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Kyun-Seop Baecran-robot
Kyun-Seop Bae
authored and
cran-robot
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version 0.5.1
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‎DESCRIPTION

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Package: wnl
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Version: 0.5.0
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Version: 0.5.1
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Title: Minimization Tool for Pharmacokinetic-Pharmacodynamic Data
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Analysis
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Description: This is a set of minimization tools (maximum likelihood estimation and least square fitting) to solve examples in the Johan Gabrielsson and Dan Weiner's book "Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications" 5th ed. (ISBN:9198299107). Examples include linear and nonlinear compartmental model, turn-over model, single or multiple dosing bolus/infusion/oral models, allometry, toxicokinetics, reversible metabolism, in-vitro/in-vivo extrapolation, enterohepatic circulation, metabolite modeling, Emax model, inhibitory model, tolerance model, oscillating response model, enantiomer interaction model, effect compartment model, drug-drug interaction model, receptor occupancy model, and rebound phenomena model.
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LazyLoad: yes
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Repository: CRAN
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URL: https://cran.r-project.org/package=wnl
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Packaged: 2019-05-09 23:00:13 UTC; K
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Date/Publication: 2019-05-10 05:50:03 UTC
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Packaged: 2019-06-10 15:27:53 UTC; K
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Date/Publication: 2019-06-10 16:00:03 UTC

‎MD5

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29dc5fe708b6f8d142b570c81663d173 *DESCRIPTION
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cbb420ff79f01b61f37dd4b825f016f8 *DESCRIPTION
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6e182aead6f74b388217bc3a0324aed1 *NAMESPACE
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117a37f41d92aaa0ca02f28370f0b157 *R/BasicUtil.R
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19b351cbaa60346e05cfd856ca40af78 *R/Comp1.R
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8a303e9889ab5f2e2fbc6c0eea75165b *R/ExpandDH.R
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27eba374983f3dd4c5a24228f43b3a19 *R/ExpandDH.R
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0295aa648727a7c235c793349775d9e4 *R/Objs.R
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92df4dda26d15a5df93bf94940863c95 *R/Secondary.R
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ed74bd57f6f5412bbd8cd8998d0e58c5 *R/SolComp2.R
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7791bb80ef7ff7935d783d3550d08bb7 *R/SolComp3.R
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b801ef09ac2e1d9c931f1d08fe4a6b90 *R/cmpChi.R
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8451602da7499fe946b9651a018e50bd *R/dx.R
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36b8832dd393615d7dac0e467ba12ec2 *R/nComp.R
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542e2e6b89b83d764eeb659c1dc73407 *R/nComp.R
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1be35636747a8e1af4534c8ae357a046 *R/nlr.R
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f422ba48f6c90476e0de760d6c972d50 *R/sysdata.rda
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93992a72ede869c9d1980d0c4f2151ac *R/sysdata.rda
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43d78a974589c393897dca7131944451 *R/wnl5.R
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5e7046e2aae1e60bcf99c7bd8072600c *inst/NEWS.Rd
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5b5ba0d4cee87aa87853c541b6c84968 *inst/doc/wnl-manual.pdf
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66a56f88c150d82e82ae4ea858169065 *inst/NEWS.Rd
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75463c4d3bd42432719b597f93dd859d *inst/doc/Compartment-vignette.pdf
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0b43b932b5fbb544fb57f6f78d0a7f0a *inst/doc/wnl-manual.pdf
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9e17aba9a349e47eed1a4658ae8c1d43 *man/BasicUtil.Rd
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f9ab397c3e89d4acba0f7c5f06e91923 *man/Comp1.Rd
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7140e061fba988759ef156f0a830af7f *man/DAT.Rd
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fcf1c25de6e2e11ee4e65aba449ecfa8 *man/ExpandDH.Rd
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cc44d9f1d6828706027b073be2a8250c *man/DAT.Rd
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1d89dcf882509be8a127789a9d87c4c3 *man/ExpandDH.Rd
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4c8010f47e5cd1f1c27e78b5b0337e06 *man/Objs.Rd
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473916c208b3d722e54944b5f2a4a18f *man/Secondary.Rd
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c721ee252fb7c6f42e4ec4fc413546ff *man/SolComp2.Rd
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ed43eff40571eec929d48e06e3dc97a9 *man/SolComp3.Rd
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43624bff3686aa856271bc124fcb20fd *man/SolComp2.Rd
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d704d41d4b9bb62e0586f6a0b54fdd58 *man/SolComp3.Rd
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fc55e5845bc733167940373376146426 *man/cmpChi.Rd
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e1e7543809d439770aa7d1cb6e897193 *man/dx.Rd
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ee0ea2d2db69f309039e2d70a791ac0a *man/e.Rd
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c5e8d10aba98a36161b28cc6e5627251 *man/nComp.Rd
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62aedc1f108c168426e5402b454b82e1 *man/nComp.Rd
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3e4f3583a2a0905de278d8ca52139616 *man/nlr.Rd
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8640ba18696633870f706cd63f6d440c *man/wnl-package.Rd
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902adeb3c4af0439299bbd830aa6ed04 *man/wnl5.Rd

‎R/ExpandDH.R

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ExpandDH = function(DH, Fo = 1)
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{
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DH[is.na(DH)] = 0
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DH[DH$AMT > 0 & DH$CMT == 1, "AMT"] = Fo * DH[DH$AMT > 0 & DH$CMT==1, "AMT"]
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DH[DH$AMT > 0 & DH$CMT == 1, "AMT"] = Fo*DH[DH$AMT > 0 & DH$CMT == 1, "AMT"]
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DH[,"BOLUS"] = 0
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DH[DH$AMT > 0 & DH$RATE == 0, "BOLUS"] = DH[DH$AMT > 0 & DH$RATE == 0, "AMT"]
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DH[,"RATE2"] = 0
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BlankRow = DH[1,]
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BlankRow[1,] = rep(0, ncol(DH))
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RateDat = DH[DH$RATE > 0,]
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nRate = nrow(RateDat)
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for (i in 1:nRate) {
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cRATE = RateDat[i, "RATE"]
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cCMT = RateDat[i, "CMT"]
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cTIME = RateDat[i, "TIME"]
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cDUR = RateDat[i, "AMT"] / RateDat[i,"RATE"]
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cEND = cTIME + cDUR
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if (!(cEND %in% DH[,"TIME"])) {
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BlankRow[1, "TIME"] = cEND
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DH = rbind(DH, BlankRow)
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DH = DH[order(DH$TIME),]
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}
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DH[DH$TIME >= cTIME & DH$TIME < (cTIME + cDUR), "RATE2"] = DH[DH$TIME >= cTIME & DH$TIME < (cTIME + cDUR), "RATE2"] + cRATE
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DH[DH$TIME >= cTIME & DH$TIME < (cTIME + cDUR), "CMT"] = cCMT
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}

‎R/nComp.R

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nComp = function(Sol, Ka, DH)
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nComp = function(Sol, Ka=0, DH)
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{
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L = Sol$L
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Co = Sol$Co

‎R/sysdata.rda

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‎inst/NEWS.Rd

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\name{NEWS}
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\title{News for Package \pkg{wnl}}
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\section{Version 0.5.1 (2019-06-09)}{
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\itemize{
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\item{DAT table changed.}
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}
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}
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\section{Version 0.5.0 (2019-05-09)}{
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\itemize{
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\item{Analytical solutions of compartmental models added.}

‎inst/doc/Compartment-vignette.pdf

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‎inst/doc/wnl-manual.pdf

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‎man/DAT.Rd

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}
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\usage{RptCfg}
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\format{
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This data frame has 5 columns with 19 time-points for the simulation.
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This data frame has 5 columns with 18 time-points for the simulation.
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\describe{
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\item{\code{TIME}}{Time}
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\item{\code{AMT}}{Amount given for the compartment of \code{CMT} column}

‎man/ExpandDH.Rd

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Returns expanded dosing history table.
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}
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\author{Kyun-Seop Bae <k@acr.kr>}
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\examples{
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DAT
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ExpandDH(DAT) # One observation point is increased at the time of 27.
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}
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\keyword{Dosing history}

‎man/SolComp2.Rd

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\name{SolComp2}
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\alias{SolComp2}
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\title{Analytical Solution for Two-compartment Model}
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\title{Get Lambdas and Coefficients of Two-compartment Model}
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\description{
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It calculates lambdas and coefficients for two-compartment model from K10, K12, and K21.
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}

‎man/SolComp3.Rd

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\name{SolComp3}
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\alias{SolComp3}
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\title{Analytical Solution for Three-compartment Model}
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\title{Get Lambdas and Coefficients of Three-compartment Model}
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\description{
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It calculates lambdas and coefficients for three-compartment model from K10, K12, K21, K13, and K31.
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}

‎man/nComp.Rd

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\name{nComp}
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\alias{nComp}
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\title{Multi-compartment model - analytical}
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\title{Get Amounts of Each Compartments using Lambdas and Coefficients of Multi-compartment Model}
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\description{
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It calculates using multi-compartment model.
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}
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\usage{
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nComp(Sol, Ka, DH)
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nComp(Sol, Ka=0, DH)
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}
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\arguments{
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\item{Sol}{Solution list of lambdas and coefficients}
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}
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\details{
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First compartment is the gut compartment for oral dosing. IV bolus and infusion dosing should be done at the second compartment.
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If a bolus dose was given at time T, it is reflected at times of larger than T. This is more close to real observation. ADAPT does like this, but NONMEM does not.
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}
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\value{
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This returns a table with the gut and the other compartment columns

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