General

Bootstrap from Wikipedia.
- This contains an overview of different methods for computing bootstrap confidence intervals.
- boot.ci() from the 'boot' package provides a short explanation for different methods for computing bootstrap confidence intervals.
Bootstrapping made easy and tidy with slipper
bootstrap package. "An Introduction to the Bootstrap" by B. Efron and R. Tibshirani, 1993

boot package. Functions and datasets for bootstrapping from the book Bootstrap Methods and Their Application by A. C. Davison and D. V. Hinkley (1997, CUP). A short course material can be found here.The main functions are boot() and boot.ci().

https://www.rdocumentation.org/packages/boot/versions/1.3-20

R in Action Nonparametric bootstrapping

# Compute the bootstrapped 95% confidence interval for R-squared in the linear regression
rsq <- function(data, indices, formula) {
  d <- data[indices,] # allows boot to select sample
  fit <- lm(formula, data=d)
  return(summary(fit)$r.square)
} # 'formula' is optional depends on the problem

# bootstrapping with 1000 replications
set.seed(1234)
bootobject <- boot(data=mtcars, statistic=rsq, R=1000, 
                   formula=mpg~wt+disp)
plot(bootobject) # or plot(bootobject, index = 1) if we have multiple statistics
ci <- boot.ci(bootobject, conf = .95, type=c("perc", "bca") ) 
    # default type is "all" which contains c("norm","basic", "stud", "perc", "bca"). 
    # 'bca' (Bias Corrected and Accelerated) by Efron 1987 uses 
    # percentiles but adjusted to account for bias and skewness.
# Level     Percentile            BCa          
# 95%   ( 0.6838,  0.8833 )   ( 0.6344,  0.8549 )
# Calculations and Intervals on Original Scale
# Some BCa intervals may be unstable
ci$bca[4:5]  
# [1] 0.6343589 0.8549305
# the mean is not the same
mean(c(0.6838,  0.8833 ))
# [1] 0.78355
mean(c(0.6344,  0.8549 ))
# [1] 0.74465
summary(lm(mpg~wt+disp, data = mtcars))$r.square
# [1] 0.7809306

Resampling Methods in R: The boot Package by Canty
An introduction to bootstrap with applications with R by Davison and Kuonen.
http://people.tamu.edu/~alawing/materials/ESSM689/Btutorial.pdf
http://statweb.stanford.edu/~tibs/sta305files/FoxOnBootingRegInR.pdf
http://www.stat.wisc.edu/~larget/stat302/chap3.pdf
https://www.stat.cmu.edu/~cshalizi/402/lectures/08-bootstrap/lecture-08.pdf. Variance, se, bias, confidence interval (basic, percentile), hypothesis testing, parametric & non-parametric bootstrap, bootstrapping regression models.
Understanding Bootstrap Confidence Interval Output from the R boot Package which covers the nonparametric and parametric bootstrap.

http://www.math.ntu.edu.tw/~hchen/teaching/LargeSample/references/R-bootstrap.pdf No package is used
http://web.as.uky.edu/statistics/users/pbreheny/621/F10/notes/9-21.pdf Bootstrap confidence interval
http://www-stat.wharton.upenn.edu/~stine/research/spida_2005.pdf
Optimism corrected bootstrapping (Harrell et al 1996)
- Adjusting for optimism/overfitting in measures of predictive ability using bootstrapping
- Part 1: Optimism corrected bootstrapping: a problematic method
- Part 2: Optimism corrected bootstrapping is definitely bias, further evidence
- Part 3: Two more implementations of optimism corrected bootstrapping show shocking bias
- Part 4: Why does bias occur in optimism corrected bootstrapping?
- Part 5: Code corrections to optimism corrected bootstrapping series
Bootstrapping Part 2: Calculating p-values!!! from StatQuest
Using bootstrapped sampling to assess variability in score predictions. The rsample package was used.

Nonparametric bootstrap

This is the most common bootstrap method

The upstrap Crainiceanu & Crainiceanu, Biostatistics 2018

Parametric bootstrap

Parametric bootstraps resample a known distribution function, whose parameters are estimated from your sample
http://www.math.ntu.edu.tw/~hchen/teaching/LargeSample/notes/notebootstrap.pdf#page=3 No package is used
A parametric or non-parametric bootstrap?
https://www.stat.cmu.edu/~cshalizi/402/lectures/08-bootstrap/lecture-08.pdf#page=11
simulatorZ Bioc package