Dev tools · alternatives · 2026

24 alternatives to New Causal Impact Library

Bayesian CausalImpact for Python — R-compatible Gibbs sampler in Rust (PyO3), 30x faster, no TensorFlow - YuminosukeSato/bsts-causalimpact

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

1
Git bayesect – Bayesian Git bisection for non-deterministic bugs▲337
Bayesian git bisect. Contribute to hauntsaninja/git_bayesect development by creating an account on GitHub.
Mar 2026 · github.com · its alternatives →
2
Comprehensive Tutorials in Deep Learning Using TensorFlow▲327
:satellite: Simple and ready-to-use tutorials for TensorFlow - GitHub - instillai/TensorFlow-Course: :satellite: Simple and ready-to-use tutorials for TensorFlow
2018 · github.com · its alternatives →
3
Fast vector similarity using Rust and Python▲141
I recently found myself computing the similarity between lots of very high dimensional vectors (i.e., sentence embedding vectors from LLMs), and I wanted to try some more powerful measures of similarity/dependency than just Cosine similarity, which seems to be the default for everything nowadays because of its computational efficiency. There are many other more involved measures that can detect more subtle relationships, but the problem is that some of them are quite slow to compute, especially if you're trying to do it in Python. For my favorite measure of statistical dependency,…
2023 · github.com · its alternatives →
4
Automated smooth Nth order derivatives of noisy data▲146
This little project came about because I kept running into the same problem: cleanly differentiating sensor data before doing analysis. There are a ton of ways to solve this problem, I've always personally been a fan of using kalman filters for the job as its easy to get the double whammy of resampling/upsampling to a fixed consistent rate and also smoothing/outlier rejection. I wrote a little numpy only bayesian filtering/smoothing library recently (https://github.com/hugohadfield/bayesfilter/) so this felt like a fun and very useful first thing to…
2024 · github.com · its alternatives →
5BA
Brancher – A user-friendly PyTorch module for probabilistic inference▲96
2019 · brancher.org · its alternatives →
6
Timber – Ollama for classical ML models, 336x faster than Python▲207
Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference. - kossisoroyce/timber
Mar 2026 · github.com · its alternatives →
7
A weekend replication of STOKE, a stochastic superoptimiser▲58
Haskell re-implementation of STOKE, the stochastic superoptimizer - blaze/notebooks/tutorial.ipynb at master · bollu/blaze
2019 · github.com · its alternatives →
8
A radically more scalable alternative to Bayesian Optimization▲96
Experimental Global Optimization Algorithm. Contribute to chrisstroemel/Simple development by creating an account on GitHub.
2018 · github.com · its alternatives →
9
Matrixprofile-ts – A Python library for timeseries motifs and discords▲95
A Python library for detecting patterns and anomalies in massive datasets using the Matrix Profile - target/matrixprofile-ts
2019 · github.com · its alternatives →
10
A Go implementation of TensorFlow's streaming quantiles estimator▲98
Optimal Quantile Approximation in Streams. Contribute to axiomhq/quantiles development by creating an account on GitHub.
2018 · github.com · its alternatives →
11
ADTK – new Python library for time series anomaly detection▲67
A Python toolkit for rule-based/unsupervised anomaly detection in time series - arundo/adtk
2019 · github.com · its alternatives →
12
I built a tensor library from scratch in C++/CUDA▲119
Hi HN, Over the past few months, I've been building `dsc`, a tensor library from scratch in C++/CUDA. My main focus has been on getting the basics right, prioritizing a clean API, simplicity, and clear observability for running small LLMs locally. The key features are: - C++ core with CUDA support written from scratch. - A familiar, PyTorch-like Python API. - Runs real models: it's complete enough to load a model like Qwen from HuggingFace and run inference on both CUDA and CPU with a single line change[1]. - Simple, built-in observability for both Python and C++. Next on the roadmap is…
2025 · github.com · its alternatives →
13
Robyn – A fast, extensible async Python web server with a Rust runtime▲103
Robyn is a Super Fast Async Python Web Framework with a Rust runtime. - sparckles/Robyn
2023 · github.com · its alternatives →
14
A SIMD-accelerated noise library in Rust▲73
SIMD noise library for Rust. Contribute to verpeteren/rust-simd-noise development by creating an account on GitHub.
2018 · github.com · its alternatives →
15
TensorFlow 1.0▲80
Open-source machine learning library by Google
2018 · tensorflow.org · its alternatives →
16PL
Python library to scan ML models for vulnerabilities▲20
Hi! I’ve been working on this automatic scanner for ML models to detect issues like underperforming data slices, overconfidence in predictions, robustness problems, and others. It supports all main Python ML frameworks (sklearn, torch, xgboost, …) and integrates with the quality assurance solution we are building at Giskard AI (https://giskard.ai) to systematically test models before putting them in production. It is still a beta and I would love to hear your feedback if you have the time to try it out. We have quite a few tutorials in the docs with ready-made colab notebooks to…
2023 · docs.giskard.ai · its alternatives →
17
NannyML Regression v0.8.0▲85
OSS Python library for detecting silent ML model failure
2022 · nannyml.com · its alternatives →
18
High-Performance Wavelet Matrix for Python, Implemented in Rust▲93
I built a Rust-powered Wavelet Matrix library for Python. There were surprisingly few practical Wavelet Matrix implementations available for Python, so I implemented one with a focus on performance, usability, and typed APIs. It supports fast rank/select, top-k, quantile, range queries, and even dynamic updates. Feedback welcome!
Dec 2025 · pypi.org · its alternatives →
19
Modular Diffusion – A modular Python library for diffusion models▲40
Hello everyone! I've been working on this project for a few months as part of my thesis in Machine Learning. It's meant to be a library that provides an easy-to-use but flexible API to design and train Diffusion Models. I decided to make it because I wanted to quickly prototype a Diffusion Model but there were no good tools to do it with. I think it really can help people prototype their own Diffusion Models a lot faster and only in a few lines of code. The base idea is to have a Model class that takes different modules corresponding to the different aspects of the Diffusion Model process…
2023 · github.com · its alternatives →
20
Model2vec-Rs – Fast Static Text Embeddings in Rust▲60
Hey HN! We’ve just open-sourced model2vec-rs, a Rust crate for loading and running Model2Vec static embedding models with zero Python dependency. This allows you to embed text at (very) high throughput; for example, in a Rust-based microservice or CLI tool. This can be used for semantic search, retrieval, RAG, or any other text embedding usecase. Main Features: - Rust-native inference: Load any Model2Vec model from Hugging Face or your local path with StaticModel::from_pretrained(...). - Tiny footprint: The crate itself is only ~1.7 mb, with embedding models between 7 and 30 mb. Performance:…
2025 · github.com · its alternatives →
21
4d-Modeller – R library to make Bayesian spatiotemporal modeling easy▲20
Hi, I've been working with other colleagues at University of Bristol for the last year to build an R-package that makes bayesian spatio-temporal modeling easy. That is, if you have some problem like predicting sea level rise over time, disease transmissions, etc., this package may be useful for you. It centers on a collection of Shiny apps that guide you through using the various pieces of spatio-temporal modeling packages like R-INLA or inlabru. Hopefully people find this useful. I think the nice part is that we really tried to design the package so you can add it to your modeling tool kit…
2023 · 4dmodeller.github.io · its alternatives →
22
R Package for Bayesian AB Testing▲38
🐢 bayesAB: Fast Bayesian Methods for A/B Testing. Contribute to FrankPortman/bayesAB development by creating an account on GitHub.
2017 · github.com · its alternatives →
23
Spanda – Sub-microsecond LLM epistemic uncertainty in Rust▲13
Zero-cost epistemic uncertainty quantification & hallucination detection for LLMs (90,000x faster than Semantic Entropy) - Adarshent/Spnda
Sep 2026 · github.com · its alternatives →
24
LeanRL: Fast PyTorch RL with Torch.compile and CUDA Graphs▲53
We're excited to announce that we've open-sourced LeanRL, a lightweight PyTorch reinforcement learning library that provides recipes for fast RL training using torch.compile and CUDA graphs. By leveraging these tools, we've achieved significant speed-ups compared to the original CleanRL implementations - up to 6x faster! Reinforcement learning is notoriously CPU-bound due to the high frequency of small CPU operations. PyTorch's powerful compiler can help alleviate these issues, but comes with its own costs. LeanRL addresses this challenge by providing simple recipes to accelerate your…
2024 · github.com · its alternatives →

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