R package for DoE with a single factor? Is there a package for R available to construct an experiment design with only a single factor?
To be more precise, I want to evaluate the dependency of voltage on the temperature. The latter shall be randomized and replicated.
On this occasion I'm wondering whether to consider blocking as well as the entire experiment will take about two weeks since the temperature differences are large and the system has to settle down each time.
 A: I will try to answer you, though it is somewhat vague at this point what you are looking for.
It seems that you already have a setup to gather the data you want.
The basic way I would try to analyze the data you would have would generally be regressions (lm or glm, as appropriate) or aov analyses. In the simplest formulation, you would input the formula as voltage ~ temperature in input the appropriately labeled data.
As for blocking, which you specifically mentioned, I would handle that by adding other variables to your formula. It seems specifically that you are concerned about a time element as well. You could create an ordered categorical variable for when each measurement happened (or group them more), put the time elapsed in as a numeric variable, or use one of R's built-in time-series-regression tools, such as ts and arima. lubridate might be a helpful package for some of this, but so would all of the tidyverse.
Some packages that would useful if you are looking into time series include zoo, (maybe) plm, xts, and tsibble, though depending on the specifics that you want, you might want to look here.
Of course, you did ask for specifically experimental design packages. Since I am still not sure what you are looking for those packages to do, I would recommend ones that would go relatively well with the approach I outlined above, which would be MOTE and its dependencies, and GAD. I could elaborate more with more detail.
