Simple functions for plotting linear calibration functions and estimating standard errors for measurements according to the Handbook of Chemometrics and Qualimetrics: Part A by Massart et al. There are also functions estimating the limit of detection (LOD) and limit of quantification (LOQ). The functions work on model objects from - optionally weighted - linear regression (lm) or robust linear regression ('rlm' from the 'MASS' package).
Static documentation of this R package can be found at https://pkgdown.jrwb.de/chemCal
'inverse.predict': Do not work on the means of the calibration standards any more, as this ignores the variability of y values about the means. Thanks to Anna Burniol Figols for pointing out this issue
Use testthat for tests to simplify development. Adapt the tests using data with replicate standard measurements to work on the means in order to show the relation to 'inverse.predict' from earlier versions. Include comparisons with investr::calibrate(method = 'Wald') for unweighted regressions. Include tests with more precision to check for changes in numerical output across versions.
'lod' and 'loq': In the lists that are returned, return the list component 'y' without names, because we always only have a single element in 'y' (previously the name '1' was returned).
Convert vignette to html and explain the changes to 'inverse.predict'
Add two example dataset, one from an online course at the University of Toronto, one from Rocke and Lorenzato (1995)
Update static documentation
For a detailed list of changes to the chemCal source please consult the commit history on https://cgit.jrwb.de/chemCal