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gmcmacran/README.md

Hello

Thanks for visiting my github page. My repos focus on machine learning, statistics, functional programming, puzzle solving, and notes to myself. I tend to program in either python or R. Some repos are full-fledged data science tools and are published outside of github. Others are pet projects.

Machine Learning

  • python: statsmodels provides classes and functions for the estimation of many different statistical models.
  • R: dann is an implementation of Hastie and Tibshirani’s Discriminant Adaptive Nearest Neighbor Classification.
  • R: tidydann adds the 'dann' model and the 'sub_dann' model to the Tidymodels ecosystem.
  • python: microsoftLTR trains a M.L. model that directly optimizes gain.
  • R: survivoR builds time to event models.
  • python: anomaly_detection trains multiple anomaly detection models on a simulated dataset.
  • R: extendedFamily adds new links to R’s generalized linear models.
  • python: translator translates English to Spanish with tensorflow.
  • python: glm_irls is an implementation of generalized linear models from the ground up using numpy.
  • python: coord-descent-glm is an implementation of generalized linear models using coordinate descent and functional programming.
  • python: tensorflow contains examples of
    • two versions of a generative adversarial network
    • transfer learning
    • data augmentation
    • functional A.P.I. with a residual connection
    • auto encoder
  • python: aws_docker_py is a containerized model in AWS.
  • python: semi_supervised_two explores the usefulness of semi-supervised machine learning.
  • python: feature_selection compares different feature selection methods for machine learning.

Hypothesis Testing

  • R: LRTesteR is a collection of hypothesis tests and confidence intervals based on the likelihood ratio.

Simulation Studies

  • R: TypeOneTypeTwoSim is a simulation of type I error rates, type II error rates, and coverage rates of functions in LRTesteR.
  • R: calibration studys calibration of p values from likelihood ratio tests when sample size is small.
  • R: normalTestsCompare compares power of Gaussian goodness of fit tests.
  • R: muTestsCompare compares nonparametric tests for mu.
  • R: medianTestsCompare compares nonparametric tests for the median.

Data Creation

  • R: GlmSimulatoR allows the user to easily and quickly create data for the generalized linear model.
  • python: datasets-friedman-1994 implements simulated dataset algorithms from Friedman (1994).

Functional Programming

  • R: functional_playground contains odds and ends of functional programming ideas.
  • R: altForm contains alternative formulations of statistical functions.

Puzzle Solving

  • python: backtracking solving puzzles using backtracking algorithms.
    • Sudoku puzzles
    • Knights tour problem
    • N queens problem
    • Pizza Hut's pi day challenge.

Notes

  • R: glm_notes is a collection of notes about generalized linear models.
  • python: interviewQuestions is a collection of technical programming questions I have been asked during data science interviews.
  • python: conda_environments contains conda commands for my typical conda environments.
  • pencil: proofs is a collection of math proofs.

Pinned Loading

  1. statsmodels/statsmodels statsmodels/statsmodels Public

    Statsmodels: statistical modeling and econometrics in Python

    Python 10.1k 2.9k

  2. LRTesteR LRTesteR Public

    A collection of hypothesis tests and confidence intervals based on the likelihood ratio.

    R

  3. microsoftLTR microsoftLTR Public

    Optimizing gain with LightGBM and Microsoft's 30K data.

    Python

  4. survivoR survivoR Public

    Building survival models

    R

  5. translator translator Public

    English to Spanish with tensorflow.

    Python

  6. tidydann tidydann Public

    add the 'dann' model and the 'sub_dann' model to the Tidymodels ecosystem.

    R