Eisbrenner

Eisbrenner's Stars

• avehtari/BDA_py_demos

  Bayesian Data Analysis demos for Python

  Language:Jupyter Notebook993300

• steidani/ConTrack

  Contour Tracking of circulation anomalies (atmospheric blocking, cyclones and anticyclones) in weather and climate data

  Language:Python6014

• xgcm/xhistogram

  Fast, flexible, label-aware histograms for numpy and xarray

  Language:Python9020

• 1313e/CMasher

  Scientific colormaps for making accessible, informative and 'cmashing' plots

  Language:Python41829

• mattiasvillani/AdvBayesLearnCourse

  Course material for the PhD course in Advanced Bayesian Learning

  Language:PostScript5518

• mjskay/ggdist

  Visualizations of distributions and uncertainty

  Language:R84826

• cxli233/FriendsDontLetFriends

  Friends don't let friends make certain types of data visualization - What are they and why are they bad.

  Language:R6.3k231

• matteocourthoud/awesome-causal-inference

  A curated list of causal inference libraries, resources, and applications.

  791140

• zhaokg/Rbeast

  Bayesian Change-Point Detection and Time Series Decomposition

  Language:C23638

• bambinos/bambi

  BAyesian Model-Building Interface (Bambi) in Python.

  Language:Python1.1k121

• adamhaber/pybrms

  Pythonic interface to R's brms

  Language:Jupyter Notebook447

• gingko/client

  Gingko Writer. Tree-based writing software, written in Elm.

  Language:Elm36049

• mattiasvillani/BayesianLearningBook

  Home for the book-in-progress 'Bayesian Learning'

  Language:HTML279

• shelf-sea/water-masses

  On the origin of water masses in the northern European shelf seas from a Lagrangian perspective

  Language:Python2

• amit-sharma/causal-inference-tutorial

  Repository with code and slides for a tutorial on causal inference.

  Language:Jupyter Notebook562105

• py-why/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.

  Language:Python7k924

• StanJulia/Stan.jl

  Stan.jl illustrates the usage of the 'single method' packages, e.g. StanSample, StanOptimize, etc.

  Language:Julia21331

• mattiasvillani/BayesLearnCourse

  Bayesian Learning course at Stockholm University

  Language:PostScript14532

• jmoiron/humanize

  python humanize functions

  Language:Python1.7k150

• astroML/astroML

  Machine learning, statistics, and data mining for astronomy and astrophysics

  Language:Python1.1k308

• stan-dev/pystan

  PyStan, a Python interface to Stan, a platform for statistical modeling. Documentation: https://pystan.readthedocs.io

  Language:Python33858

• JuliaDynamics/DynamicalSystems.jl

  Award winning software library for nonlinear dynamics and nonlinear timeseries analysis

  Language:Julia84093

• JuliaDynamics/TimeseriesSurrogates.jl

  A Julia package for generating timeseries surrogates

  Language:Julia479

• SciML/Surrogates.jl

  Surrogate modeling and optimization for scientific machine learning (SciML)

  Language:Julia33069

• MakieOrg/Makie.jl

  Interactive data visualizations and plotting in Julia

  Language:Julia2.4k306

• frostming/pdm-vscode

  DEPRECATED: A PDM plugin that autogenerates workspace vscode settings for you

  Language:Python231

• papamarkou/StatsLearningByExample.jl

  Learn Julia via Examples

  Language:Jupyter Notebook8732

• JuliaDataCubes/YAXArrays.jl

  Yet Another XArray-like Julia package

  Language:Julia10118

• Yujie-W/NetcdfIO.jl

  Wrapper functions for NCDatasets.jl

  Language:Julia1

• frankenfruity/plasma-pstate

  Intel P-state and CPUFreq Manager Widget

  Language:QML8710