R 31

• BFV Homomorphic Encryption in R %b %e, %Y
• attention package on CRAN %b %e, %Y
• Self-Attention from Scratch in R %b %e, %Y
• sigmoid package %b %e, %Y
• Coding a Recurrent Neural Network from Scratch in R %b %e, %Y
• Handcoding a Logit Model %b %e, %Y
• Handcoding a Difference in Differences %b %e, %Y
• Handcoding a Panel Model %b %e, %Y
• Neural Network IV with Simulated Data %b %e, %Y
• Neural Network Instrumental Variables %b %e, %Y
• Hand Coding Instumental Variables %b %e, %Y
• learNN package %b %e, %Y
• Linear Model and Neural Network %b %e, %Y
• Hand Coding Hilton's Dropout %b %e, %Y
• Hand Coding Gradient Descent %b %e, %Y
• Coding a Neural Network from Scratch in R %b %e, %Y
• Hand Coding the Power of a Test %b %e, %Y
• WIOD data sets package %b %e, %Y
• The Linear Model from Scratch in R %b %e, %Y
• introducing diagonals %b %e, %Y
• plot.ly %b %e, %Y
• diagonals in a network %b %e, %Y
• gvc package on CRAN %b %e, %Y
• decompr on CRAN %b %e, %Y
• Data Science Specialisation %b %e, %Y
• Learning R and Git %b %e, %Y
• The decompr package %b %e, %Y
• ggvis, shiny, and HTML5 slides %b %e, %Y
• Hand Coding a Linear Model function %b %e, %Y
• Hand Coding Categorical Variables %b %e, %Y
• Hand Coding Instrumental Variables %b %e, %Y