Desktop GUI

The OpenPKPD desktop GUI is organised around a simple hierarchy:

Workspace → the full session shown in the application window
Project → a modelling effort or study
Scenario → a branch within a project (for example Baseline, NCA, or a covariate variant)
Workflow page → a task-focused screen such as Data, Model, or Results

Use the Help → Help for this workflow action to jump directly to the section for the current page.

GUI architecture

        graph TB
    subgraph "Shell"
        MW["Main Window · Menu Bar · Sidebar Nav"]
    end

    subgraph "Workflows"
        WFS["Dashboard · Data · Model · Fit · NCA\nCovariate · Advanced · Results · Plots · Diagnostics"]
    end

    subgraph "Services"
        SVS["fit · data · nca · vpc · bootstrap · scm\ndesign · project · artifact · serialization · validation"]
    end

    subgraph "Domain"
        WS["Workspace"]
        PRJ["Project"]
        SCN["Scenario"]
        WS --> PRJ
        PRJ --> SCN
    end

    CORE["openpkpd Core Library\nEstimation · NCA · VPC · SCM · Bootstrap · Design · Plots · Reports"]

    MW --> WFS
    WFS --> SVS
    SVS --> WS
    SVS --> CORE

Quick start

If you are new to the GUI, the most common end-to-end path is:

Create or open a project.
Load data in Data.
Define the model in Model.
Run estimation in Fit.
Review outputs in Results, Plots, and Diagnostics.
Save a snapshot (.opkpd) so the workspace can be restored later.

Common tasks at a glance

If you want to…	Go to…	Notes
Load a CSV or sample dataset	Data workflow	Import your own file or load a bundled example
Set a lower limit of quantification	Data workflow	LOQ spinner in the options row
Build or edit a model	Model workflow	Builder and code views stay in sync with saved state
Select BLQ handling (M1/M3)	Model workflow	BLQ method combo in estimation settings
Estimate model parameters	Fit workflow	Uses the current scenario dataset + model
Run non-compartmental analysis	NCA workflow	Works without a compartmental fit
Review reports, logs, and artifacts	Results workflow	Best place to inspect completed fits
Browse plot artifacts	Plots workflow	Filter by analysis and plot type
Review diagnostics / generate NPDE	Diagnostics workflow	Focused fit-quality outputs
Highlight a subject across GOF plots	Diagnostics workflow	Subject filter combo in the filter row
Run VPC / bootstrap / design tools	Advanced workflow	Post-fit analysis tabs
Run VPC with stratification or pcVPC	Advanced workflow → VPC tab	Stratify-by combo and pcVPC checkbox
Run stepwise covariate modelling	Covariate workflow	Requires a ready base model
Save or reload the whole workspace	Workspace menu	Uses `.opkpd` snapshots

Shell basics

Workspace, projects, and scenarios

A workspace can contain multiple projects.
Each project contains one or more scenarios.
Each new project starts with a default Baseline scenario.
Scenario workflows are independent, supporting branching experiments: one scenario can hold the base fit, another an NCA-only analysis, and another a covariate extension.

        graph TD
    WS["Workspace\nname · root_path · recent_files"]
    PRJ["Project\nname · project_id"]
    SCN["Scenario\nname · scenario_id"]
    DS_A["DatasetAsset\npath · column mapping · LOQ · validated flag"]
    MS_A["ModelSpec\nADVAN · TRANS · PK/ERROR code\nestimation config · BLQ method"]
    RR_A["RunRecord\nrun_id · status\ntheta / omega / sigma · OFV"]
    AR_A["ArtifactRecord\nkind · path · label · created_at"]

    WS -->|"1 to many"| PRJ
    PRJ -->|"1 to many"| SCN
    SCN --> DS_A
    SCN --> MS_A
    SCN -->|"list"| RR_A
    SCN -->|"list"| AR_A

Preferences and defaults

The Preferences dialog is where you set shell-wide defaults such as:

Default workspace files location
UI presentation preferences such as font sizing
CPU cores for estimation/simulation

The default workspace files location is used as the starting directory for save and snapshot dialogs when an item does not already have a more specific path.

CPU cores (parallelism)

The CPU cores for estimation/simulation spinner controls how many parallel workers the GUI uses when running estimation and simulation jobs.

Value	Behaviour
0 (Auto)	Use all available CPU cores. Recommended for most users.
1	Serial execution — one subject or replicate at a time.
N > 1	Use exactly N parallel workers.

The spinner label shows the number of cores detected on your machine at startup (Auto (N cores detected)).

Parallelism is applied to:

FOCE / FOCEI inner-loop — per-subject η optimisation (process-based, true multi-core).
SAEM E-step — per-subject Metropolis–Hastings proposals (thread-based).
IMP subject evaluations — per-subject importance sampling (thread-based).
VPC simulation replicates — each replicate is dispatched to a thread worker.

Methods that are inherently serial (FO, Laplacian, BAYES, Nonparametric) are unaffected by this setting.

The preference is saved across sessions. It takes effect the next time a fit or VPC job is started — no restart required.

Snapshots and recent files

The GUI snapshot format is .opkpd.

Project snapshots preserve the workspace state, including:

projects and scenarios
active selection
saved workflow inputs
run history
registered artifacts
references to external files

When loading a snapshot, the GUI prompts before discarding unsaved changes. If external files are missing, the workspace metadata is still restored so you can reconnect those files later.

Workflow guide

Dashboard workflow

The Dashboard page is the fastest way to assess a scenario’s status.

It summarizes:

dataset readiness
model readiness
fit/NCA/results availability
recommended next actions

Use the Dashboard when you want to answer: What is ready? What still needs attention? What should I do next?

Quick buttons for recent snapshots and New Project / Open Snapshot actions are also available at the top of the Dashboard.

Data workflow

The Data page is where datasets are loaded, previewed, and validated.

Main areas

Example dataset controls — filter, browse, and load bundled examples with rich detail view (links to source files and URLs)
Import row — import a CSV from disk via path or file-browser button
Options row — separator character, quote/comment ignore character, treat-as-whitespace toggle, and scalar LOQ spinner
Columns panel — dataset column list
Preview panel — row preview table (first 100 rows)
Validation panel — warnings and errors from the imported dataset

An unsaved-changes indicator appears when the import controls differ from the saved dataset state.

Lower limit of quantification (LOQ)

The LOQ spinner in the options row lets you specify a scalar lower limit of quantification. When the dataset does not already contain an LLOQ column, the GUI injects the value as a constant LLOQ column at fit time, making it available to BLQ-aware estimation methods (see Model workflow → BLQ method).

LOQ setting	Effect at fit time
0.0 / None	No LOQ injected — `LLOQ` column unchanged.
> 0	Written to a new `LLOQ` column if no `LLOQ` column exists.

The LOQ value is persisted with the dataset import configuration across sessions.

Typical usage

Load an example dataset or import a CSV.
Confirm the source path and parsing options are correct.
If BLQ observations are present, enter the assay LOQ in the LOQ spinner.
Review the columns and preview panels.
Resolve validation errors before moving to Model.

Model workflow

The Model page defines the active model state for the scenario.

Model mode

Two radio buttons at the top select the model input mode:

Builder — a form-based editor for ADVAN/TRANS selection, THETA/OMEGA/SIGMA tables, PK/ERROR code, and estimation settings. Default for new models.
Control stream — a plain-text editor for loading or writing a NONMEM-style .ctl file directly.

Switching modes is instant; the scenario retains the last-saved state regardless of which view is active.

Main areas

Configuration panel (Builder mode) — dataset path, named model picker, ADVAN/TRANS spinners, estimation controls, and THETA/OMEGA/SIGMA parameter tables
Translation panel — translation summary, validation output, and code view
Control stream panel (Control stream mode) — plain-text editor plus example and file-open/save controls

Help buttons (?) on each section show tooltip descriptions of that control’s purpose, accepted values, and tips.

Named model presets

The Named model combo offers one-click presets for common ADVAN/TRANS pairs:

Preset	ADVAN	TRANS
1-compartment IV	1	1
1-compartment oral	2	2
2-compartment IV	3	1
2-compartment oral	4	4
Custom ODE	6	—

Selecting a preset fills in the ADVAN/TRANS spinners and default parameter tables automatically.

Estimation settings

Control	Description
Estimation method combo	`FO`, `FOCE`, `FOCEI`, `LAPLACIAN`, `SAEM`, `IMP`, `BAYES`, `NONPARAMETRIC`
maxeval spinbox	Maximum function evaluations
n_starts spinbox	Multi-start random restarts
Tight gtol checkbox	Tighter gradient convergence tolerance
BLQ method combo	BLQ handling: M1 (ignore) or M3 (censored likelihood)
Covariance checkbox	Enable sandwich covariance step
Covariance matrix combo	Covariance matrix method (Sandwich, Hessian, etc.)
Nonparametric base combo	Base method for NONPARAMETRIC (FOCE / FOCEI / FO)

Advanced FOCE/FOCEI optimizer controls (visible when a gradient-based method is selected):

Control	Description
Outer optimizer combo	Primary outer optimizer (L-BFGS-B, POWELL, etc.)
Fallback optimizer combo	Optimizer if outer fails
Fallback maxeval spinbox	Iteration limit for fallback
Retain best iterate checkbox	Keep best θ/η across all iterations
Retry on abnormal checkbox	Retry with perturbed ETAs on abnormal exit
Retry omega scales input	Comma-separated ETA perturbation scales (e.g. `0.5, 0.25, 0.1`)

BLQ method

The BLQ combo selects how below-quantification limit observations are handled at fit time.

Option	Method	Description
M1 — Ignore BLQ	M1	BLQ observations excluded from the likelihood (default).
M3 — Censored likelihood	M3	BLQ observations contribute `Φ((LOQ − IPRED)/σ)` — the NONMEM M3 method. Requires `LLOQ` in the dataset or a scalar LOQ set in the Data workflow.

The selected method is saved with the model specification and applied automatically when a fit is started.

THETA / OMEGA / SIGMA tables

Add / Remove buttons manage rows and blocks.
Suggest THETA auto-populates initial estimates based on the current model structure.
Cells are directly editable; changes are reflected immediately in the translation summary.

Control stream mode — example control streams

When Control stream mode is active, an Examples group appears below the editor with:

a searchable dropdown listing all bundled example control streams
a Load example button to apply the selected example
an Open from file… button to load a .ctl file from disk
a Save to file… button to write the current text to disk

Dataset handling in control stream mode

When you open a control stream, the GUI reads the $DATA path embedded in the file and automatically loads that CSV onto the Data screen.

Priority at fit time:

If you subsequently load a different dataset on the Data screen, that dataset takes precedence.
If no dataset has been loaded on the Data screen, the fit falls back to the $DATA path from the control stream.

This means you can open any bundled example and run it without touching the Data page, while retaining the ability to substitute your own data.

Keyboard shortcuts

Shortcut	Action
`Ctrl+S`	Save model state

Typical usage

Using Builder mode:

Confirm the correct dataset is attached.
Select a named model preset or set ADVAN/TRANS manually.
Choose the estimation method and BLQ handling.
Review/edit THETA, OMEGA, and SIGMA values; use Suggest THETA if needed.
Validate the translation.
Click Save model state before moving to Fit.

Using Control stream mode:

Select Control stream in the mode radio buttons.
Pick a bundled example and click Load example, or click Open from file….
The $DATA file is loaded automatically onto the Data screen.
Edit the control stream text if needed.
Click Save model state before moving to Fit.

Fit workflow

The Fit page checks scenario readiness for estimation and launches the fit.

Main areas

Preparation panel — readiness summary plus clickable validation items
Run panel — latest run summary, live convergence plot, and streaming log output
Action row — cancel (during run) and run fit buttons

Preparation summary states

Label	Meaning
Ready to start fit	Dataset and model are valid; the fit can be started.
Fit in progress	A fit is currently running.
Fit needs attention	One or more validation items must be resolved first.

Clicking a validation item in the list navigates directly to the source workflow (Data or Model) and focuses the relevant control.

Convergence plot

A live OFV-history chart updates during the run, showing the objective function value at each iteration. The chart is available in the run panel while the job is active.

Keyboard shortcuts

Shortcut	Action
`Ctrl+R`	Start fit (when ready)
`Escape`	Cancel running fit

Typical usage

Open Fit after saving dataset and model state.
Read the preparation summary.
Click validation items to resolve any issues.
Click Run fit.
Watch the convergence plot and run log.

NCA workflow

The NCA page supports standalone non-compartmental analysis.

Main areas

Options row — route, AUC method, λz settings, and terminal-phase options
Readiness panel — dataset validation and preparation summary
Results panel — latest run summary, preview output, and logs
Action row — refresh, open results, open artifacts folder, and run NCA

Options

Control	Description
Route combo	Administration route (Oral, IV, etc.)
AUC method combo	Numerical integration method (linear, linear-log, etc.)
Min points spinbox	Minimum number of points for λz terminal-phase regression
Exclude Cmax checkbox	Exclude the Cmax time point from λz fitting

Typical usage

Load a concentration-time dataset in Data.
Open NCA and review the readiness panel.
Configure route and AUC options.
Click Run NCA.
Review the generated CSV summary artifact.

Results workflow

The Results page focuses on fit runs, saved artifacts, and common follow-up actions.

Top summary

Overview label — run/output count and latest analysis status for the current scenario
Comparison snapshot — sibling-scenario summary for quick side-by-side context
Comparison panel — richer sibling-scenario summary with latest fit state, successful-run counts, and output counts
Comparison delta — quick comparison against the current best review target; highlights when the current scenario is ahead or behind
Artifact delta — highlights missing/extra artifact roles and plot groups versus the selected comparison target
Stale warning — warns when saved inputs changed after the latest successful fit
Next action — points back to the blocking workflow when Results is still empty

Runs list

Lists all fit runs for the selected scenario in reverse chronological order. Each row shows status, short run ID, and either the summary text or the error message. Selecting a row populates the detail and artifact panels.

Detail panel

Run detail label — status and summary for the selected run
Run metadata label — started/finished timestamps, log line count, artifact count
Run log — full scrollable log output for the run

Artifact panel

Filter row:

Control	Description
Kind filter	Filter by artifact kind (`html`, `csv`, `png`, etc.)
Role combo	Filter by logical role (`report`, `plot`, `diagnostics_table`, etc.)
Plot type combo	Filter by plot type (`gof_panel`, `cwres_vs_time`, `vpc`, etc.)

Review menu (dropdown):

Action	Description
Open convergence	Open the OFV-history plot
Open GOF review	Open the GOF panel artifact
Open residual review	Open CWRES / residual trends
Open ETA review	Open ETA histogram/pairs plots
Open profile review	Open profile-likelihood plots
Open Bayesian review	Open Bayesian posterior artifacts from the latest Bayesian-capable run

Quick actions:

Open latest report — open the latest HTML/PDF report
Export report PDF — export the latest report to PDF
Save latest plot copy… — copy the latest plot to another path
Save copy… — copy the selected artifact to disk
Open artifact — open the selected artifact in the system viewer
Open folder — open the artifact’s containing folder

Artifact preview — inline preview of the selected artifact:

Type	Preview
`.html`	Rendered HTML with relative asset links resolved
`.csv`	Interactive sortable table with column headers
`.txt`, `.log`, `.json`, `.md`	Plain-text browser
`.png`, `.jpg`, `.svg`	Image viewer with scroll support

Comparison review workflow

Use Comparison snapshot and Comparison panel to pick the most useful sibling scenario.
Read Comparison delta to see whether the current scenario is ahead or behind on successful runs and saved outputs.
Read Artifact delta to spot missing reports, tables, or plot groups.
Use Open comparison scenario to switch Results directly to that scenario.
Use the review menu to jump to convergence, GOF, or the latest report.

Plots workflow

The Plots page provides a browsable gallery of plot artifacts for the selected scenario.

Plot list

Lists all plot artifacts grouped by run. Each row shows the label, plot type, and file path. Selecting a row loads the preview panel.

Preview panel

Displays the selected plot inline (PNG, SVG, HTML). Use Open to launch in the system viewer or Save copy… to export.

Filter controls

Control	Description
Analysis combo	Filter by analysis type (`fit`, `vpc`, `bootstrap`, etc.)
Plot type combo	Filter by plot type (`gof_panel`, `vpc`, `cwres_vs_time`, etc.)

Diagnostics workflow

The Diagnostics page focuses on fit-quality outputs and diagnostics tables.

The page shows:

Overview label — scenario name, dataset/model readiness, latest fit status, artifact count
Stale warning — warns if dataset or model changed after the latest fit
Status label — whether the plotting stack is available
Next steps label — contextual guidance based on the current scenario state
NPDE status label — NPDE availability and whether an NPDE CSV exists

Filter row

Control	Description
Role combo	Filter artifacts by role
Plot type combo	Filter by plot type
Subject combo	Filter GOF plots by subject ID; overlays that subject’s points in red

Subject highlighting

When a subject is selected in the Subject combo, the active GOF plot is re-rendered with the selected subject’s observations highlighted in red. This works for single-panel plot types: DV vs IPRED, DV vs PRED, CWRES vs TIME, CWRES vs PRED, and |IWRES| vs IPRED.

Action buttons

Button	Description
View dropdown	Open GOF panel, residual trends, diagnostics CSV, or NPDE CSV
Open artifact	Open the selected artifact in the system viewer
Save copy…	Copy the selected artifact to disk
Open folder	Open the artifact’s containing folder
Generate NPDE	Run background NPDE generation (requires a fit in this session)
Refresh diagnostics	Refresh all status labels and artifact lists

Advanced workflow

The Advanced page is a tabbed hub for post-fit validation and design tools.

The current tabs are:

VPC
Bootstrap
Design
Artifacts

VPC tab

Control	Range / Default	Description
Replicates spinbox	10–5000, default 200	Number of simulation replicates
Bins spinbox	2–50, default 10	Number of time-axis bins
Seed spinbox	1–999999, default 42	Random seed for reproducibility
pcVPC checkbox	off	Prediction-corrected VPC — normalises DV and predictions by median PRED within each bin
Stratify by combo	None (default)	Optional covariate column to stratify the VPC (e.g. `DOSE`, `SEX`); produces one panel per stratum

The Stratify by combo is populated from the active dataset’s columns, excluding mandatory NONMEM columns (ID, TIME, AMT, DV, EVID, MDV). It repopulates automatically when the dataset changes.

Run VPC is enabled only when a fit was completed in the GUI session. Replicates are distributed across worker threads using the CPU cores setting from Preferences (0 = auto).

Generated artifacts:

Artifact	Description
VPC summary CSV	Observed and simulated percentile bands per bin
VPC plot	Shaded percentile overlay on observed data
Simulation panel	Spaghetti plots of simulated vs. observed profiles
Prediction interval plot	Simulated median and PI bounds vs. time

When stratification is active, the run summary includes stratify=<column>.

Bootstrap tab

Control	Range / Default	Description
Replicates spinbox	10–2000, default 100	Number of bootstrap samples
Jobs spinbox	1–64, default 1	Parallel worker count
Seed spinbox	1–999999, default 42	Random seed
CI level spinbox	0.50–0.99, default 0.95	Confidence interval level for BCa

Run bootstrap generates summary, CI table, and sample-table artifacts.

Design tab

The Design tab exposes optimal-design controls:

Control	Description
Samples spinbox	Sample observations per subject
Subjects spinbox	Number of subjects
Tmin / Tmax spinboxes	Sampling window boundaries
Criterion combo	Optimality criterion (D, A, E, etc.)
Method combo	Optimisation algorithm
Restarts spinbox	Number of random restarts

Run design generates summary, metrics, schedule, FIM, and expected-SE artifacts.

Current scope note:

the GUI fronts the currently implemented PFIM engine rather than a full multi-endpoint design workbench,
residual-error support follows the scalar-variance PFIM boundary documented in the optimal-design example and validation docs,
users needing broader design experimentation should prefer the Python API and validate assumptions explicitly.

Artifacts tab

The Artifacts tab browses the shared Advanced artifact pool.

Scope	Artifacts included
All	All Advanced artifacts
VPC	`vpc_summary` and VPC plot artifacts
Bootstrap	`bootstrap_summary`, `bootstrap_ci_table`, `bootstrap_samples`
Design	`design_summary`, `design_metrics`, `design_schedule`, `design_fim`, `design_expected_se`

Action buttons: Open artifact, Save copy…, Open folder.

Covariate workflow

The Covariate page exposes stepwise covariate modelling (SCM) against the active base model.

Candidates table

Columns: Parameter, Covariate, Effect, Reference

Parameter — PK parameter to test (e.g. CL, V)
Covariate — dataset column to use (e.g. WT, AGE)
Effect — relationship type: power, linear, exp, categorical
Reference — reference value for continuous covariates

Use Add candidate and Remove selected to manage the list.

Search parameters

Control	Range / Default	Description
Forward p-value	0.001–0.5, default 0.05	Inclusion threshold for the forward step
Backward p-value	0.0001–0.5, default 0.001	Exclusion threshold for the backward step
Parallel jobs	−1–64, auto	Number of parallel estimation jobs (−1 = auto)

Results table

Columns: Type, Relationship, ΔOFV, p-value, Status.

Rows are colour-coded: accepted relationships are highlighted green, rejected ones are shown normally. Use the accepted relationships as the basis for a derived scenario or model update.

Action buttons

Button	Description
Refresh readiness	Re-check that the base model and dataset are ready
Run SCM	Submit the stepwise covariate search
Cancel	Cancel a running SCM

End-to-end walkthroughs

Walkthrough 1 — Theophylline one-compartment FOCE from scratch

This walkthrough fits the classic theophylline oral one-compartment PK dataset using FOCE estimation.

Step 1 — Create the project

Launch the GUI (openpkpd-gui).
In the Workspace menu, choose New Project… and enter a name such as Theophylline.
The GUI creates a default Baseline scenario.

Step 2 — Load the dataset

Select Baseline → Data.
Type theo in Filter examples.
Select theophylline and click Load example.
Confirm the validation panel shows no blocking issues.

Step 3 — Define the model

Select Baseline → Model.
Ensure the Builder radio button is selected (the default).
Set Problem title to Theophylline 1-CMT oral.
Click Use active dataset.
Set ADVAN = 2, TRANS = 2, Estimation = FOCE.
Review THETA initial estimates (use Suggest THETA if needed).
Leave BLQ method = M1 — Ignore BLQ (no BLQ observations in this dataset).
Click Save model state.

Alternative: Select the Control stream radio button, pick a theophylline example from the Examples dropdown, and click Load example. The dataset is loaded automatically.

Step 4 — Run the fit

Select Baseline → Fit.
Confirm the preparation panel says the fit is ready.
Click Run fit (or press Ctrl+R).
Watch the live convergence plot and run log.

Step 5 — Review results

Open Baseline → Results to inspect the fit, report artifacts, and plot outputs.
Use Comparison panel to compare with sibling scenarios.
Use the review dropdown (convergence, GOF, residuals, ETA review) for focused inspection.
Open Baseline → Diagnostics for CWRES and other diagnostics.
Use the Subject filter in Diagnostics to highlight individual subjects across GOF plots.

Step 6 — Save the snapshot

Choose Workspace → Save Project Snapshot….
Save the .opkpd archive so the entire workspace can be restored later.

Walkthrough 2 — Standalone NCA on a plasma concentration dataset

This walkthrough runs non-compartmental analysis without a compartmental fit.

Step 1 — Create the scenario

In an existing or new project, select the project.
Choose Workspace → New Scenario… and name it NCA.

Step 2 — Load the dataset

Open the scenario’s Data workflow.
Import a CSV with at least ID, TIME, DV, and EVID.
Confirm the preview and validation panels look correct.

Step 3 — Configure and run NCA

Open NCA → NCA.
Set route, AUC method, and λz options.
Confirm the readiness panel reports the dataset cleanly.
Click Run NCA.
Review the latest results summary and preview output.

Walkthrough 3 — Post-fit VPC with stratification and covariate search

This walkthrough assumes you already completed the fit from Walkthrough 1.

Step 1 — Generate a stratified VPC

Select Baseline → Advanced.
On the VPC tab, set replicates, bins, and seed.
To stratify by dose group, choose DOSE from the Stratify by combo.
Check pcVPC for prediction-corrected output (optional).
Click Run VPC.
Open the Artifacts tab, filter Scope = VPC to review plots and summary.

Step 2 — Configure the covariate search

Select Baseline → Covariate.
Add one or more candidates such as CL ~ WT.
Set forward/backward thresholds and parallel job settings.

Step 3 — Run SCM

Click Run SCM.
Review accepted and rejected relationships in the results table.
Use accepted relationships as the basis for a derived scenario/model update.

Troubleshooting and tips

Common reasons a button is disabled

Fit / SCM / NCA run buttons are often disabled because required dataset or model state is incomplete or an existing run is still in progress.
Read the readiness summary on the current workflow page first.
Review validation lists before assuming a background runner failed.
For VPC and Bootstrap, a reusable fit must exist in the current session.

BLQ observations

If your dataset contains observations below the assay LOQ:

Enter the scalar LOQ value in the LOQ spinner on the Data page.
On the Model page, set BLQ method = M3 — Censored likelihood.
Run the fit normally — the M3 likelihood contribution is applied automatically.

If your dataset already has a per-observation LLOQ column, the LOQ spinner value is ignored at fit time (the existing column takes precedence).

Choosing the right review page

Use Results when you want run-centric context, logs, and all artifacts.
Use Plots when you want a gallery view filtered by plot type or analysis.
Use Diagnostics when you want fit-quality outputs, NPDE actions, or subject-level highlighting across GOF plots.

Keyboard shortcuts

Shortcut	Context	Action
`Ctrl+S`	Model workflow	Save model state
`Ctrl+R`	Fit workflow	Start fit
`Escape`	Fit workflow	Cancel running fit

Help resources

Help → Help for this workflow jumps directly to the current workflow section.
Help → User Guide opens the full guide from the top.
Help buttons (?) on each major section in the Model workflow show inline tooltips with control descriptions.
If a workflow references artifacts, the scenario must usually have completed at least one relevant run.

Desktop GUI

GUI architecture

Quick start

Common tasks at a glance

Shell basics

Workspace, projects, and scenarios

Navigation and layout

Menu overview

Workspace menu

Help menu

Preferences and defaults

CPU cores (parallelism)

Snapshots and recent files

Workflow guide

Dashboard workflow

Data workflow

Main areas

Lower limit of quantification (LOQ)

Typical usage

Model workflow

Model mode

Main areas

Named model presets

Estimation settings

BLQ method

THETA / OMEGA / SIGMA tables

Control stream mode — example control streams

Dataset handling in control stream mode

Keyboard shortcuts

Typical usage

Fit workflow

Main areas

Preparation summary states

Convergence plot

Keyboard shortcuts

Typical usage

NCA workflow

Main areas

Options

Typical usage

Results workflow

Top summary

Runs list

Detail panel

Artifact panel

Comparison review workflow

Plots workflow

Plot list

Preview panel

Filter controls

Diagnostics workflow

Filter row

Subject highlighting

Action buttons

Advanced workflow

VPC tab

Bootstrap tab

Design tab

Artifacts tab

Covariate workflow

Candidates table

Search parameters

Results table

Action buttons

End-to-end walkthroughs

Walkthrough 1 — Theophylline one-compartment FOCE from scratch

Step 1 — Create the project

Step 2 — Load the dataset

Step 3 — Define the model

Step 4 — Run the fit

Step 5 — Review results

Step 6 — Save the snapshot

Walkthrough 2 — Standalone NCA on a plasma concentration dataset

Step 1 — Create the scenario

Step 2 — Load the dataset

Step 3 — Configure and run NCA

Walkthrough 3 — Post-fit VPC with stratification and covariate search

Step 1 — Generate a stratified VPC

Step 2 — Configure the covariate search

Step 3 — Run SCM