4DModeller
4DModeller: A focused R toolkit for Bayesian spatio-temporal models, meshes and model assessment. Ranked #61 of 106 in Statistical Analysis Software by our editors (3.3/10); pricing: Free plan; best for bayesian spatio-temporal modeling specialists.
At a glance
- Editor score3.3 / 10
- PricingFree plan
- Best forBayesian spatio-temporal modeling specialists
- Free planYes
- Paid fromFree
- DeploymentBoth
- Facts checked23 Sep 2026
Where it wins
- Builds Bayesian hierarchical models with INLA and inlabru
- Includes finite-element mesh, prior and hyperparameter tools
- Pairs R workflows with Shiny apps and applied tutorials
Where it doesn't
- Focused on spatio-temporal modeling rather than general statistical analysis
- Workflows expect tabular data with coordinates, time indices and values
- Documentation is the listed support channel
Our verdict on 4DModeller
4DModeller is an open-source R package with companion Shiny apps and tutorials for building spatially distributed, temporally dependent statistical models. It is aimed at specialists who need Bayesian hierarchical modeling with INLA and inlabru, particularly when their data combines spatial coordinates, time indices and measured values. The package is available for Linux, Windows and macOS, and its workflows cover mesh construction, model setup, visualization and assessment.
Its strongest fit is the connected modeling workflow: users can build finite-element meshes, specify priors and hyperparameters, and evaluate models within the same specialized toolkit. Companion Shiny apps provide tools for mesh building and model creation, while tutorials walk through examples involving COVID-19, hydrology and ocean heat content. This makes 4DModeller more relevant to spatial and temporal modeling work than to broad statistical analysis; people seeking a general-purpose analysis environment may prefer a wider-ranging tool.
The package is openly available under GPL (>= 3), with an open-source pricing model and a free plan. It integrates with INLA, inlabru and R, and deployment is described as both desktop and cloud. Documentation is the listed support channel, so buyers should be comfortable learning from written guidance and tutorials. Choose 4DModeller when its INLA/inlabru-centered approach and mesh tools match the modeling problem; look elsewhere if the priority is general statistical software or a different support model.
Questions about 4DModeller
What does 4DModeller run on? Linux, Windows and macOS. It runs as a desktop app and in the cloud.
What does 4DModeller integrate with? 3 integrations: INLA, inlabru and R.
Who is 4DModeller built for? Solo users, small businesses, mid-market companies and enterprises (our editorial estimate).
Your product? Our editors can test 4DModeller in depth and publish a review, labelled as paid · from $149/mo →
4DModeller pricing
4DModeller fact sheet
| Free plan | Yes |
|---|---|
| Paid from | Free |
| Deployment | Both |
| Statistical tests | Not verified |
| Regression models | Not verified |
| Scripting support | Yes |
| Bayesian analysis | Yes |
| Data import formats | Tabular datasets with spatial coordinates, time indices, and values; specific file formats are not stated. |
| Deployment | Desktop, Cloud |
| Platforms | Linux, Windows, macOS |
| Support | Docs |
| Built for | Solo, Small business, Mid-market, Enterprise (editorial estimate) |
| Integrations | 3 integrations: INLA, inlabru, R |
| Pricing | Free plan |
| Website | 4dmodeller.github.io |
| Facts checked | 23 Sep 2026 |
4DModeller integrations
4DModeller lists 3 integrations on its own site.
- INLA
- inlabru
- R
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Reviewed by iTechGuides Editors · Editorial team · Updated Nov 2026
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