### Example: Launching and Cleaning Up Shiny App

Source: https://nlmixr2.github.io/rxode2/reference/genShinyApp.template.html

Demonstrates how to launch the generated Shiny application and clean up the created directory afterwards. Use `runApp(appDir)` to start the application.

```R
# \donttest{
# remove myapp when the example is complete
on.exit(unlink("myapp", recursive = TRUE, force = TRUE))
```

--------------------------------

### Integrate rxnbinomMu into rxode2 model

Source: https://nlmixr2.github.io/rxode2/reference/rxnbinom.html

This example shows how to incorporate `rxnbinomMu` with its mean parameterization into an `rxode2` model. Proper event table and simulation setup are required.

```R
rx <- function() {
  model({
    a <- rxnbinomMu(10, 40)
  })
}


s <- rxSolve(rx, et)
#>  
#>  
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#>  
#>  
```

--------------------------------

### Install rxode2 Development Version from GitHub

Source: https://nlmixr2.github.io/rxode2-manual/install.html

Install development versions of rxode2 components directly from GitHub. Ensure you have the 'devtools' package installed.

```r
devtools::install_github("nlmixr2/rxode2parse")
devtools::install_github("nlmixr2/rxode2random")
devtools::install_github("nlmixr2/rxode2et")
devtools::install_github("nlmixr2/rxode2ll")
devtools::install_github("nlmixr2/rxode2")
```

--------------------------------

### Integrate rxnbinom into rxode2 model

Source: https://nlmixr2.github.io/rxode2/reference/rxnbinom.html

This example demonstrates how to use `rxnbinom` directly within an `rxode2` model definition. Ensure proper setup of event tables and simulation parameters.

```R
rx <- function() {
  model({
    a <- rxnbinom(10, 0.5)
  })
}

et <- et(1, id = 1:100)


s <- rxSolve(rx, et)
#>  
#>  
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#>  
#>  
```

--------------------------------

### Example: Simulate Progress Bar Usage

Source: https://nlmixr2.github.io/rxode2/reference/rxProgress.html

This example demonstrates the typical usage pattern of rxProgress functions within a loop. It initializes the progress bar, simulates work with Sys.sleep, and ensures cleanup with on.exit.

```R
f <- function() {
  on.exit({
    rxProgressAbort()
  })
  rxProgress(100)
  for (i in 1:100) {
    rxTick()
    Sys.sleep(1 / 100)
  }
  rxProgressStop()
}
# \donttest{
f()
# }
```

--------------------------------

### Integrate rxbinom within rxode2 Model

Source: https://nlmixr2.github.io/rxode2/reference/rxbinom.html

This example demonstrates how to use rxbinom directly within an rxode2 model definition. Ensure proper setup of the model and event table for simulation.

```R
rx <- function() {
  model({
    a <- rxbinom(1, 0.5)
  })
}

et <- et(1, id = 1:2)

s <- rxSolve(rx, et)
#>  
#>  
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#>  
#>  
```

--------------------------------

### Example: Simulate from Multivariate Normal

Source: https://nlmixr2.github.io/rxode2/reference/rxRmvn.html

This example demonstrates how to use the rxRmvn function to simulate random vectors from a multivariate normal distribution. It sets up a dimension and a mean vector, similar to examples from the mvnfast package.

```R
## From mvnfast
## Unlike mvnfast, uses threefry simulation

d <- 5
mu <- 1:d
```

--------------------------------

### Install rxode2 Development Version

Source: https://nlmixr2.github.io/rxode2/index.html

Installs the development versions of rxode2 and rxode2ll from GitHub. Ensure your C++ toolchain supports C++14.

```R
# install.packages("devtools")
devtools::install_github("nlmixr2/rxode2ll")
devtools::install_github("nlmixr2/rxode2")
```

--------------------------------

### Example Usage with rxode2

Source: https://nlmixr2.github.io/rxode2/reference/dGELU.html

Demonstrates how to use the GELU derivative functions within an rxode2 model.

```APIDOC
## Examples
```R
# you can use rxode2 as well
r <- rxode2({
   r1 <- dGELU(time)
   r2 <- d2GELU(time)
   r3 <- d3GELU(time)
   r4 <- d4GELU(time)
})

et <- et(c(-2, -1, 0, 1, 2))
rxSolve(r, et)
```
```

--------------------------------

### rxode2 Event Table Example

Source: https://nlmixr2.github.io/rxode2/reference/et.html

Demonstrates the creation of an event table using rxode2, including model definition and library loading.

```r
if (FALSE) { # \dontrun{

library(rxode2)
library(units)

# Model from rxode2 tutorial

```

--------------------------------

### Example Mixture Model Simulation and Plotting

Source: https://nlmixr2.github.io/rxode2/reference/mix.html

This example demonstrates a mixture model with two clearance populations. It includes the model definition, simulation using `rxSolve`, and plotting of the simulated results using `plot` and `ipredSim`. Ensure the model source is intact for comment parsing.

```R
one.cmt <- function() {
  ini({
    tka <- 0.45 # Log Ka
    tcl1 <- log(c(0, 2.7, 100)) # Log Cl
    tcl2 <- log(c(0, 0.1, 120)) # Log Cl
    tv <- 3.45; label("log V")
    p1 <- 0.3
    eta.ka ~ 0.6
    eta.cl ~ 0.3
    eta.v ~ 0.1
    add.sd <- 0.7
  })
  model({
    ka <- exp(tka + eta.ka)
    # This is the example mixture model
    cl <- mix(exp(tcl1 + eta.cl), p1, exp(tcl2 + eta.cl))
    v <- exp(tv + eta.v)
    me <- mixest # This is the assigned mixture estimate
    mn <- mixnum # This is the number of mixture estimate in the model
    # This is the uniform mixture estimate used in simualtion to
    # determine the population
    mu <- mixunif
    linCmt() ~ add(add.sd)
  })
}
```

```R
s <- rxSolve(one.cmt, et(amt=320, ii=12, addl=2, cmt=1) |>
                      et(seq(0, 72)) |>
                      et(id=1:20))
#>  
#>  
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#>  
#>  

plot(s, ipredSim)
```

--------------------------------

### Install rxode2 from CRAN

Source: https://nlmixr2.github.io/rxode2-manual/install.html

Use this command to install the stable release of rxode2 from CRAN.

```r
install.packages("rxode2")
```

--------------------------------

### Install rxode2 Development Version from GitHub (R 4.0+)

Source: https://nlmixr2.github.io/rxode2-manual/install.html

Installs the development version of rxode2 and its components from GitHub. This assumes your C++ toolchain is set up to support C++14, which is often required for packages using StanHeaders.

```r
# install.packages("devtools")
devtools::install_github("nlmixr2/rxode2parse")
devtools::install_github("nlmixr2/rxode2random")
devtools::install_github("nlmixr2/rxode2et")
devtools::install_github("nlmixr2/rxode2ll")
devtools::install_github("nlmixr2/rxode2")
```

--------------------------------

### rxode2 Model Syntax Example

Source: https://nlmixr2.github.io/rxode2-manual/syntax.html

An example of rxode2 model syntax, defining parameters, calculating physiological variables, and setting up differential equations for compartments. This syntax is used to build complex pharmacokinetic models.

```rxode2
rxode2({
    param(lKbBR, lKbMU, lKbAD, lCLint, eta.LClint, lKbBO, lKbRB, 
        WT)
    KbBR = exp(lKbBR)
    KbMU = exp(lKbMU)
    KbAD = exp(lKbAD)
    CLint = exp(lCLint + eta.LClint)
    KbBO = exp(lKbBO)
    KbRB = exp(lKbRB)
    CO = (187 * WT^0.81) * 60/1000
    QHT = 4 * CO/100
    QBR = 12 * CO/100
    QMU = 17 * CO/100
    QAD = 5 * CO/100
    QSK = 5 * CO/100
    QSP = 3 * CO/100
    QPA = 1 * CO/100
    QLI = 25.5 * CO/100
    QST = 1 * CO/100
    QGU = 14 * CO/100
    QHA = QLI - (QSP + QPA + QST + QGU)
    QBO = 5 * CO/100
    QKI = 19 * CO/100
    QRB = CO - (QHT + QBR + QMU + QAD + QSK + QLI + QBO + QKI)
    QLU = QHT + QBR + QMU + QAD + QSK + QLI + QBO + QKI + QRB
    VLU = (0.76 * WT/100)/1.051
    VHT = (0.47 * WT/100)/1.03
    VBR = (2 * WT/100)/1.036
    VMU = (40 * WT/100)/1.041
    VAD = (21.42 * WT/100)/0.916
    VSK = (3.71 * WT/100)/1.116
    VSP = (0.26 * WT/100)/1.054
    VPA = (0.14 * WT/100)/1.045
    VLI = (2.57 * WT/100)/1.04
    VST = (0.21 * WT/100)/1.05
})
```

--------------------------------

### rxSolveSetup

Source: https://nlmixr2.github.io/rxode2/reference/rxSolveSetup.html

Checks if the memory is installed for a solve. Returns a boolean indicating if the memory is currently free for rxode2.

```APIDOC
## rxSolveSetup

### Description
Checks if the memory is installed for a solve.

### Usage
```
rxSolveSetup()
```

### Value
boolean saying if the memnory is currently free for rxode2
```

--------------------------------

### Example Usage of rxUdfUiIniLhs

Source: https://nlmixr2.github.io/rxode2/reference/rxUdfUiIniLhs.html

Demonstrates the output of rxUdfUiIniLhs(). It returns NULL, indicating an empty or uninitialized state, ready for further definition.

```R
rxUdfUiIniLhs()
#> NULL
```

--------------------------------

### Example Usage of Package Loading Functions

Source: https://nlmixr2.github.io/rxode2/reference/rxode2parseAssignPackagesToLoad.html

Demonstrates how to use rxode2parseGetPackagesToLoad() to see the current packages and then rxode2parseAssignPackagesToLoad() to set them (in this case, setting them to the current list).

```r
rxode2parseGetPackagesToLoad()
#> [1] "rxode2ll" "lotri"   

rxode2parseAssignPackagesToLoad(rxode2parseGetPackagesToLoad())
#> [1] "rxode2ll" "lotri"   
```

--------------------------------

### Customize Automatic Variable Detection

Source: https://nlmixr2.github.io/rxode2/articles/Modifying-Models.html

Customize the automatic variable detection in rxode2 models using rxSetPipingAuto(). This example sets the pattern to only include variables starting with 'te'.

```R
rxSetPipingAuto(thetamodelVars = rex::rex("te"))

mod9 <- mod |> 
  model({
    emax <- exp(temax)
    e0 <- exp(te0 + eta.e0)
    ec50 <- exp(tec50)
    kin <- exp(tkin)
    kout <- exp(tkout)
  }, append=FALSE)
#> ℹ promote `temax` to population parameter with initial estimate 1
#> ℹ promote `te0` to population parameter with initial estimate 1
```

--------------------------------

### rxode2 Example: Bid and QD Dosing Regimens

Source: https://nlmixr2.github.io/rxode2/reference/add.dosing.html

Demonstrates creating bidirectional (bid) and once-daily (qd) dosing regimens for 5 days using rxode2's eventTable functions.

```R
library(rxode2)
library(units)

mod1 <-function(){
  ini({
    KA <- 2.94E-01
    CL <- 1.86E+01
    V2 <- 4.02E+01
    Q <- 1.05E+01
    V3 <- 2.97E+02
    Kin <- 1
    Kout <- 1
    EC50 <- 200
  })
 model({
    C2 <- centr/V2
    C3 <- peri/V3
    d/dt(depot) <- -KA*depot
    d/dt(centr) <- KA*depot - CL*C2 - Q*C2 + Q*C3
    d/dt(peri)  <-                    Q*C2 - Q*C3
    d/dt(eff)   <- Kin - Kout*(1-C2/(EC50+C2))*eff
 })
}

## These are making the more complex regimens of the rxode2 tutorial

## bid for 5 days
bid <- et(timeUnits="hr") |> 
       et(amt=10000,ii=12,until=set_units(5, "days"))

## qd for 5 days
qd <- et(timeUnits="hr") |> 
      et(amt=20000,ii=24,until=set_units(5, "days"))

## bid for 5 days followed by qd for 5 days

et <- seq(bid,qd) |> 
      et(seq(0,11*24,length.out=100))

bidQd <- rxSolve(mod1, et)

plot(bidQd, C2)
```

--------------------------------

### rxode2 Model Setup and Solving

Source: https://nlmixr2.github.io/rxode2/articles/rxode2-syntax.html

Demonstrates how to define and use an rxode2 model in R. It shows model compilation, parameter definition, initial conditions, and the `solve()` function call with an event table.

```R
m1 <- rxode2(model = ode, modName = "m1")

# model parameters -- a named vector is required
theta <-
   c(KA=0.29, CL=18.6, V2=40.2, Q=10.5, V3=297, Kin=1, Kout=1, EC50=200)

# state variables and their amounts at time 0 (the use of names is
# encouraged, but not required)
inits <- c(depot=0, centr=0, peri=0, eff=1)

# qd1 is an eventTable specification with a set of dosing and sampling
# records (code not shown here)

solve(theta, event = qd1, inits = inits)
```

--------------------------------

### rxode2 Example: Infusion and Oral Dosing Regimens

Source: https://nlmixr2.github.io/rxode2/reference/add.dosing.html

Demonstrates creating infusion and oral (qd) dosing regimens for 5 days each, using rxode2's eventTable functions and specifying compartments by name.

```R
## Now Infusion for 5 days followed by oral for 5 days

##  note you can dose to a named compartment instead of using the compartment number
infusion <- et(timeUnits = "hr") |> 
      et(amt=10000, rate=5000, ii=24, until=set_units(5, "days"), cmt="centr")


qd <- et(timeUnits = "hr") |> 
  et(amt=10000, ii=24, until=set_units(5, "days"), cmt="depot")

et <- seq(infusion,qd)

infusionQd <- rxSolve(mod1, et)

plot(infusionQd, C2)
```

--------------------------------

### Initialize User-Defined Function LHS

Source: https://nlmixr2.github.io/rxode2/reference/rxUdfUiIniLhs.html

Call rxUdfUiIniLhs() to get the initial language expression for a user-defined function's LHS. This is typically used as a starting point for defining custom functions in rxode2.

```R
rxUdfUiIniLhs()
```

--------------------------------

### Example Usage

Source: https://nlmixr2.github.io/rxode2/reference/rxProgress.html

Demonstrates how to use `rxProgress`, `rxTick`, and `rxProgressStop` to show progress during a simulated task. `rxProgressAbort` is used with `on.exit` to ensure the progress bar is handled even if errors occur.

```APIDOC
## Examples

```R
f <- function() {
  on.exit({
    rxProgressAbort()
  })
  rxProgress(100)
  for (i in 1:100) {
    rxTick()
    Sys.sleep(1 / 100)
  }
  rxProgressStop()
}
# \donttest{
f()
# }
```
```

--------------------------------

### Run rxode2 interactive tutorials

Source: https://nlmixr2.github.io/rxode2/articles/rxode2-tutorials.html

Launches interactive learnr tutorials for rxode2 syntax and event tables. These tutorials require the learnr package and are designed to be run from an R session.

```r
learnr::run_tutorial("rxode2-00-syntax", "rxode2")
```

```r
learnr::run_tutorial("rxode2-00-events", "rxode2")
```

--------------------------------

### Bolus Infusion Examples in RxODE

Source: https://nlmixr2.github.io/rxode2/articles/rxode2-events-classic.html

These examples show bolus infusions to compartment 1. The first example models bioavailability changes affecting infusion duration, while the second models bioavailability changes affecting the infusion rate.

```rxode2
0 | 10101 | 50  
---|---|---
0.5 | 0 | 0  
1 | 0 | 0  
1.5 | 10101 | -50  
```

```rxode2
0 | 20101 | 50  
---|---|---
0.5 | 0 | 0  
1 | 0 | 0  
1.5 | 20101 | -50  
```

--------------------------------

### Get List of Supported Functions

Source: https://nlmixr2.github.io/rxode2/reference/rxSupportedFuns.html

Call `rxSupportedFuns()` to get a list of supported functions in rxode2. This function requires no arguments.

```r
rxSupportedFuns()
```

--------------------------------

### Initialize a New Package for rxode2 Models

Source: https://nlmixr2.github.io/rxode2/articles/rxode2-rxUse.html

Set up a new R package to include rxode2 models. This involves creating the package structure, setting licenses, and declaring dependencies on `rxode2`.

```R
library(rxode2)
library(usethis)
pkgPath  <- file.path(rxTempDir(),"MyRxModel")
create_package(pkgPath);
use_gpl3_license("Matt")
use_package("rxode2", "LinkingTo")
use_package("rxode2", "Depends") ##  library(rxode2) on load; Can use imports instead.
use_roxygen_md()
##use_readme_md()
```

--------------------------------

### Complex dosing regimen simulation

Source: https://nlmixr2.github.io/rxode2/reference/etRep.html

This example demonstrates creating a complex dosing regimen by combining bid and qd schedules, then simulating it using rxSolve. It requires the rxode2 and units packages.

```R
library(rxode2)
library(units)

mod1 <-function(){
  ini({
    KA <- 2.94E-01
    CL <- 1.86E+01
    V2 <- 4.02E+01
    Q <- 1.05E+01
    V3 <- 2.97E+02
    Kin <- 1
    Kout <- 1
    EC50 <- 200
  })
 model({
    C2 <- centr/V2
    C3 <- peri/V3
    d/dt(depot) <- -KA*depot
    d/dt(centr) <- KA*depot - CL*C2 - Q*C2 + Q*C3
    d/dt(peri)  <-                    Q*C2 - Q*C3
    d/dt(eff)   <- Kin - Kout*(1-C2/(EC50+C2))*eff
 })
}

## These are making the more complex regimens of the rxode2 tutorial

## bid for 5 days
bid <- et(timeUnits="hr") |>
       et(amt=10000,ii=12,until=set_units(5, "days"))

## qd for 5 days
qd <- et(timeUnits="hr") |>
      et(amt=20000,ii=24,until=set_units(5, "days"))

## bid for 5 days followed by qd for 5 days

et <- seq(bid,qd) |>
      et(seq(0,11*24,length.out=100))

bidQd <- rxSolve(mod1, et)

plot(bidQd, C2)
```

--------------------------------

### rxode2 EventTable Creation

Source: https://nlmixr2.github.io/rxode2/reference/rxode2.html

Demonstrates the creation of an EventTable for rxode2 simulations. It sets up QD dosing for 5 days and defines sampling times.

```R
# Step 2 - Create the model input as an EventTable,
# including dosing and observation (sampling) events

# QD (once daily) dosing for 5 days.

qd <- et(amountUnits = "ug", timeUnits = "hours") |>
  et(amt = 10000, addl = 4, ii = 24)

# Sample the system hourly during the first day, every 8 hours
# then after
qd <- qd |> et(0:24) |>
  et(from = 24 + 8, to = 5 * 24, by = 8)
```

--------------------------------

### Install Rtools on Windows

Source: https://nlmixr2.github.io/rxode2-manual/install.html

Installs Rtools on Windows using the 'installr' package. This is necessary for compiling R packages that require a C/C++ toolchain.

```r
install.packages("installr")
library(installr)
install.rtools()
```

--------------------------------

### Install symengine dependencies on macOS

Source: https://nlmixr2.github.io/rxode2-manual/install.html

Installs necessary dependencies (cmake, gmp, mpfr, libmpc) for compiling the 'symengine' package on macOS using Homebrew.

```bash
brew install cmake gmp mpfr libmpc
```

--------------------------------

### rxode2 String Handling Example 1

Source: https://nlmixr2.github.io/rxode2/reference/rxode2.html

Demonstrates how strings are handled in rxode2 models. This example shows a conditional assignment to a string variable 'tAPGAR'.

```R
if (APGAR == 10 || APGAR == 8 || APGAR == 9) {
    tAPGAR <- "High"
  } else if (APGAR == 1 || APGAR == 2 || APGAR == 3) {
    tAPGAR <- "Low"
  } else if (APGAR == 4 || APGAR == 5 || APGAR == 6 || APGAR == 7) {
    tAPGAR <- "Med"
  } else {
    tAPGAR<- "Med"
  }
```

--------------------------------

### rxode2 Model Specification Example

Source: https://nlmixr2.github.io/rxode2-manual/syntax.html

This is a basic example of an rxode2 model specification, including conditional logic for bioavailability and differential equations for compartments and effect.

```rxode2
   # An rxode2 model specification (this line is a comment).

   if(comed==0){   # concomitant medication (con-med)?
      F = 1.0;     # full bioavailability w.o. con-med
   }
   else {
      F = 0.80;    # 20% reduced bioavailability
   }

   C2 = centr/V2;  # concentration in the central compartment
   C3 = peri/V3;   # concentration in the peripheral compartment

   # ODE describing the PK and PD

   d/dt(depot) = -KA*depot;
   d/dt(centr) = F*KA*depot - CL*C2 - Q*C2 + Q*C3;
   d/dt(peri)  =                      Q*C2 - Q*C3;
   d/dt(eff)   = Kin - Kout*(1-C2/(EC50+C2))*eff;
```

--------------------------------

### Simulate with Dosing Windows

Source: https://nlmixr2.github.io/rxode2/articles/rxode2-event-table.html

Simulate a model using an event table with defined dosing windows. This example adds sampling times to the previously defined event table and then solves and plots the simulation.

```R
ev <- ev |> et(seq(0,48,length.out=200))

solve(m1, ev,
      params=data.frame(KA=0.294*exp(rnorm(4)),
                        CL=18.6*exp(rnorm(4)))) |>
  plot(C2)
```

--------------------------------

### RxODE2 Simulation with lapply

Source: https://nlmixr2.github.io/rxode2/articles/rxode2-speed.html

This example demonstrates a real-life scenario of running rxode2 simulations using `lapply` for parallel processing. It includes defining an ODE model, setting up population parameters with inter-individual variability, and generating simulation data.

```R
library(rxode2)
library(data.table)
#Define the rxode2 model
  ode1 <- "
  d/dt(abs)    = -KA*abs;
  d/dt(centr)  =  KA*abs-(CL/V)*centr;
  C2=centr/V;
  "

#Create the rxode2 simulation object
mod1 <- rxode2(model = ode1)
```

```R
#Population parameter values on log-scale
  paramsl <- c(CL = log(4),
               V = log(70),
               KA = log(1))
#make 10,000 subjects to sample from:
  nsubg <- 300 # subjects per dose
  doses <- c(10, 30, 60, 120)
  nsub <- nsubg * length(doses)
#IIV of 30% for each parameter
  omega <- diag(c(0.09, 0.09, 0.09))# IIV covariance matrix
  sigma <- 0.2
#Sample from the multivariate normal
  set.seed(98176247)
  rxSetSeed(98176247)
  library(MASS)
  mv <-
    mvrnorm(nsub, rep(0, dim(omega)[1]), omega) # Sample from covariance matrix
#Combine population parameters with IIV
  params.all <-
    data.table(
      "ID" = seq(1:nsub),
      "CL" = exp(paramsl['CL'] + mv[, 1]),
      "V" = exp(paramsl['V'] + mv[, 2]),
      "KA" = exp(paramsl['KA'] + mv[, 3])
    )
#set the doses (looping through the 4 doses)
params.all[, AMT := rep(100 * doses,nsubg)]
```

```R
Startlapply <- Sys.time()

#Run the simulations using lapply for speed
  s = lapply(1:nsub, function(i) {
#selects the parameters associated with the subject to be simulated
    params <- params.all[i]
#creates an eventTable with 7 doses every 24 hours
    ev <- eventTable()
    ev$add.dosing(
      dose = params$AMT,
      nbr.doses = 1,
      dosing.to = 1,
      rate = NULL,
      start.time = 0
    )
#generates 4 random samples in a 24 hour period
    ev$add.sampling(c(0, sort(round(sample(runif(600, 0, 1440), 4) / 60, 2))))
#runs the rxode2 simulation
    x <- as.data.table(mod1$run(params, ev))
#merges the parameters and ID number to the simulation output
    x[, names(params) := params]
  })
```

```R
#runs the entire sequence of 100 subjects and binds the results to the object res
  res = as.data.table(do.call("rbind", s))

Stoplapply <- Sys.time()

print(Stoplapply - Startlapply)
```

--------------------------------

### Install rxode2 Development Version via r-universe

Source: https://nlmixr2.github.io/rxode2-manual/install.html

Install development versions of rxode2 and its dependencies using the nlmixr2 r-universe repository. This method compiles binaries for macOS and Windows.

```r
install.packages(c("dparser", "rxode2ll", "rxode2parse",
                   "rxode2random", "rxode2et", "rxode2"),
                 repos=c(nlmixr2="https://nlmixr2.r-universe.dev",
                         CRAN="https://cloud.r-project.org"))
```

--------------------------------

### Standard Compilation and Documentation Workflow

Source: https://nlmixr2.github.io/rxode2/articles/rxode2-rxUse.html

The standard commands to load, document, and install an R package after making changes to rxode2 models or package code.

```R
devtools::load_all()
devtools::document()
devtools::install()
```

--------------------------------

### rxode2 String Handling Example 2

Source: https://nlmixr2.github.io/rxode2/reference/rxode2.html

Shows an alternative to the first example where a string literal 'Med' is replaced by its integer representation '3', leveraging rxode2's automatic type conversion.

```R
if (APGAR == 10 || APGAR == 8 || APGAR == 9) {
    tAPGAR <- "High"
  } else if (APGAR == 1 || APGAR == 2 || APGAR == 3) {
    tAPGAR <- "Low"
  } else if (APGAR == 4 || APGAR == 5 || APGAR == 6 || APGAR == 7) {
    tAPGAR <- "Med"
  } else {
    tAPGAR<- 3
  }
```

--------------------------------

### Steady State with Lagged Bolus Dose (Example)

Source: https://nlmixr2.github.io/rxode2-manual/events.html

An example of steady-state dosing with a lagged bolus dose, showing the use of event IDs 109 and 101 for trough calculation and dose application.

```rxode2
0 | 109 | 50 | 24  
---|---|---|---
0 | 101 | 50 |   
12 | 119 | 100 | 24  
12 | 101 | 100 |
```

--------------------------------

### mrgsolve $GLOBAL Block Example

Source: https://nlmixr2.github.io/rxode2/articles/rxode2-mrgsolve-comparison.html

Example of mrgsolve's $GLOBAL block for C++ declarations that persist across model functions. rxode2 lacks a direct equivalent at the model specification level.

```cpp
// mrgsolve only
$GLOBAL
static int call_count = 0;
```

--------------------------------

### Compile and Inspect Model

Source: https://nlmixr2.github.io/rxode2/reference/rxLastCompile.html

This example demonstrates compiling a simple rxode2 model and then using `rxLastCompile()` to inspect the compilation results. It shows the expected output format when a model compiles successfully.

```R
rxode2({
  a <- b
})
#>  
#>  
#> rxode2 5.0.2 model named rx_56011c882132e5a394aa4d3d0463b63b model (✔ ready). 
#> value$params: b
#> value$lhs: a
rxLastCompile()
#> using C compiler: ‘gcc (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0’


```

--------------------------------

### Set Omega Parameters with ~

Source: https://nlmixr2.github.io/rxode2/reference/ini.html

Demonstrates setting 'omega' parameters using the '~' operator for variance-covariance or correlation matrices.

```R
etaCL ~ 0.1
```

--------------------------------

### Compare rxStateOde with rxState

Source: https://nlmixr2.github.io/rxode2/reference/rxStateOde.html

This example demonstrates the difference between rxStateOde, which returns only ODE states, and rxState, which returns all states including compartments and sensitivities. Use rxStateOde when you need a focused view of dynamic variables.

```R
mod <- rxode2({
  Cp <- linCmt(Cl, V, Q2, V2, Q3, V3)
  ke0 <- log(2)/(50)
  d/dt(Ce) <- (Cp-Ce)*ke0
})

rxStateOde(mod)
#> [1] "Ce"

rxState(mod)
#> [1] "Ce"          "central"     "peripheral1" "peripheral2"
```

```R
mod <- rxode2({
  Cp <- linCmt(Cl, V, Q2, V2, Q3, V3, ka)
  ke0 <- log(2)/(50)
  d/dt(Ce) <- (Cp-Ce)*ke0
}, linCmtSens="linCmtB")

rxStateOde(mod)
#> [1] "Ce"

rxState(mod)
#>  [1] "Ce"                         "depot"                     
#>  [3] "central"                    "peripheral1"               
#>  [5] "peripheral2"                "rx__sens_central_BY_p1"    
#>  [7] "rx__sens_central_BY_v1"     "rx__sens_central_BY_p2"    
#>  [9] "rx__sens_central_BY_p3"     "rx__sens_central_BY_p4"    
#> [11] "rx__sens_central_BY_p5"     "rx__sens_central_BY_ka"    
#> [13] "rx__sens_peripheral1_BY_p1" "rx__sens_peripheral1_BY_v1"
#> [15] "rx__sens_peripheral1_BY_p2" "rx__sens_peripheral1_BY_p3"
#> [17] "rx__sens_peripheral1_BY_p4" "rx__sens_peripheral1_BY_p5"
#> [19] "rx__sens_peripheral1_BY_ka" "rx__sens_peripheral2_BY_p1"
#> [21] "rx__sens_peripheral2_BY_v1" "rx__sens_peripheral2_BY_p2"
#> [23] "rx__sens_peripheral2_BY_p3" "rx__sens_peripheral2_BY_p4"
#> [25] "rx__sens_peripheral2_BY_p5" "rx__sens_peripheral2_BY_ka"
#> [27] "rx__sens_depot_BY_ka"      
```

--------------------------------

### Modeled Rate and Duration Infusion in RxODE

Source: https://nlmixr2.github.io/rxode2/articles/rxode2-events-classic.html

Examples demonstrating modeled rate and duration for infusions. The first shows a modeled rate with a specific amount, and the second shows a modeled duration with a specific amount.

```rxode2
0 | 90101 | 50  
---|---|---
0 | 70101 | 50  
0.5 | 0 | 0  
1 | 0 | 0  
```

```rxode2
0 | 80101 | 50  
---|---|---
0 | 60101 | 50  
0.5 | 0 | 0  
1 | 0 | 0  
```

--------------------------------

### Example of rxUi model definition and assertions

Source: https://nlmixr2.github.io/rxode2/reference/assertRxUi.html

This example demonstrates how to define a simple one-compartment linear model using `one.cmt` and then apply `assertRxUi` and `assertRxUiSingleEndpoint` to validate its structure. It highlights the usage of these assertion functions in practice.

```R
one.cmt <- function() {
 ini({
   tka <- 0.45; label("Ka")
   tcl <- log(c(0, 2.7, 100)); label("Cl")
   tv <- 3.45; label("V")
   eta.ka ~ 0.6
   eta.cl ~ 0.3
   eta.v ~ 0.1
   add.sd <- 0.7
 })
 model({
   ka <- exp(tka + eta.ka)
   cl <- exp(tcl + eta.cl)
   v <- exp(tv + eta.v)
   linCmt() ~ add(add.sd)
 })
}

assertRxUi(one.cmt)
assertRxUiSingleEndpoint(one.cmt)

```

--------------------------------

### Setup and Control Progress Bar

Source: https://nlmixr2.github.io/rxode2/reference/rxProgress.html

Initialize a progress bar with the total number of operations. Use rxTick to advance the bar, rxProgressStop to complete it, and rxProgressAbort to handle errors or interruptions.

```R
rxProgress(num, core = 0L)

rxTick()

rxProgressStop(clear = TRUE)

rxProgressAbort(error = "Aborted calculation")
```

--------------------------------

### Steady State Dosing in RxODE

Source: https://nlmixr2.github.io/rxode2/articles/rxode2-events-classic.html

Examples for steady-state dosing. The first shows a simple steady-state dose to compartment 1. The second demonstrates steady-state dosing with superpositioning for morning and afternoon doses.

```rxode2
0 | 110 | 50 | 24  
---|---|---|---  
```

```rxode2
0 | 110 | 50 | 24  
---|---|---|---
12 | 120 | 100 | 24  
```

--------------------------------

### String to Numeric Conversion in rxode2

Source: https://nlmixr2.github.io/rxode2/articles/rxode2-syntax.html

Illustrates how strings are converted to numeric values in rxode2. The first example shows a typical R conditional structure for assigning string values. The subsequent examples demonstrate how these strings can be represented by their corresponding numeric indices, especially after pre-declaring levels.

```R
if (APGAR == 10 || APGAR == 8 || APGAR == 9) {
    tAPGAR <- "High"
  } else if (APGAR == 1 || APGAR == 2 || APGAR == 3) {
    tAPGAR <- "Low"
  } else if (APGAR == 4 || APGAR == 5 || APGAR == 6 || APGAR == 7) {
    tAPGAR <- "Med"
  } else {
    tAPGAR<- "Med"
  }
```

```R
if (APGAR == 10 || APGAR == 8 || APGAR == 9) {
    tAPGAR <- "High"
  } else if (APGAR == 1 || APGAR == 2 || APGAR == 3) {
    tAPGAR <- "Low"
  } else if (APGAR == 4 || APGAR == 5 || APGAR == 6 || APGAR == 7) {
    tAPGAR <- "Med"
  } else {
    tAPGAR<- 3
  }
```

```R
levels(tAPGAR) <- c("Med", "Low", "High")
if (APGAR == 10 || APGAR == 8 || APGAR == 9) {
    tAPGAR <- 3
  } else if (APGAR == 1 || APGAR == 2 || APGAR == 3) {
    tAPGAR <- 2
  } else if (APGAR == 4 || APGAR == 5 || APGAR == 6 || APGAR == 7) {
    tAPGAR <- 1
  } else {
    tAPGAR<- 1
  }
```

--------------------------------

### rxode2parseGetPointerAssignment

Source: https://nlmixr2.github.io/rxode2/reference/rxode2parseGetPointerAssignment.html

This function gets the currently assigned function pointer assignments.

```APIDOC
## rxode2parseGetPointerAssignment

### Description
This function gets the currently assigned function pointer assignments.

### Usage
```
rxode2parseGetPointerAssignment()
```

### Value
The currently assigned pointer assignments
```

--------------------------------

### Get rxode2 model from object

Source: https://nlmixr2.github.io/rxode2/reference/rxGetrxode2.html

Retrieves the rxode2 model from an rxode2 family object.

```APIDOC
## rxGetrxode2

### Description
Get rxode2 model from object

### Usage
```R
rxGetrxode2(obj)
```

### Arguments

*   `obj` (rxode2 family of objects): The input object from which to extract the model.

### Value

rxode2 model
```

--------------------------------

### Create QD dosing regimen for 5 days

Source: https://nlmixr2.github.io/rxode2/reference/et.html

Create a QD (once a day) dosing event table for 5 days. Ensure time units are set appropriately.

```R
qd <- et(timeUnits="hr") |>
      et(amt=20000,ii=24,until=set_units(5, "days"))
```

--------------------------------

### Simulate with Geom Distribution Log Likelihood in rxode2

Source: https://nlmixr2.github.io/rxode2/reference/llikGeom.html

This example demonstrates how to integrate `llikGeom` and `llikGeomDprob` into an rxode2 model for simulation. It defines an `rxode2` model using a function, sets up an event table (`et`), and then solves the model using `rxSolve`.

```R
et  <- et(1:10)
et$prob <- 0.2

model <- function() {
  model({
    fx <- llikGeom(time, prob)
    dProb <- llikGeomDprob(time, prob)
  })
}

rxSolve(model, et)
```

--------------------------------

### get.sampling() - Get Sampling Records

Source: https://nlmixr2.github.io/rxode2/reference/eventTable.html

Returns a data.frame of sampling time observation records.

```APIDOC
## get.sampling()

### Description
Returns a data.frame of sampled observation records.

### Usage
```R
get.sampling()
```
```

--------------------------------

### Load rxode2 and lotri libraries

Source: https://nlmixr2.github.io/rxode2/articles/rxode2-eta-eps-resampling.html

Load the necessary libraries for rxode2 modeling and lotri for defining variance-covariance matrices.

```r
library(rxode2)
#> rxode2 5.0.2 using 2 threads (see ?getRxThreads)
#>   no cache: create with `rxCreateCache()`
library(lotri)
```

--------------------------------

### Initialize Model with Lotri-Type Syntax for Omega

Source: https://nlmixr2.github.io/rxode2/articles/Modifying-Models.html

Use lotri-type syntax to set initial estimates for parameters and their covariances within the omega matrix. This method is useful for defining the initial structure of between-subject variability.

```R
f4 <- f |> ini(eta.ka + eta.v ~ c(0.6,
                                  0.01, 0.2))
print(f4)
```

--------------------------------

### get.nobs() - Get Number of Observations

Source: https://nlmixr2.github.io/rxode2/reference/eventTable.html

Returns the number of observation (not dosing) records in the event table.

```APIDOC
## get.nobs()

### Description
Returns the number of observation (not dosing) records.

### Usage
```R
get.nobs()
```
```

--------------------------------

### get.dosing() - Get Dosing Records

Source: https://nlmixr2.github.io/rxode2/reference/eventTable.html

Returns a data.frame of dosing records currently in the event table.

```APIDOC
## get.dosing()

### Description
Returns a data.frame of dosing records.

### Usage
```R
get.dosing()
```
```