BCEA
BCEA: A focused R package for evaluating healthcare interventions under uncertainty. Ranked #59 of 106 in Statistical Analysis Software by our editors (3.5/10); pricing: Free plan; best for health economists conducting Bayesian cost-effectiveness.
At a glance
- Editor score3.5 / 10
- PricingFree plan
- Best forHealth economists conducting Bayesian cost-effectiveness
- Free planYes
- Paid fromFree
- DeploymentDesktop
- Facts checked23 Sep 2026

Where it wins
- Calculates net monetary benefit from Bayesian simulation draws
- Produces cost-effectiveness planes, acceptability curves and frontiers
- Includes EVPI and EVPPI analyses with graphical reporting
Where it doesn't
- Narrowly focused on Bayesian cost-effectiveness analysis
- Requires R and compatible Bayesian model simulation outputs
- Support is through documentation and community, not 24/7 channels
Our verdict on BCEA
BCEA is an open-source R package for Bayesian cost-effectiveness analysis, designed for health economists and researchers comparing healthcare interventions. It works with simulation outputs of costs and health effects to quantify decision uncertainty. Its focus is specific: this is a specialist analysis package rather than general statistical software for a broad range of tests or regression models. Researchers already working with Bayesian model outputs are its clearest audience; users seeking a broader statistical environment should look elsewhere.
The package centers on decision-focused outputs. It calculates net monetary benefit and generates cost-effectiveness plane plots, cost-effectiveness acceptability curves (CEAC), and cost-effectiveness acceptability frontiers (CEAF). It also supports expected value of perfect information (EVPI) and expected value of partial information (EVPPI), with graphical summaries and reporting. These tools keep intervention comparisons and uncertainty analysis together, which suits health economic evaluations more directly than a general-purpose statistics package. Data inputs are R data objects and simulation outputs from Bayesian models, including JAGS, BUGS, and Stan workflows, so its fit depends on an existing R and Bayesian analysis workflow.
BCEA is distributed as an R package and is available for Windows, macOS, and Linux. It is open source and has a free plan; there are no vendor-hosted subscription plans. Documentation and community are the listed support channels, rather than 24/7 support. That makes it a practical option for solo researchers and smaller teams comfortable with R and community-led support, while organizations needing broader statistical coverage or dedicated round-the-clock assistance may prefer another product. Choose BCEA when Bayesian cost-effectiveness and value-of-information reporting are central to the work, not as a general replacement for statistical analysis software.
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BCEA pricing
BCEA fact sheet
| Free plan | Yes |
|---|---|
| Paid from | Free |
| Deployment | Desktop |
| Statistical tests | No |
| Regression models | No |
| Scripting support | Yes |
| Bayesian analysis | Yes |
| Data import formats | R data objects and simulation outputs from Bayesian models (including JAGS/BUGS/Stan workflows) |
| Deployment | Desktop |
| Platforms | Windows, macOS, Linux |
| Support | Docs, Community |
| Built for | Solo, Small business, Mid-market (editorial estimate) |
| Pricing | Free plan |
| Website | n8thangreen.github.io |
| Facts checked | 23 Sep 2026 |
Alternatives to BCEA
- RA free, programmable environment for statistical analysis, modeling, visualization, and data import.9.4
- StataA broad, scriptable desktop environment for professional and reproducible applied analysis.9.3
- IBM SPSS StatisticsA broad desktop statistics suite with testing, modeling, forecasting, reporting, and R/Python integration.9.2
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The editor score above is our own research. What this page doesn't have yet is a reader's view — what you used BCEA for, what worked and what didn't. No stars are seeded and no review is paid for; an editor reads every one before it appears.
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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