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Progress updates for 'lme4' functions

The progressify package allows you to easily add progress reporting to sequential and parallel map-reduce code by piping to the progressify() function. Easy!

TL;DR

library(progressify)
handlers(global = TRUE)
library(lme4)

# Fit random-slope model
fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
my_stat <- function(fit) {
  fixef(fit)
}

# Run bootstrap with progress signaling
res <- bootMer(fm1, my_stat, nsim = 1000) |> progressify()

Introduction

This vignette demonstrates how to use this approach to add progress reporting to lme4 functions such as bootMer(). The lme4 package provides functions for fitting linear, generalized linear, and nonlinear mixed-effects models. For example, bootMer() runs a statistic function nsim times:

library(lme4)

# Fit random-slope model
fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
my_stat <- function(fit) {
  fixef(fit)
}

res <- bootMer(fm1, my_stat, nsim = 1000)

By default, bootMer() provides no progress feedback. However, we can easily add progress reporting using the progressify() function:

library(lme4)
library(progressify)
handlers(global = TRUE)

# Fit random-slope model
fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
my_stat <- function(fit) {
  fixef(fit)
}

res <- bootMer(fm1, my_stat, nsim = 1000) |> progressify()

Supported Functions

The progressify() function supports the following lme4 functions:

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They may not be fully stable and should be used with caution. We make no claims about them.
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