emilelatour Goto Github PK

aplore3

[R package]: Datasets from "Applied Logistic Regression" by Hosmer D.W., Lemeshow S. and Sturdivant X.

awesome-r-pkgtools

A curated list of awesome tools for R package development

awtools

misc functions and aesthetic elements for my personal website

biodataintrodatavizslides

Slides for Intro to Data Viz workshop

colorblindr

csp-2018

Example code for CSP 2018 presentation

csp-2021-missing-data

Slides, code, references, materials from 2021 CSP Workshop on Missing Data and Multiple Imputation

dataviz-course-archive

Archive of lesson material from an online course at Worcester Polytechnic Institute

dotfiles

my . files

edat

edat -- Explore your data; quick plots and tables of a data set

emilelatour

Personal website of Emile Latour

emilelatour.github.io

A brazen two-column theme for Jekyll.

extracat

:exclamation: This is a read-only mirror of the CRAN R package repository. extracat — Categorical Data Analysis and Visualization

gradual-introduction-tidyverse

Workshop introducing basic and intermediate skills in tidyverse

gradual_shiny

Workshop introducing Shiny programming concepts

hart-talk_rslurm

Talk on rslurm

healthyr_preamble

A very brief intro to our coding style, mostly to introduce the pipe %>%

laaddins

Additional Addins for RStudio

lagree

Calculate a various interrater agreement coefficients

lamisc

Miscellaneous tools for data cleaning and other tasks.

latable

Latour's table functions, helpers, and wrappers.

latex-course

An interactive introduction to LaTeX using Overleaf.

laviz

Emile's visualization fun zone

leekasso

Code for comparing the top 10 predictors to the lasso/debiased lasso

logregression-workshop

In biomedical research we often wish to classify data into two or more groups (eg. healthy and diseased) based on a variety of measurement variables, but how do you determine if the model you’ve selected is good? In this BioData Club workshop instructor Crista Moreno will discuss the mathematics of logistic regression for binary classification modeling, and how to prevent the harms of overfitting with cross validation in R. Attendees will gain knowledge through hands-on exercises about the following concepts and data science skills. Logistic regression (logit function, probability, binary classification) Overfitting (adding parameters and high dimensional spaces) Cross validation Cross validation Error R (R markdown, R packages magrittr, dplyr, ggplot2, tidyr, corrplot, caret, rgl) Anyone with interest in building a classification model for biomedical data is encouraged to attend! Prior experience with R, Rstudio, and a basic knowledge of classification modeling (also mathematical functions) will be helpful, but is not a requirement.

machine-learning-with-r-datasets

Formatted datasets for Machine Learning With R by Brett Lantz

markdowntemplates

:white_check_mark::small_red_triangle_down: A collection of alternate R markdown templates

mlwr

Machine Learning with R

myquartotemplate

Quarto template with preferences, packages and yaml headers set.

ohsu-code-club

Welcome to OHSU Code Club!