R4DS Ch. 23

glm()

{stats}

compute generalized linear model

rpart()

{rpart}

compute recursive partitioning and regression trees

ns()

{splines}

generate basis matrix for natural cubic splines

spread_predictions()

{modelr}

add predictions and pivot wider

seq_range()

{modelr}

return sequence over the range of a vector

gam()

{mgcv}

compute generalized additive model

gather_predictions()

{modelr}

add predictions and pivot longer

loess()

{stats}

apply local polynomial regression fitting

xgboost()

{sgboost}

computer gradient boosting models

rlm()

{MASS}

compute robust linear model

glmnet()

{glmnet}

compute generalized linear model via penalized maximum likelihood

optim()

{stats}

apply optimization algorithm

add_residuals()

{modelr}

add residuals to a data frame

model_matrix()

{modelr}

construct a design matrix

add_predictions()

{modelr}

add predictions to a data frame

randomForest()

{randomForest}

compute random forest model

data_grid()

{modelr}

generate a data grid

geom_ref_line()

{modelr}

add reference line to plot

poly()

{stats}

compute orthogonal polynomials

The end!