Add-on package for the R package `mboost`

to calculate p-values and confidence intevals for model parameters. Package currently supports models fitted with Gaussian family including linear, group and spline base-learners.

```
# library("devtools")
# install_github("davidruegamer/iboost")
library("iboost")
library("mboost")
set.seed(0)
n <- 200
x1 <- rnorm(n)
x2 <- rnorm(n) + 0.25 * x1
x3 <- rnorm(n)
eta <- 3 * sin(x1) + x2^2
y <- scale(eta + rnorm(n), scale = FALSE)
spline1 <- bbs(x1, knots = 20, df = 4)
knots.x2 <- quantile(x2, c(0.25, 0.5, 0.75))
spline2 <- bbs(x2, knots = knots.x2, df = 4)
spline3 <- bbs(x3, knots = 20, df = 4)
data <- data.frame(y=y, x1=x1, x2=x2, x3=x3)
mod1 <- mboost(y ~ spline1 + spline2 + spline3,
control=boost_control(mstop = 73), offset = 0,
data = data)
# calculate p-values and intervals for model with
# fixed stopping iteration:
# this is done with only B = 100 samples for
# demonstrative purposes and should be increased
# for actual research questions
res <- iboost(mod1, method = "impsamp", B = 100)
# do the same with crossvalidation
fixFolds <- cv(weights = model.weights(mod1),
type = "kfold", B = 10)
cvr <- cvrisk(mod1, folds = fixFolds, papply = lapply)
modf <- mod1[mstop(cvr)]
# define corresponding refit function
modFun <- function(y){
mod <- mboost_fit(response = y,
blg = blList,
offset = 0,
control = boost_control(mstop = 73))
cvr <- cvrisk(mod, folds = fixFolds, papply = lapply)
return(mod[mstop(cvr)])
}
# this will take a while
(res <- iboost(modf, refit.mboost = modFun, method = "impsamp", B = 1000))
```

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