This is definitely not all you need

deep learning

A summary of findings regarding deep learning for tabular data.

Practical Bayes Part I

bayesian

Dealing with common model problems.

Practical Bayes Part II

bayesian

Taking a better approach and avoiding issues.

Models by Example

regression

Roll your own to understand more.

Micro-macro models

mixed models
SEM
regression
factor analysis

An analysis in the wrong direction? Predicting group level targets with lower level covariates.

Exploratory Data Analysis

exploratory data analysis

Recently, Staniak & Biecek (2019) wrote an article in the R Journal exploring several of such packages, so I thought I'd try them out for myself, and take others along with me for that ride.

Predictions with an offset

regression
GLM

Reconciling R and Stata Approaches

Exploring the Pandemic

visualization

Processing and Visualizing Covid-19 Data

Factor Analysis with the psych package

factor analysis
reliability

Making sense of the results

Convergence Problems

regression
mixed models

Lack of convergence got ya down? A plan of attack.

Categorical Effects as Random

mixed models

Exploring random slopes for categorical covariates and similar models

Mixed Models for Big Data

GAM
mixed models
big data
bayesian

Explorations of a fast penalized regression approach with mgcv

Fractional Regression

regression
GLM
mixed models
fractional regression

A quick primer regarding data between zero and one, including zero and one

Comparisons of the Unseen

SEM
factor analysis

Examining group differences across latent variables

Empirical Bayes

emprical bayes
regression
mixed models
bayesian

Revisiting an old post

Shrinkage in Mixed Effects Models

regression
mixed models

A demonstration of random effects

Mediation Models

SEM
mediation

Various package options for conducting mediation analysis

Radix

Using Radix/Distill for Scientific Publishing or a Website

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index.knit

Statistics, Data, Science

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