# Hierarchical logistic regression package in R

I'm working on a logistic regression model; the purpose of the analysis is to identify factors that influence use of an app - the DV being use/no use, and IVs being a couple of numerical and categorical variables.

I want to do a hierarchical regression and add variables step-wise, and compare how much each variable improves the model. At the moment I define multiple models, add a variable for each model, compare the models using an ANOVA and save and compare the (adjusted) R squares in a table:

model1 <- glm(mh_use ~ Q7, data = results_mod, family = "binomial")
model2 <- glm(mh_use ~ Q7 + Q6_4_TEXT, data = results_mod, family = "binomial")
model3 <- glm(mh_use ~ Q7 + Q6_4_TEXT + Q66, data = results_mod, family = "binomial")
anova(model1, model2, model3, test= "Chisq")

rtbl <- matrix(nrow = 3, ncol = 2, byrow = TRUE)


Rather than doing this manually, I was wondering if there is a package/function in R that does this for you and gives the relevant results? And is R squared the best comparison for logistic regression or should I include other measures?