This package contains course materials from Yale BIS557 Computational Statistics (2020 Fall). The goal is to design and implement algorithms for statistical analyses, including regression models (ridge/lasso/multi-logistic), cross-validation, stochastic gradient descent, neural networks, etc. R and Python code are designed for the implementation.
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("yijunyang/bis557")
This is a basic example which shows you how to solve a numerically-stable ridge regression that takes into account colinear regression variables:
# library(bis557)
data("iris")
# create a colinear term
iris$colinear <- 2*iris$Petal.Width + 0.1
# claim formula and dataset
form <- Sepal.Length ~ .
d <- iris
# make model matrices
mms <- make_model_matrices(form, d, contrasts = NULL)
X <- mms$X
Y <- mms$Y
# implement ridge regression
ridge_regression(form, d, lambda = 10)
# run the ridge regression function with cross validation
cv_ridge(form, d, lambda = seq(0, 0.05 ,0.001))