Parsing

Model Parsing

parse_model_file()

Parse a .ferx file into a CompiledModel. Only the core model blocks are processed ([parameters], [individual_parameters], [structural_model], [error_model]).

#![allow(unused)]
fn main() {
pub fn parse_model_file(path: &Path) -> Result<CompiledModel, String>
}

parse_model_string()

Parse a model from a string instead of a file.

#![allow(unused)]
fn main() {
pub fn parse_model_string(content: &str) -> Result<CompiledModel, String>
}

Example:

#![allow(unused)]
fn main() {
let model_str = r#"
[parameters]
  theta TVCL(0.1, 0.001, 10.0)
  theta TVV(10.0, 0.1, 500.0)
  omega ETA_CL ~ 0.09
  sigma ADD_ERR ~ 1.0

[individual_parameters]
  CL = TVCL * exp(ETA_CL)
  V  = TVV

[structural_model]
  pk one_cpt_iv_bolus(cl=CL, v=V)

[error_model]
  DV ~ additive(ADD_ERR)
"#;

let model = parse_model_string(model_str)?;
}

parse_full_model_file()

Parse a complete model file including [fit_options] and [simulation] blocks.

#![allow(unused)]
fn main() {
pub fn parse_full_model_file(path: &Path) -> Result<ParsedModel, String>
}

Returns a ParsedModel which contains:

#![allow(unused)]
fn main() {
pub struct ParsedModel {
    pub model: CompiledModel,
    pub simulation: Option<SimulationSpec>,
    pub fit_options: FitOptions,
}
}

Data Parsing

read_nonmem_csv()

Read a NONMEM-format CSV file into a Population.

#![allow(unused)]
fn main() {
pub fn read_nonmem_csv(
    path: &Path,
    covariate_columns: Option<&[&str]>,
) -> Result<Population, String>
}

Parameters:

• path: Path to the CSV file
• covariate_columns: Optional list of covariate column names. If None, all non-standard columns are auto-detected as covariates.

Example:

#![allow(unused)]
fn main() {
// Auto-detect covariates
let pop = read_nonmem_csv(Path::new("data.csv"), None)?;

// Explicit covariate list
let pop = read_nonmem_csv(
    Path::new("data.csv"),
    Some(&["WT", "CRCL", "AGE"]),
)?;

println!("{} subjects, {} observations",
         pop.subjects.len(), pop.n_obs());
}

Data Processing Details

• Column names are matched case-insensitively
• Standard NONMEM columns (ID, TIME, DV, EVID, AMT, CMT, RATE, MDV, II, SS) are recognized automatically
• Missing values (., empty string) are handled appropriately
• Rows with EVID=1 are treated as dose events
• Rows with EVID=0 and MDV=0 are treated as observations
• Time-constant covariates use the first non-missing value per subject
• Time-varying covariates use Last Observation Carried Forward (LOCF)