This release is mostly about preprocessing - added some new methods, improved the existent once and implemented a possibility to combine preprocessing methods together (including parameter values) and apply them all together in a correct sequence. See preprocessing section in the tutorials for details
New features and improvements
prep.norm()for normalization of spectra (or any other signals) is more versatile now and supports normalization to unit sum, length, area, to height or area under internal standard peak, and SNV. SNV via
prop.snv()is still supported for compatibility.
prep.savgol()has been rewritten to fix a minor bug when first derivative was inverted, but also to make the handling of the edge points better. See …
added possibility for providing partially known contributions (parameter
cont.forced) or spectral values (parameter
mcrals(). See more in help text and user guide for the package.
added possibility to run iPLS using test set (parameters
y.test) instead of cross-validation.
added a possibility to provide user defined indices of the purest variables in
mcrpure()instead of detecting them automatically.
fixed bug #98, which caused a drop of row names when data frame was used as a data source for PCA/SIMCA.
fixed bug #99, which did not allow to use user defined indices of pure variables in
- added Procrustes Cross-Validation method,
pcv()(it is also available as a separate project).
- added Kubelka-Munk transformation for diffuse reflectance spectra (
- fixed bug #94 which caused wrong limits in PCA distance plot when outliers are present but excluded.
- fixed bug #95 which lead to issues when PLS regression methods (e.g.
plotRMSE()) are used for PLS-DA model object.
- added additional check that parameter
cgroupfor plotting functions is provided as a vector or as a factor to avoid confusion.
- added link to YouTube channel with Chemometric course based on mdatools package.
Fixed bug #85 when using y-values as data frame gave an error in PLS regression
Fixed bug #86 and changed the way PLS limits maximum number of components to avoid problems with singular matrices. Now if PLS algorithm finds during calculations that provided number of components is too large, it gives a warning and reduces this number.
Code refactoring and tests for preprocessing methods