The development of this package is motivated by the water, sanitation, and hygiene (WaSH) data in which we were interested in investigating the contribution of demographic and socioeconomic factors to improved WaSH indicators among the slum dwellers in Nairobi, Kenya. We noticed that the predictions we generated using the existing packages consistently over or under estimated the observed proportions; and did not align well with the observed data points. In other words, what we call . There are several (challenges) reasons for this, including:
 the choice of the
 uncertainty estimation – the choice of for computing confidence intervals
 biases induced by nonlinear averaging due to nonlinear transformation in generalized linear models
It implements two approaches for constructing outcome plots (prediction and effect plots). These include:
 meanbased approach
 observedvalue approach
It can also be used to generate biascorrected prediction and effect estimates for generalized linear models involving nonlinear link functions, including models with random effects. This package complements the existing ones by providing:
 a straightforward way to generate effects plots
 a robust way to correct for nonlinear averaging bias in generalized (mixed) models
Installation
You can install the development version of varpred from GitHub with:
Example
We use mtcars
data to show outcome plots:

isolate=TRUE
to generate effect plot 
isolate=FALSE
to generate prediction plot
library(varpred)
library(ggplot2)
## Set theme for plots
varpredtheme()
## Fit the model
mod < lm(mpg ~ wt + hp, mtcars)
## Effect
ef < varpred(mod, "wt", isolate=TRUE, modelname="effect")
plot(ef)