• 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 V2026.06 June, 2026
    • 7.3 V2025.05 May, 2025
    • 7.4 V2025.03 March, 2025
    • 7.5 V2024.09 September, 2024
    • 7.6 V1.17.0 December, 2023
    • 7.7 V1.16.0 July, 2023
    • 7.8 V1.15.0 June, 2023
    • 7.9 V1.14.0 April, 2023
    • 7.10 V1.12.0 November, 2022
    • 7.11 V1.11.0 October, 2022
    • 7.12 V1.10.0 May, 2022
    • 7.13 V1.9.0 February 01, 2022
    • 7.14 V1.8.0 October 16, 2021
    • 7.15 V1.7.2 September 15, 2021
    • 7.16 V1.7.1 May 4, 2021
    • 7.17 V1.7.0 April 10, 2021
    • 7.18 V1.6.0 February 18, 2021
    • 7.19 V1.5.0 October 23, 2020
    • 7.20 V1.4.0 August 21, 2020
    • 7.21 V1.3.2 July 5, 2020
    • 7.22 V1.3.1 May 20, 2020
    • 7.23 V1.3.0 May 01, 2020
    • 7.24 V1.2.1 March 08, 2020
    • 7.25 V1.2.0 March 02, 2020
    • 7.26 V1.1.1 January 31, 2020 (Brexit Day)
    • 7.27 V1.1.0 December 23, 2019
    • 7.28 V1.0.9 October 13, 2019
    • 7.29 V1.0.8 September 13, 2019
    • 7.30 V1.0.7 September 2, 2019
    • 7.31 V1.0.6 May 28, 2019
    • 7.32 V1.0.5 May 20, 2019
    • 7.33 V1.0.4 April 19, 2019
    • 7.34 V1.0.3 April 3, 2019
    • 7.35 V1.0.2 March 18, 2019
    • 7.36 V1.0.1 February 23, 2019
    • 7.37 V1.0.0 February 8, 2019
    • 7.38 V0.9.999 January 16, 2019
    • 7.39 V0.9.99 December 06, 2018
    • 7.40 V0.9.9 November 27, 2018
    • 7.41 V0.9.3 October 25, 2018
    • 7.42 V0.9.2 October 18, 2018
    • 7.43 V0.9.1 October 07, 2018
    • 7.44 V0.9.0 September 03, 2018
    • 7.45 V0.8.1 August 7, 2018
    • 7.46 V0.8.0 June 25, 2018
    • 7.47 V0.7.2 May 10, 2018
    • 7.48 V0.7.0 April 23, 2018
    • 7.49 V0.6.6 April 16, 2018
    • 7.50 V0.6.4 March 16, 2018
    • 7.51 V0.6.3 March 08, 2018
    • 7.52 V0.6.2 February 08, 2018
    • 7.53 V0.6.1 January 26, 2018
    • 7.54 V0.6.0 January 24, 2018
    • 7.55 V0.5.8 January 19, 2018
    • 7.56 V0.5.7 January 16, 2018
    • 7.57 V0.5.6 December 14, 2017
    • 7.58 V0.5.5 December 04, 2017
    • 7.59 V0.5.1 October 14, 2017
    • 7.60 V0.5.0 October 10, 2017
    • 7.61 V0.4.2 September 21, 2017
    • 7.62 V0.4.1 September 14, 2017
    • 7.63 V0.3.0 August 14, 2017
    • 7.64 V0.2.0 July 14, 2017
    • 7.65 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
      • 14.1.1 Original dataset
      • 14.1.2 Base Model
      • 14.1.3 Error model
      • 14.1.4 Covariate model building
      • 14.1.5 Covariance model
      • 14.1.6 Generation of VPCs for final model
    • 14.2 Reporting
    • 14.3 Covariate selection
    • 14.4 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
  • (c) 2018-2023 IntiQuan AG

Or function

or.Rd

Mainly present to allow the use of it in IQRmodels.

or(...)

Arguments

...

Comma separated numeric values

Value

Or of all values

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_rmdir(), 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(), piecewise(), progressBar(), remove_duplicates(), run_silent_IQR(), stopIQR(), tempdirIQR(), tempfileIQR(), warningIQR()

Other Allowed in IQRmodel: IQRmodel(), and(), ge(), gt(), interp0(), interp1(), interpcs(), le(), lt(), mod(), piecewise()