covid19model VS looper

Compare covid19model vs looper and see what are their differences.

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covid19model looper
2 2
943 235
-0.4% -
0.0 7.3
about 3 years ago about 2 months ago
Stan
MIT License -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

covid19model

Posts with mentions or reviews of covid19model. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-01-17.

looper

Posts with mentions or reviews of looper. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing covid19model and looper you can also consider the following projects:

machinelearnjs - Machine Learning library for the web and Node.

Data-science-best-resources - Carefully curated resource links for data science in one place

causalnex - A Python library that helps data scientists to infer causation rather than observing correlation.

dowhy - DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

causalglm - Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning

datascience - Curated list of Python resources for data science.

causal-learn - Causal Discovery in Python. It also includes (conditional) independence tests and score functions.

HumesGuillotine - Hume's Guillotine: Beheading the social pseudo-sciences with the Algorithmic Information Criterion for CAUSAL model selection.

awesome-causality-algorithms - An index of algorithms for learning causality with data