Visual editor
Latent variables, indicators, paths — drag & drop. No YAML, no code, no friction.
PLS-SEM · Open-source engine Structural equation modeling.
Structural equation modeling.
Finally modern.
The open platform for structural equation models — visual path editor, the full PLS-SEM pipeline and publication-ready reports. Right in the browser. Or self-hosted, when your data is sensitive.
No setup, no credit card · Open-source engine · Validated against established reference values
Customer Loyalty — structural model GoF = 0.51
Features
Everything you need for serious PLS-SEM.
Established statistics, modern tooling, open standards.
Complete PLS-SEM pipeline
Loadings, weights, path coefficients, R², GoF — validated against established reference values from the PLS-SEM literature.
Clean reports
PDF, XLSX, LaTeX — publication-ready. With tables, charts and a methods section you can drop straight into your paper.
Self-host for sensitive data
Docker image for teaching, clinical work and the enterprise. Your data never leaves your network.
Open export formats
Models, weights and reports in open standards (JSON, CSV, LaTeX). Migrate in and out of other PLS tools — no lock-in.
Teams & versioning
Co-authors, reviewers and version comparison for research groups. Without endless email threads.
Workflow
Four steps from data to model.
- 01
Upload data
CSV, XLSX, SPSS or Stata — we read whatever you have.
- 02
Draw the model
Constructs and paths via drag & drop. Live preview of the model structure.
- 03
Estimate
One click. plspm computes — loadings, paths, bootstrap confidences.
- 04
Export
Report as PDF, tables as XLSX, model as JSON or LaTeX snippet — all in open formats.
For whom
Concrete applications from the field.
PLS-SEM is used wherever relationships between latent, non-directly-measurable constructs need to be estimated — from PhD dissertations to the market research department of a Fortune 500 company.
Research & teaching
Empirical research without licensing hurdles
Dissertations, journal submissions and seminars — no commercial licence, no shared lab account. Open engine, documented methodology, reproducible reports.
- Acceptance models (TAM / UTAUT) for health-IT adoption
- Service quality & loyalty (ACSI / ECSI) in hospitality research
- Reputation and trust models (Corporate Reputation Model)
Marketing & CX
What really drives your customer KPIs?
Instead of correlating individual touchpoints, OpenPLS estimates the whole system simultaneously. You see which lever actually moves loyalty and NPS — and which is just noise.
- NPS and CSAT driver models from survey data
- Customer-journey impact models (Awareness → Trust → Conversion)
- Brand-equity studies with formative indices
Industry & operations
Make latent drivers of complex processes measurable
OpenPLS is particularly strong with small N and many indicators — the typical setup for employee, supplier and innovation surveys.
- Employee engagement and safety culture (safety-climate models)
- Supplier performance and supply-chain resilience
- Innovation capability (absorptive capacity, dynamic capabilities)
Health & public sector
Sensitive data, clear methodology
Self-host as a Docker container keeps patient and employee data inside your own network. Methodologically identical to the cloud version, documented for ethics committees.
- Patient-reported-outcome models in rehab & care
- Adoption of digital health offerings (DTx, telemedicine)
- Citizen-trust models for e-government initiatives
Open Source
An engine you are allowed to inspect.
Reviewers and dissertation committees want to know exactly how your numbers were produced. With OpenPLS, the answer is simple: here is the code.
Engine is Open Source
The computation engine (loadings, paths, bootstrap, fit) is open — GPL-3.0, reproducible, auditable. No vendor lock-in for your methods section.
Continuously extended
IPMA, PLSpredict, moderation, FIMIX segmentation and a PCA inner-weighting scheme are on the roadmap. Every extension lands in the engine first, then in the app.
Validated against references
A growing matrix of example models is checked against established reference values from the PLS-SEM literature — loadings, paths, R², SRMR. Per-case status is public.
Pricing
Free. Full stop.
We fund OpenPLS through consulting. The software stays free.
Cloud
Free forever
Everything you need for your research. Hosted by us.
- Unlimited models and datasets
- All reports & exports
- Visual path editor
- Version comparison
Self-host
$0 GPL-3.0
Engine as a Docker container and Python library. On your server, inside your network.
- Full engine functionality offline
- CLI and Python API
- Data sovereignty for clinic / industry
- Community support on GitHub
Consulting
On request Custom
Custom adaptations, training and methodological advice for research groups.
- Methodological coaching
- Custom models / plugins
- On-site workshops
- Priority support