• Preface
    • Who this book is for
    • How to read this book
  • I Introduction
  • 1 License and Availability
    • 1.1 Source Code
  • 2 Requirements
  • 3 Installation
    • 3.1 IQR Tools
    • 3.2 Setup after installation
  • 4 Examples in this Book
    • 4.1 Public version IQR Tools
    • 4.2 Versioned IQR Tools
  • 5 Reproducibility of Results
    • 5.1 The CRAN Nightmare
    • 5.2 MRAN Time Machine
    • 5.3 IQR Tools Installer
  • 6 Validation
    • 6.1 Validation of IQR Tools
    • 6.2 Validation support
  • 7 Release Notes
    • 7.1 PUBLIC Version of IQR Tools
    • 7.2 V2025.05 May, 2025
    • 7.3 V2025.03 March, 2025
    • 7.4 V2024.09 September, 2024
    • 7.5 V1.17.0 December, 2023
    • 7.6 V1.16.0 July, 2023
    • 7.7 V1.15.0 June, 2023
    • 7.8 V1.14.0 April, 2023
    • 7.9 V1.12.0 November, 2022
    • 7.10 V1.11.0 October, 2022
    • 7.11 V1.10.0 May, 2022
    • 7.12 V1.9.0 February 01, 2022
    • 7.13 V1.8.0 October 16, 2021
    • 7.14 V1.7.2 September 15, 2021
    • 7.15 V1.7.1 May 4, 2021
    • 7.16 V1.7.0 April 10, 2021
    • 7.17 V1.6.0 February 18, 2021
    • 7.18 V1.5.0 October 23, 2020
    • 7.19 V1.4.0 August 21, 2020
    • 7.20 V1.3.2 July 5, 2020
    • 7.21 V1.3.1 May 20, 2020
    • 7.22 V1.3.0 May 01, 2020
    • 7.23 V1.2.1 March 08, 2020
    • 7.24 V1.2.0 March 02, 2020
    • 7.25 V1.1.1 January 31, 2020 (Brexit Day)
    • 7.26 V1.1.0 December 23, 2019
    • 7.27 V1.0.9 October 13, 2019
    • 7.28 V1.0.8 September 13, 2019
    • 7.29 V1.0.7 September 2, 2019
    • 7.30 V1.0.6 May 28, 2019
    • 7.31 V1.0.5 May 20, 2019
    • 7.32 V1.0.4 April 19, 2019
    • 7.33 V1.0.3 April 3, 2019
    • 7.34 V1.0.2 March 18, 2019
    • 7.35 V1.0.1 February 23, 2019
    • 7.36 V1.0.0 February 8, 2019
    • 7.37 V0.9.999 January 16, 2019
    • 7.38 V0.9.99 December 06, 2018
    • 7.39 V0.9.9 November 27, 2018
    • 7.40 V0.9.3 October 25, 2018
    • 7.41 V0.9.2 October 18, 2018
    • 7.42 V0.9.1 October 07, 2018
    • 7.43 V0.9.0 September 03, 2018
    • 7.44 V0.8.1 August 7, 2018
    • 7.45 V0.8.0 June 25, 2018
    • 7.46 V0.7.2 May 10, 2018
    • 7.47 V0.7.0 April 23, 2018
    • 7.48 V0.6.6 April 16, 2018
    • 7.49 V0.6.4 March 16, 2018
    • 7.50 V0.6.3 March 08, 2018
    • 7.51 V0.6.2 February 08, 2018
    • 7.52 V0.6.1 January 26, 2018
    • 7.53 V0.6.0 January 24, 2018
    • 7.54 V0.5.8 January 19, 2018
    • 7.55 V0.5.7 January 16, 2018
    • 7.56 V0.5.6 December 14, 2017
    • 7.57 V0.5.5 December 04, 2017
    • 7.58 V0.5.1 October 14, 2017
    • 7.59 V0.5.0 October 10, 2017
    • 7.60 V0.4.2 September 21, 2017
    • 7.61 V0.4.1 September 14, 2017
    • 7.62 V0.3.0 August 14, 2017
    • 7.63 V0.2.0 July 14, 2017
    • 7.64 V0.1.0 June 5, 2017
  • II Case Studies
  • 8 Analysis dataset preparation
    • 8.1 Example workflow
      • 8.1.1 Original dataset in general row-based format
      • 8.1.2 Import as IQRdataGENERAL format
      • 8.1.3 Source data exploration
      • 8.1.4 Cleaning to create an analysis dataset
      • 8.1.5 Export
    • 8.2 Workflow customization
      • 8.2.1 Dataset handling
      • 8.2.2 Import/export options
      • 8.2.3 Cleaning options
      • 8.2.4 Data exploration
  • 9 Model definition
    • 9.1 Model definition basics
      • 9.1.1 Biochemical reaction model
      • 9.1.2 One compartment linear PK
      • 9.1.3 PK/PD
    • 9.2 Dosing representation
    • 9.3 Advanced model definition
      • 9.3.1 Example
      • 9.3.2 Lagtimes
      • 9.3.3 Mathematical functions
      • 9.3.4 Implementing conditional statements (if-then-else)
      • 9.3.5 Interpolation functions
      • 9.3.6 MODEL FUNCTIONS section
      • 9.3.7 MODEL EVENTS section
    • 9.4 Handling of models in R
      • 9.4.1 Model import
      • 9.4.2 Support of SBML
      • 9.4.3 Basic model information
      • 9.4.4 Model export
    • 9.5 PK model library
    • 9.6 Example models
      • 9.6.1 PBPK
      • 9.6.2 Friberg neutropenia
      • 9.6.3 Novak-Tyson Cell-Cycle
      • 9.6.4 Parasitemia PK/PD
      • 9.6.5 Bouncing ball
      • 9.6.6 C-Functions
      • 9.6.7 Fantasy events
      • 9.6.8 Novak-Tyson biochemical
  • 10 Simulation of models
    • 10.1 Simulation
    • 10.2 Simulation settings
      • 10.2.1 Simulation time
      • 10.2.2 Initial conditions
      • 10.2.3 Parameters
      • 10.2.4 Output selection
    • 10.3 Dosing events
      • 10.3.1 Single dosing input
      • 10.3.2 Multiple dosing inputs
      • 10.3.3 Special dosing parameters
    • 10.4 Parameter sensitivities
    • 10.5 Integrator in C
  • 11 NLME Modeling
    • 11.1 Longitudinal Models
      • 11.1.1 Required data format
      • 11.1.2 Structural models
      • 11.1.3 Linear vs. nonlinear models
      • 11.1.4 Time varying covariates
      • 11.1.5 Basic PK model
      • 11.1.6 Tabular results
      • 11.1.7 General diagnostics
      • 11.1.8 Output diagnostics
      • 11.1.9 Lagtime and FOCEI
      • 11.1.10 Zero-order absorption
      • 11.1.11 NLME model settings
      • 11.1.12 Sequential PK/PD
      • 11.1.13 Regression parameters
      • 11.1.14 Error models
      • 11.1.15 Multiple outputs
      • 11.1.16 Basic covariate models
      • 11.1.17 Covariate plots
      • 11.1.18 Complex covariates
      • 11.1.19 Covariance
      • 11.1.20 BLOQ data
      • 11.1.21 IV/SC PK model
      • 11.1.22 NONMEM Bayes
      • 11.1.23 Other features
    • 11.2 Time-to-event models
      • 11.2.1 Data format
      • 11.2.2 Defining TTE NLME models
      • 11.2.3 Weibull
      • 11.2.4 Weibull with delay
      • 11.2.5 Exponential
      • 11.2.6 Exponential with delay
      • 11.2.7 Gompertz
      • 11.2.8 Gompertz with delay
      • 11.2.9 Log-logistic
      • 11.2.10 Diagnostics
    • 11.3 Joint models
      • 11.3.1 Longitudinal + TTE
      • 11.3.2 Data format
      • 11.3.3 RTTE & interval censoring
      • 11.3.4 NONMEM
  • 12 QSP Modeling
    • 12.1 Background
    • 12.2 Interface
      • 12.2.1 Data
      • 12.2.2 ModelSpec
    • 12.3 Systems Biology Example: Epo-Receptor
      • 12.3.1 Basic model simulation
      • 12.3.2 Manipulating parameters for simulations
      • 12.3.3 Defining experimental conditions
      • 12.3.4 Modeling data - exploration by manual parameter tweaking
      • 12.3.5 Modeling data - parameter estimation
      • 12.3.6 Modeling data - multistart optimization
      • 12.3.7 Modeling data - Profile Likelihood
      • 12.3.8 Modelling data - IIV and BLOQ (censored data)
  • 13 Model evaluation
    • 13.1 Goodness-of-fit
      • 13.1.1 Random effects
      • 13.1.2 Random effects / covariates
      • 13.1.3 GOF plots
      • 13.1.4 Individual plots
      • 13.1.5 Export to file
      • 13.1.6 Plot data
    • 13.2 VPC
      • 13.2.1 Generate VPC
      • 13.2.2 Prediction corrected VPC (pcVPC)
      • 13.2.3 VPC data
      • 13.2.4 VPC sequential modeling
      • 13.2.5 Additional settings
    • 13.3 Bootstrap
      • 13.3.1 Generate bootstrap
      • 13.3.2 Bootstrap results
      • 13.3.3 Stratification
      • 13.3.4 Large bootstraps
    • 13.4 Profile likelihood
  • 14 Advanced modeling workflows
    • 14.1 PopPK workflow
    • 14.2 Covariate selection
    • 14.3 Pop PK/PD workflow
  • 15 Population simulations
    • 15.1 Basic population simulation
      • 15.1.1 Basic example
      • 15.1.2 Event table generation
      • 15.1.3 Parameter sampling
      • 15.1.4 Customizing simulations
    • 15.2 Clinical trial simulation
      • 15.2.1 Parallel design example
      • 15.2.2 Adaptive design example
  • 16 Experimental design
    • 16.1 Use of PopED
      • 16.1.1 PopED / IQR Toolsinterface
      • 16.1.2 Same example in PopED
    • 16.2 Use of profile likelihood
  • 17 Exposure response analysis
    • 17.1 Logistic regression
      • 17.1.1 Single predictor
      • 17.1.2 Multiple predictors
    • 17.2 Kaplan-Meier
      • 17.2.1 Simple plot
      • 17.2.2 Stratified plot
      • 17.2.3 Style and annotation
      • 17.2.4 Risk table
      • 17.2.5 Confidence intervals
      • 17.2.6 Using the CENScol argument
    • 17.3 Cox Regression
  • 18 Reporting in Microsoft Word
    • 18.1 Example analysis report
    • 18.2 Applying styles when creating Word document
  • III Manuals
  • 19 General Dataset Format
    • 19.1 General columns
    • 19.2 Additional columns
    • 19.3 Deprecated columns
  • 20 Structural Model Syntax
    • 20.1 Model sections
      • 20.1.1 Model name
      • 20.1.2 Model notes
      • 20.1.3 Model states
      • 20.1.4 Model state information
      • 20.1.5 Model parameters
      • 20.1.6 Model variables
      • 20.1.7 Model reactions
      • 20.1.8 Model functions
      • 20.1.9 Model events
      • 20.1.10 Model C functions
    • 20.2 Pre-defined functions
    • 20.3 IQRmodel object
  • 21 Dosing definition
    • 21.1 IQRdosing object
  • 22 General Parameter Format (GPF)
    • 22.1 The GPF excel file
    • 22.2 Columns in the GPF estimates sheet
      • 22.2.1 Naming convention
      • 22.2.2 Example GPF file
    • 22.3 Parameter transformations
      • 22.3.1 Transformation between original and normal units
    • 22.4 Basic terms
  • 23 Random sampling of NLME model parameters
    • 23.1 Input
      • 23.1.1 Example GPF file
      • 23.1.2 Example patient data
    • 23.2 Calling the function sampleIndParamValues
      • 23.2.1 Output
      • 23.2.2 Example output
    • 23.3 Covariate adjustment formulae
    • 23.4 Possible values for FLAG_SAMPLE
    • 23.5 Sampling steps
      • 23.5.1 Step 1: Sampling of population parameter values
      • 23.5.2 Step 2: Sampling records from the patient data
      • 23.5.3 Step 3: Calculating typical individual parameter values
      • 23.5.4 Step 4: Sampling random effects
      • 23.5.5 Step 5: Calculating individual parameter values
    • 23.6 Testing for sampling discrepancies
      • 23.6.1 Detecting discrepancies in samples from the parameter uncertainty distribution
  • Appendix
  • A Function Reference
    • A.1 Allowed in IQRmodel
    • A.2 Auxiliary
    • A.3 Bootstrap
    • A.4 Covariate analysis
    • A.5 Data exploration
    • A.6 Data handling
    • A.7 Formatting
    • A.8 GPF
    • A.9 Help & Documentation
    • A.10 Installation
    • A.11 IQR plot
    • A.12 IQR report
    • A.13 IQR shiny
    • A.14 IQR slides
    • A.15 IQR Table
    • A.16 IQRaedataER
    • A.17 IQRdataER
    • A.18 IQRdataGeneral
    • A.19 IQRdataGENERAL
    • A.20 IQRdataGENERAL auxiliaries
    • A.21 IQReventTable
    • A.22 IQRmodel
    • A.23 IQRnlmeData
    • A.24 IQRnlmeProject
    • A.25 IQRtteProject
    • A.26 Logistic regression
    • A.27 MONOLIX
    • A.28 NLMIXR
    • A.29 Optimal Experiment Design
    • A.30 Other functions
    • A.31 Output
    • A.32 Parameter sampling
    • A.33 QSP
    • A.34 SAS
    • A.35 Sensitivity analysis
    • A.36 Sensitivity Analysis
    • A.37 Simulation
    • A.38 stat functions
    • A.39 Statistics
    • A.40 Submission package
    • A.41 Survival analysis
    • A.42 VPC
  • (c) 2018-2023 IntiQuan AG

aux_rmdir function

aux_rmdir.Rd

Removes a folder

aux_rmdir(pathdir)

Arguments

pathdir

path to folder to remove

See also

Other Auxiliary: IQRloadCSVdata(), IQRsaveCSVdata(), and(), aux_explode(), aux_explodePC(), aux_fileparts(), aux_fileread(), aux_filewrite(), aux_getRelPath(), aux_mkdir(), aux_na_locf(), aux_postFillChar(), aux_preFillChar(), aux_quantilenumber(), aux_simplifypath(), aux_splitVectorEqualPieces(), aux_strFindAll(), aux_strrep(), aux_strtrim(), aux_unlevel(), aux_version(), calcAICBIC(), clusterX(), compare_IQRmodel_IQRsysModel_simulation(), fit_EmaxModel(), format_GUM(), ge(), gen_aux_version(), geocv(), geomean(), geosd(), ginv(), gt(), interp0(), interp1(), interpcs(), inv_logit(), le(), logit(), lt(), mod(), mvrnorm(), norm_M3(), or(), piecewise(), progressBar(), remove_duplicates(), run_silent_IQR(), stopIQR(), tempdirIQR(), tempfileIQR(), warningIQR()