NextStat: High-Performance Statistical Inference Engine
NextStat helps research teams and technical organizations perform statistical analysis at scale. It is designed for environments requiring high-performance inference and GPU acceleration for large datasets.
At a glance
- Category
- Data & Analytics
- Best for
- Data Scientists, Quantitative Researchers, Biotech and Pharma Researchers, Actuaries, Technical Research Teams
- Pricing
- NextStat uses a dual-licensing model: AGPL-3.0 for open source usage and a Commercial License for proprietary deployments. Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
- Key use cases
- SaaS Churn Analysis, Pharmacokinetics (PK/PD), Actuarial and Insurance Modeling, Particle Physics Research, Causal Inference in Econometrics
- Integrations
- PyTorch, ROOT TTree import, Apache Arrow IPC import, Parquet import, OpenAI function calling
- Official website
- nextstat.io
NextStat is a statistical inference engine built in Rust, designed for both frequentist and Bayesian methods. It provides a high-performance core accessible via a Python API and R bindings, which may help users reduce the overhead associated with pure Python implementations.
The tool is intended for technical researchers and specialists in fields such as pharma, biotech, insurance, and particle physics. It supports various models, including survival analysis, econometrics, and population pharmacokinetics, and utilizes SIMD, CUDA, and Metal GPU acceleration for data processing.
Buyers should note that this is a technical tool intended for users comfortable with API-driven or CLI-based workflows rather than a traditional point-and-click business interface.
Key Features
A compiled core designed for high-performance execution of statistical methods.
Supports CUDA and Metal backends to accelerate NLL reduction and batch toy fitting.
Uses mmap for native reading of ROOT TTree, HS3, Arrow IPC, and Parquet files.
Provides PyTorch autograd wrappers that may help neural networks optimize discovery significance.
A Metropolis-Adjusted Microcanonical Sampler designed for hierarchical models.
Includes tool definitions compatible with OpenAI function calling, LangChain, and MCP.
Use Cases
Supports the creation of cohort retention curves, churn risk models, and causal uplift estimation using AIPW.
Supports population NLME estimation, FOCE/FOCEI, and Visual Predictive Checks for clinical pharmacology.
Supports Gamma/Tweedie GLM for pricing and extreme value theory (GEV/GPD) for reinsurance.
Processes HistFactory workspaces and calculates CLs limits via toy-based or asymptotic tests.
Supports Diff-in-Differences, Event Studies, and IV/2SLS for research purposes.
Best For
Integrations
Pricing
NextStat uses a dual-licensing model: AGPL-3.0 for open source usage and a Commercial License for proprietary deployments. Pricing was not clearly available from the provided evidence. Buyers should confirm current pricing on the vendor website.
FAQ
NextStat is used for high-performance statistical inference, including survival analysis, econometrics, and particle physics, supporting both frequentist and Bayesian methods.
It is designed for data scientists and researchers in fields like biotech, pharma, and insurance who require high-compute statistical tools.
Yes, it supports CUDA for NVIDIA GPUs and Metal for Apple Silicon to assist with NLL reduction and toy fitting.
It uses a dual-licensing model with AGPL-3.0 for open-source use and a separate commercial license for proprietary deployments.
Source category: Data & Analytics
Source subcategory: Technical Computing
