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a-beginner-guide-to-carry-out-extreme-value-analysis-with-codes-in-python

A beginner's guide to carry out extreme value analysis, which consists of basic steps, multiple distribution fitting, confidential intervals, IDF/DDF, and a simple application of IDF information for roof drainage design. The guide mainly focuses on extreme rainfall analysis. However, the basic steps are also suitable for other climatic or hydrologic variables such as temperature, wind speed or runoff.

awesome-causality-algorithms

An index of algorithms for learning causality with data

bda_course_aalto

Bayesian Data Analysis course at Aalto

correlation-trees

gee-and-survival-analysis

gp_constr

Python model for constrained GP

hoff-bayesian-statistics

R Markdown notes for Peter D. Hoff, "A First Course in Bayesian Statistical Methods"

interpretable-machine-learning

lccm

Estimation of latent class choice models using the Expectation Maximization algorithm in addition to constraining the choice set across classes

melu

ml_model_deploymenet_flask

mob_logistic-regression-tree

numpy-100

100 numpy exercises (with solutions)

phase-i-anlysis-via-gaussian-process-regression

polyfit

Scikit learn compatible constrained and robust polynomial regression in Python

risk-adjusted-cusum

structuredinference

Structured Inference Networks for Nonlinear State Space Models

tmle3

🎯🎓 Generalized Targeted Learning Framework