GITNUXSOFTWARE ADVICE
Data Science AnalyticsTop 8 Best Pk Analysis Software of 2026
Ranking roundup of top pk analysis software tools, comparing Monolix, Phoenix WinNonlin, and NONMEM for PK modeling and fit needs.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Monolix is the strongest pick for teams that need repeatable population PK/PD modeling with solid diagnostics and scenario batch runs, whereas PK-Sim fits best when labs want open-source PBPK and PK model simulations starting from dosing and sampling data.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Monolix
Integrated simulation-based diagnostics driven from the same fitted model and parameter estimates.
Built for fits when teams need repeatable population PK modeling with strong diagnostics and scenario batch runs..
Phoenix WinNonlin
Editor pickModel simulation and diagnostic workflows for PK model checking, including scenario-based evaluation using fitted parameters.
Built for fits when pharmacometrics teams need repeatable NCA and compartmental modeling with diagnostics and script-driven reruns..
NONMEM
Editor pickControl-stream execution for nonlinear mixed-effects modeling that keeps estimation, model structure, and diagnostics tightly coupled in one run context.
Built for fits when PK teams need reproducible nonlinear mixed-effects estimation and fine control over model structure..
Related reading
Comparison Table
Monolix
enterprisePopulation PK/PD analysis software using stochastic approximation methods.
Integrated simulation-based diagnostics driven from the same fitted model and parameter estimates.
Monolix targets population pharmacokinetics workflows by coupling model specification with estimation and a diagnostic loop that covers both goodness-of-fit visuals and predictive simulation checks. The workflow is centered on a reusable modeling setup, which helps when running the same model across studies with different subjects or sampling schedules. It supports covariate modeling and residual error model selection as first-class parts of the modeling cycle, not as post-processing steps.
A key tradeoff is that Monolix works best when model structure and estimation settings are formalized up front, since flexible exploratory modeling can require rework of the model specification. It fits teams that repeatedly build and validate compartmental PK models with similar data structures, such as dose-ranging studies with standardized bioanalytical assay outputs.
- +Tight model-estimation-diagnostics loop for population PK workflows
- +Simulation-based diagnostics for checking predictive behavior
- +Structured covariate and residual error specification during modeling
- +Batch execution supports repeated runs across datasets and scenarios
- –Less suited to rapid ad hoc exploration without revising model structure
- –Complex models need careful selection of estimation settings
Clinical pharmacometrics teams
Build and validate population PK models
More defensible model diagnostics
Translational PK modelers
Evaluate covariate effects on clearance
Quantified covariate impact
Show 2 more scenarios
Bioanalytical scientists
Assess model fit across assay lots
Tighter assay-to-model consistency
Use consistent estimation and diagnostic outputs to compare modeling outcomes across runs.
Modeling operations teams
Run batch scenarios for dose selection
Faster scenario comparison
Execute repeated modeling runs across datasets to support simulation-driven decision making.
Best for: Fits when teams need repeatable population PK modeling with strong diagnostics and scenario batch runs.
More related reading
Phoenix WinNonlin
enterprisePharmacokinetic and pharmacodynamic analysis software for regulated development workflows.
Model simulation and diagnostic workflows for PK model checking, including scenario-based evaluation using fitted parameters.
Phoenix WinNonlin covers noncompartmental analysis workflows and compartmental modeling in the same toolset, so teams can move from AUC and Cmax summaries into model-based fits without changing software. The workflow includes goodness-of-fit and diagnostic plotting plus bootstrap-based uncertainty workflows that support typical pharmacometrics reporting. Rank placement reflects strong coverage of standard PK outputs, model validation support, and practical repeatability for study series with many subjects.
A tradeoff is that advanced automation and model packaging require disciplined project structure so that scripts, inputs, and outputs stay consistent across runs. It fits best when a pharmacometrics group needs repeatable PK runs for multiple studies or iterative model development cycles, not just one-off exploratory plots.
- +NCAs and model-based fits share consistent outputs and reporting
- +Bootstrap and diagnostic plotting support repeatable model evaluation workflows
- +Scriptable runs reduce manual reruns across subjects and studies
- +Simulation-based checks support scenario testing beyond fitted curves
- –Advanced projects need careful configuration to avoid inconsistent reruns
- –Some niche integrations require external ETL for input preparation
- –Large modeling sessions can feel heavy without planned study partitioning
Clinical pharmacometrics teams
Iterative compartmental model development
Faster model selection cycles
Biostatistics and PK leads
Study-wide exposure reporting
Consistent pharmacokinetic summaries
Show 2 more scenarios
Translational research groups
Population modeling for covariates
Quantified between-subject variability
Fit population models and assess covariate effects using standard diagnostic outputs.
Regulated reporting teams
Bootstrap uncertainty quantification
Tighter uncertainty intervals
Estimate variability with bootstrap workflows and compare results across analysis configurations.
Best for: Fits when pharmacometrics teams need repeatable NCA and compartmental modeling with diagnostics and script-driven reruns.
NONMEM
enterprisePopulation pharmacokinetic and pharmacodynamic modeling software for nonlinear mixed-effects analysis.
Control-stream execution for nonlinear mixed-effects modeling that keeps estimation, model structure, and diagnostics tightly coupled in one run context.
NONMEM’s core capability is nonlinear mixed-effects modeling for compartmental analysis, including residual error models, covariate effects, and repeated estimation for refinement. The data flow connects dose administration records and sampling schedules to observed plasma or serum concentrations through structured input files. Model checking workflows support standard PK diagnostics, including goodness-of-fit plots, visual predictive check workflows, and resampling-based techniques.
The biggest tradeoff is that model-building and computational execution require strong technical setup of control streams, dataset formatting, and estimation choices. NONMEM fits situations where teams already run population PK modeling workflows, need reproducible estimation over many model candidates, and want fine control over the statistical model and numerical engine settings. It can be a slower choice for purely exploratory analyses when minimal scripting and UI-driven modeling are the priority.
- +Nonlinear mixed-effects modeling with detailed error and variability specification
- +Control-stream driven runs support repeatable model refinement cycles
- +Differential equation based compartment definitions for PK parameter estimation
- +Diagnostics outputs support iterative model evaluation workflows
- –Requires disciplined control-stream and dataset setup for reliable runs
- –Less suited to point-and-click exploratory PK analysis
- –Automation often relies on external scripting around batch jobs
- –Complex models can increase compute time and tuning overhead
Population PK scientists
Model covariate effects on clearance
Tighter dosing recommendations
Clinical pharmacology groups
Handle sparse sampling schedules
Stable parameter estimates
Show 2 more scenarios
Translational PK teams
Run simulation-based diagnostics for candidates
Better model credibility
Compare predicted distributions to observed data to refine the nonlinear mixed-effects model.
Modeling and simulation automation
Batch run many model variants
Faster model iteration
Execute controlled estimation batches for structured model comparisons and iterative re-fitting.
Best for: Fits when PK teams need reproducible nonlinear mixed-effects estimation and fine control over model structure.
PK-Sim
vertical specialistOpen-source physiologically based pharmacokinetic modeling software.
Object-based model building that links dosing, sampling schedules, and concentration–time simulations within a single modeling project.
PK-Sim from open-systems-pharmacology.org focuses on PK analysis and PBPK model workflows that start from concentration–time data and converge on reusable simulation models. The software supports compartmental modeling and parameter estimation workflows that include common PK outputs like exposure measures and terminal elimination descriptors.
PK-Sim also provides a simulation environment for testing dosing regimens against sampling schedules and derived concentration–time profiles. Workflow repeatability is driven by model reuse and scenario runs rather than ad hoc spreadsheet calculations.
- +Strong PBPK and compartment model reuse across study scenarios
- +Built-in noncompartmental outputs from concentration–time inputs
- +Simulation comparisons tied to sampling schedules and dosing records
- +Model validation visuals for fitting and diagnostic review
- –Model setup requires domain-specific parameter and structure decisions
- –Automation and API surfaces are limited compared with code-first toolchains
- –Handling of complex population structures needs additional workflow discipline
- –Large projects can feel slower when iterating on model components
Best for: Fits when labs need repeatable PBPK and PK model simulations from dosing and sampling data.
Pumas
API-firstJulia-based pharmacometric software for population PK and PKPD modeling.
API-first orchestration for PK run provisioning and parameter-table retrieval across iterative modeling and diagnostics.
Pumas runs PK analysis workflows from concentration–time data through parameter estimation and model diagnostics. It supports model-building automation for population and nonlinear mixed-effects modeling and ties outputs to simulation-based checks. The system emphasizes API-driven integration so labs and modelers can provision runs, import datasets, and retrieve pharmacokinetic parameter tables programmatically.
- +API surface supports scripted dataset ingestion and run execution
- +Automated model diagnostics connects fitting outputs to visual checks
- +Population modeling workflows reduce manual rework across iterations
- +Clear separation between inputs, model config, and generated outputs
- –Less suited to one-off analyses when no automation is needed
- –Governance controls for multi-analyst projects may require careful setup
- –Dataset mapping edge cases can slow down complex assay layouts
- –Limited native support for highly customized reporting layouts
Best for: Fits when teams need repeatable PK runs with API-driven integration into lab workflows.
GastroPlus
vertical specialistPhysiologically based pharmacokinetic modeling software for absorption and drug disposition studies.
Physiologically based pharmacokinetic modeling workflows that connect physiological inputs to simulated exposure outputs.
GastroPlus from Simulations Plus targets pharmacokinetic analysis with a mix of simulation modeling and PK parameter estimation workflows built around oral drug disposition. It supports compartmental and noncompartmental analyses using concentration–time data, and it can run simulation-based diagnostics like goodness-of-fit plots and visual predictive checks.
GastroPlus also includes population-style modeling capabilities for nonlinear mixed-effects modeling workflows, plus physiologically based modeling for mechanism-driven exposure predictions. The result is a single toolchain for dataset setup, model fitting, simulation, and iterative refinement when PK questions span both fitting and prediction.
- +Integrated compartmental and noncompartmental analysis in one workflow
- +Physiologically based modeling supports mechanism-driven exposure predictions
- +Simulation and model diagnostics help validate concentration–time fits
- +Nonlinear mixed-effects modeling workflow supports variability and covariates
- –Model setup requires detailed dosing and sampling schedule definitions
- –Population workflows depend on careful data formatting and cleanup
- –Extensibility through external automation appears limited versus script-first tools
- –Some advanced diagnostics take time to interpret for new teams
Best for: Fits when teams need one environment for fitting and simulation across compartmental, noncompartmental, and PBPK use cases.
nlmixr2
API-firstOpen-source R framework for nonlinear mixed-effects pharmacometric modeling.
A modeling-first nlmixr2 workflow that keeps fitting, simulation-based diagnostics, and parameter reporting tied to the same model code.
nlmixr2 is an open-source nonlinear mixed-effects modeling environment that runs PK analysis as scripted model definitions rather than a guided UI workflow.
The PK workflow centers on nonlinear mixed-effects modeling for concentration–time data, with explicit residual and variability structures and model-based predictions used for diagnostics.
Because dose administration records and sampling schedules are represented in the modeling inputs, model runs can be repeated with consistent mapping from raw assay data through PK parameter estimation.
Diagnostics and simulation-based checks are tied to the fitted model definition, which reduces drift between fitting and evaluation steps when iterating on covariate effects and error models.
- +Model specification is code-driven for reproducible PK analysis workflows
- +Population PK fitting supports nonlinear mixed-effects likelihood structures
- +Simulation-based diagnostics and visual checks run from the fitted model
- +Good support for iterative covariate modeling across multiple datasets
- –Learning curve is higher than PK GUIs that hide model structure
- –Complex models require careful data preparation for dosing and sampling alignment
- –Less emphasis on drag-and-drop workflow automation for analysts
Best for: Fits when teams need scripted population pharmacokinetics modeling with repeatable diagnostics.
mrgsolve
API-firstOpen-source R package for simulating pharmacometric models.
Compiled model execution in a script workflow that supports large batch simulations and deterministic outputs.
mrgsolve is an open modeling workflow for pharmacokinetic and pharmacodynamic analysis built around a compiled model engine for fast simulation and estimation-ready outputs. It supports both model-based simulation and downstream PK parameter summaries, including concentration–time generation from dosing and sampling inputs.
The tool’s workflow favors reproducible scripts that generate dosing scenarios, run simulation or fitting iterations, and write structured results tables for analysis and diagnostics. It is most distinct where PK models need to be executed at high throughput for scenario testing and population-style workflows.
- +Script-driven model and simulation runs for repeatable PK analyses
- +High-throughput execution supports large scenario grids and batch runs
- +Generates structured concentration outputs tied to dosing and sampling records
- +Companion workflows fit into noncompartmental and model-based analysis chains
- –Model authoring requires learning its DSL and build workflow
- –Population pharmacokinetics estimation and full diagnostics require external integration
- –Governance controls like RBAC and audit logging are not a native focus
- –Complex study designs may require custom data shaping for dosing records
Best for: Fits when PK teams need scripted model execution at scale and can handle external fitting or diagnostics.
Conclusion
After evaluating 8 data science analytics, Monolix stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right pk analysis software
This buyer's guide covers how to select pharmacokinetic analysis software for concentration-time data workflows, including noncompartmental analysis, compartmental fits, nonlinear mixed-effects modeling, and PBPK simulation.
Monolix, Phoenix WinNonlin, NONMEM, PK-Sim, Pumas, GastroPlus, nlmixr2, and mrgsolve are used as concrete examples for integration, automation, and repeatability tradeoffs across regulated and research settings.
Pharmacokinetic analysis software for fitting models, estimating parameters, and simulating exposure
PK analysis software processes concentration–time data with dosing and sampling schedule inputs to produce PK parameter tables such as exposure metrics and compartment descriptors. Many tools also run model diagnostics and simulation-based checks that tie fitted parameters back to predicted concentration–time profiles.
Some tools focus on a tight population modeling loop like Monolix and NONMEM, where model definition, estimation, and diagnostics run together for nonlinear mixed-effects workflows. Others target end-to-end simulation and PBPK style regimen testing like PK-Sim and GastroPlus, where sampling schedules and dosing records drive scenario outputs.
Evaluation criteria for PK workflows: model repeatability, diagnostics loop, and automation control
PK teams usually fail or succeed on repeatability rather than raw modeling capability, because parameter estimation, diagnostic plots, and scenario runs must remain consistent across datasets and study partitions.
The most decisive criteria center on how each tool couples fitting with simulation diagnostics, how it supports automation and scripted reruns, and how it manages the workflow artifacts needed for governance across multiple analysts.
Integrated simulation-based diagnostics tied to fitted parameters
This feature ensures scenario checks use the same model and parameter estimates that produced the fit. Monolix runs simulation-based diagnostics directly from fitted model outputs, and Phoenix WinNonlin provides scenario-based evaluation using fitted parameters.
Control-stream or model-code execution for reproducible nonlinear mixed-effects runs
Reproducibility depends on running estimation settings and model structure from a versionable artifact. NONMEM ties differential equation systems, covariance structure, and diagnostics into control-stream execution, and nlmixr2 keeps fitting, simulation-driven diagnostics, and parameter reporting in the same modeling code.
API-first orchestration for automated dataset ingestion and parameter-table retrieval
API-driven workflows reduce manual handoffs between data prep, model execution, and downstream extraction of PK parameter tables. Pumas is built around an API surface for provisioning runs and retrieving generated outputs programmatically.
Batch execution and scriptable reruns for multi-study or multi-scenario pipelines
Batch and scripting features matter when model building must repeat across subjects, studies, and scenario grids. Monolix supports batch execution across datasets and scenarios, and Phoenix WinNonlin provides scriptable analyses that reduce manual reruns.
Model project structure that links dosing, sampling schedules, and concentration–time simulations
Workflow cohesion reduces errors when dosing schedules and sampling schedules must stay aligned across simulation and fitting. PK-Sim uses object-based model building that links dosing and sampling within a single modeling project, and mrgsolve generates concentration outputs from dosing and sampling inputs in script-driven runs.
Simulation-first PBPK or mechanism-driven exposure modeling environment
Mechanism-driven simulation is required when exposure predictions need physiological input mapping instead of only empirical fits. GastroPlus provides physiologically based workflows that connect physiological inputs to simulated exposure outputs, and PK-Sim supports reusable PBPK simulation models with scenario runs tied to sampling schedules.
Decision framework for choosing the right PK analysis tool for a specific workflow
First choose the workflow center of gravity: population nonlinear mixed-effects modeling, PBPK and mechanistic simulation, or script-driven high-throughput scenario execution. The tool should match how the team already expresses model structure and how it wants diagnostics to run.
Then verify automation depth and governance fit by checking whether the tool can rerun the same model artifacts consistently across datasets and whether it exposes integration points for orchestration.
Match the tool to the workflow artifact the team will version and rerun
If model structure and estimation settings must be controlled through a code artifact, start with NONMEM control streams or nlmixr2 model code for nonlinear mixed-effects workflows. If runs must be provisioned and outputs retrieved programmatically, Pumas is designed for API-driven orchestration of dataset ingestion and run execution.
Choose a tool where diagnostics are generated from the fitted model, not from separate ad hoc steps
For teams that need simulation-based checks that reflect fitted parameters, Monolix and Phoenix WinNonlin keep diagnostics workflows tied to the same fitted model and parameter estimates. For scenario-heavy review cycles, validate that the tool supports scenario evaluation using the fitted parameters rather than only curve overlays.
Decide between GUI-structured model projects versus code-first reproducible modeling pipelines
If structured modeling objects and project-level linkage between dosing, sampling schedules, and simulations are required, PK-Sim’s object-based model building fits the workflow. If scripted reproducibility and high-throughput simulation at scale matter more than a guided interface, mrgsolve and nlmixr2 provide script-first execution patterns.
Pick the environment that covers the same analysis loop from fitting through PBPK or mechanism-driven prediction
If the analysis needs a single environment that spans noncompartmental and compartmental analysis plus physiologically based simulation, GastroPlus is built as an integrated toolchain for fitting and prediction. If PBPK model reuse and sampling-schedule-tied simulation are the priority, PK-Sim emphasizes model reuse and scenario runs within a modeling project.
Plan for operational automation and rerun reliability in complex projects
When advanced projects require consistent reruns across studies, Phoenix WinNonlin needs careful configuration to avoid inconsistent reruns, so governance has to be part of the pipeline design. When complex models require careful estimation settings, Monolix also benefits from disciplined estimation configuration to keep model refinement cycles stable.
Who should use which PK analysis software based on actual workflow fit
PK analysis tools map to distinct team needs around repeatability, diagnostics depth, and integration strength. The best match depends on whether the work is primarily population modeling, end-to-end NCA and compartmental analysis, PBPK regimen simulation, or scripted high-throughput scenario execution.
These audience segments align with each tool’s best-for fit, including how diagnostics and automation are expected to run across iterations.
Population pharmacometrics teams that need repeatable nonlinear mixed-effects modeling with strong diagnostics loops
Monolix fits this segment because it delivers an integrated simulation-based diagnostics workflow driven from the same fitted model and parameter estimates. NONMEM is a close alternative when teams require control-stream execution that keeps estimation and diagnostics tightly coupled in one run context.
Regulated pharmacometrics workflows that require repeatable NCA plus compartmental model-based fits and diagnostics
Phoenix WinNonlin fits because it centers end-to-end concentration–time processing, provides bootstrap and diagnostic plotting for repeatable model evaluation, and supports script-driven reruns. GastroPlus is a good fit when regulated workflows also require physiologically based exposure prediction within the same environment.
Teams building API-connected PK pipelines that provision runs and retrieve parameter tables programmatically
Pumas fits when automation is a first-order requirement because its API-first orchestration supports dataset ingestion, run execution, and parameter-table retrieval. This segment also benefits from automated model diagnostics that connect fitting outputs to visual checks.
Labs focused on PBPK regimen simulation with reusable models and scenario testing against dosing and sampling schedules
PK-Sim fits because it emphasizes object-based model building that links dosing, sampling schedules, and concentration–time simulations within a single modeling project. GastroPlus is an alternative when mechanism-driven exposure predictions from physiological inputs need to coexist with integrated fitting and simulation workflows.
Modeling engineers who need scripted scenario execution at scale and can run full diagnostics outside the core engine
mrgsolve fits because it uses a compiled model engine for fast simulation and supports high-throughput execution for large batch scenario grids. nlmixr2 fits when the primary priority is modeling-first nonlinear mixed-effects workflows with simulation-driven diagnostics in the same codebase.
Common PK tool selection pitfalls that derail reproducibility and integration
Many PK tool failures occur when teams select software based only on modeling capability and ignore how model artifacts, diagnostics, and reruns are orchestrated. Other failures occur when automation expectations exceed the tool’s native execution or integration style.
The pitfalls below come from recurring cons across the available tools and from where each tool requires workflow discipline.
Choosing a tool for quick exploration when the workflow requires model-structure changes to be reproducible
Monolix is less suited to rapid ad hoc exploration because it is designed for iterative population PK workflows that revise model structure within a controlled modeling-estimation-diagnostics loop. Phoenix WinNonlin can also feel heavy in large modeling sessions unless study partitioning is planned, so exploratory work should align with the tool’s rerun model.
Underestimating setup discipline needed for reliable reruns in control-stream or dataset-heavy workflows
NONMEM requires disciplined control-stream and dataset setup to keep reliable runs, so operational standards for datasets and control files must be defined before large batches. Phoenix WinNonlin’s advanced projects need careful configuration to avoid inconsistent reruns, so rerun reproducibility should be tested at the intended project scale.
Assuming automation and integration capabilities match code-first or API-first tools without confirming integration surfaces
PK-Sim has limited automation and API surface compared with script-first toolchains, so external orchestration will likely be more manual for complex pipelines. mrgsolve supports compiled simulation at scale, but population pharmacokinetics estimation and full diagnostics require external integration, so diagnostics planning must be part of the workflow design.
Selecting a PBPK or simulation-first tool when model setup and parameter decisions are not yet domain-ready
PK-Sim can require domain-specific parameter and structure decisions that slow initial setup when domain assumptions are not settled. GastroPlus also depends on detailed dosing and sampling schedule definitions, so missing schedule detail will degrade simulation setup and downstream fit validation.
Overlooking governance and analyst workflow controls for multi-analyst work
Pumas can require careful governance setup for multi-analyst projects, especially when governance controls are a workflow requirement. mrgsolve does not natively focus on RBAC and audit logging, so governance must be handled by surrounding infrastructure rather than relying on built-in controls.
How We Selected and Ranked These Tools
We evaluated Monolix, Phoenix WinNonlin, NONMEM, PK-Sim, Pumas, GastroPlus, nlmixr2, and mrgsolve using criteria tied to features, ease of use, and value. Features carried the most weight in the overall score at 40 percent, while ease of use and value each accounted for 30 percent. This criteria-based scoring reflects how each tool is described around workflow repeatability, diagnostics behavior, and automation or execution style rather than hands-on laboratory testing.
Monolix separated itself by tying simulation-based diagnostics directly to the same fitted model and parameter estimates, and that coupling improved performance on the features category more than on ease or value alone. The resulting integrated model-estimation-diagnostics loop aligns closely with repeatable population PK workflows and scenario batch runs, which is why it ranks highest across the set.
Frequently Asked Questions About pk analysis software
Which PK analysis tool is best for repeatable population PK modeling with batch scenarios?
How does Pumas handle integration for provisioning PK runs and retrieving parameter tables?
When does Phoenix WinNonlin fit better than MONMEM for compartmental modeling workflows?
What breaks if a team tries to use PK-Sim for tasks that require script-first model execution?
How does Monolix differ from nlmixr2 when mapping dose administration records and sampling schedules into a single model input?
Which tool is strongest for PBPK-style workflows that simulate dosing regimens against sampling schedules?
What tradeoff appears when switching from NONMEM’s control-stream execution to compiled scripted workflows in mrgsolve?
How do simulation-based diagnostics differ across tools like Phoenix WinNonlin and Monolix?
When does GastroPlus fit better than mrgsolve for oral drug disposition workflows?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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