A python port of the glmnet package for fitting generalized linear models via penalized maximum likelihood.

Utilities for glmnet

Accurate estimation and robust modelling of translation dynamics at codon resolution

glmnet for python

Calculating Regularized Adjusted Plus-Minus (RAPM) with R

Leap motion image recognition gesture checker GUI(C#) for developing Hand bone angles, Positions, and recognizable gestures(fuzzy logic) implementable in Virtual/Augmented reality apps.

: a R pipeline to identify the most important predictor qualitative and quantitative variables for discrimination of your variable of interest like genotype or sex or response to treatment by using phenotypic or clinical data from different diseases

MVPA tutorial - Rogers lab brain imaging unit

Norm Constrained Generalised Linear Model using numpy, numba and scipy.

glmnet compiled for MATLAB R2020, Windows 10 64-bit

Algorithmes d’apprentissage et modèles statistiques: Un exemple de régression logistique régularisée et de validation croisée pour prédire le décrochage scolaire

Survival learners for the `mlexperiments` R 📦

A tool for visualizing the coefficients of various regression models, taking into account empirical data distributions.

Advanced Regression Techniques to predict housing prices.

No description provided.

stress detection in social networks

Learners for the `mlexperiments` R 📦

The MCB for variable selection identifies two nested models (upper and lower confidence bound models) containing the true model at a given confidence level.

My role in this group project was to perform regression analysis on quarterly financial data to predict a company's market capitalization. I used R to develop ordinary least squares (OLS), stepwise, ridge, lasso, relaxed lasso, and elastic net regression models. I first used stepwise and OLS regression to develop a model and examine its residual plots. The plot displaying the residuals against the predicted values indicated multiplicative errors. I, therefore, took the natural log transformation of the dependent variable. The resulting model's R2 was significantly, negatively impacted. After examining scatter plots between the log transformation of market capitalization and the independent variables, I discovered the independent variables also had to be transformed to produce a linear relationship. Using the log transformation of both the dependent and independent variables, I developed models using all the regression techniques mentioned to strike a balance between R2 and producing a parsimonious model. All the models produced similar results, with an R2 of around .80. Since OLS is easiest to explain, had similar residual plots, and the highest R2 of all the models, it was the best model developed.

Code library for common machine learning algorithms

Sentiment analysis of TripAdvisor hotel reviews using R — combining lexicon-based methods and machine learning (TF-IDF + GLMNET)

Elastic Net, Lasso and Ridge models can be analyzed by the formula format.

Detailed exploratory and predictive analysis of Airbnb data using R for data manipulation and model building.

Comparison between the implementations of the Lasso algorithm between the Spark MLib library and the R glmnet package.

A GAUSS wrapper of the glmnet package for fitting generalized linear models via penalized maximum likelihood.

A multi-response Gaussian model capable of accurately estimating the composition of blood samples from their gene expression profiles. Fit on Affymetrix Gene ST gene expression profiles using the glmnet R package.

Development of new ML library

Compact survival analysis in R: Kaplan–Meier, log-rank, Cox PH, LASSO-Cox, time-dependent ROC. Includes clean RMarkdown report and reproducible project structure

Estimate ILI (Influenza-Like Illness) levels in Italy by looking at Wikipedia usage.

application of machine learning to indoor localization of RF sources