of a rare genetic disorder for 120 samples.
+#'
+#' @docType data
+#'
+#' @usage data(exampleData2)
+#'
+#' @name exampleData2
+#'
+#' @format A data frame with 120 rows and 5 variables:
+#' \describe{
+#'   \item{Group}{Indicator if the person was carriers, values carriers and
+#'   normals}
+#'   \item{m1}{Biomarker 1, 1. measurement blood sample}
+#'   \item{m2}{Biomarker 2, 2. measurement blood sample}
+#'   \item{m3}{Biomarker 3, 3. measurement blood sample}
+#'   \item{m4}{Biomarker 4, 4. measurement blood sample}
+#' }
+#'
+#' @examples
+#' data(exampleData2)
+#' exampleData2$Group <- factor(exampleData2$Group)
+#' gcol <- c("#E69F00", "#56B4E9")
+#' plot(exampleData2$m1, exampleData2$m2,
+#'   col = gcol[as.numeric(exampleData2$Group)]
+#' )
+#'
+"exampleData2"
+
+###############################################################################
+#' A simulation data containing 250 diseased and 250 healthy individuals.
+#' @docType data
+#'
+#' @usage data(exampleData3)
+#'
+#' @name exampleData3
+#'
+#' @format A data frame with 500 rows and 3 variables:
+#' \describe{
+#'   \item{status}{Indicator of one's condition, values healthy and diseased}
+#'   \item{marker1}{1. biomarker}
+#'   \item{marker2}{2. biomarker}
+#' }
+#'
+#' @examples
+#' data(exampleData3)
+#' exampleData3$status <- factor(exampleData3$status)
+#' gcol <- c("#E69F00", "#56B4E9")
+#' plot(exampleData3$marker1, exampleData3$marker2,
+#'   col = gcol[as.numeric(exampleData3$status)]
+#' )
+#'
+"exampleData3"
+
+###############################################################################
+#' Includes machine learning models used for the mlComb function
+#' @docType data
+#'
+#' @usage data(allMethods)
+#'
+#' @name allMethods
+#'
+#' @format A data frame with 113 rows and 2 variables:
+#' \describe{
+#'   \item{Method}{Valid name for the function}
+#'   \item{Model}{Model name}
+#' }
+#'
+#' @examples
+#' data(allMethods)
+#' allMethods
+#'
+"allMethods"

R/dtComb.R

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+#' dtComb: A Comprehensive R Library for Combining Diagnostic Tests
+#'
+#' The dtComb package calculates combination scores of two biomarkers given
+#' under four main categories: linear combinations with the linComb function,
+#' non-linear combinations with the nonlinComb function,
+#' mathematical operators with the mathComb function, and machine learning
+#' algorithms with the mlComb function.
+#'
+#' @docType package
+#' @name dtComb
+NULL

R/globals.R

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+utils::globalVariables(c(
+  "allMethods", "var", "complete.cases", "glm",
+  "binomial", "var", "runif", "optim", "CombinationScore",
+  "Labels", "Threshold", "Sensitivity", "Specificity",
+  "legend", "pt", "sd"
+))