# What is new

23.07.2020

• section about MCR Purity method has been re-written to correspond to the algorithm changes in v. 0.11.1.

12.07.2020

• added description of new preprocessing methods prep.alsbasecorr()
• several small improvements in text

02.03.2020

• fixed a code typo in chapter about outlier detection in PLS.

01.03.2020

Small changes related to v. 0.10.1:

• added description of density plots
• added description of plotHotellingEllipse() method to PCA chapter

29.01.2020

The new version of the package (0.10.0) has a lot of changes and improvements, so it was decided to update the book to a large degree. In addition to that, chapters about iPLS and randomization tests have been added as well as information on new tools. See list of changes in the package for more details.

9.11.2018

• new minor version (0.9.2) of package is available via GitHub with several bug fixes
• added text about max.cov parameter in Preprocessing chapter.

5.07.2018

• small improvements and corrections to text of other chapters
• added a short description if new opacity parameter for mdaplot() method

3.04.2018

23.11.2017

• corrections in SIMCA part of documentation about using test set

03.08.2017

• several minor versions has been released since the last book update (0.8.1-0.8.4)
• new text describes how to use summary() for regression coefficients
• text about getRegcoeffs().pls has been updated
• some updates in plans (see section above)

30.10.2016

• fixed a bug in PCA when explained variance was calculated incorrectly for data with excluded rows
• fixed several issues with SIMCA (cross-validation) and SIMCAM (Cooman’s plot)

14.10.2016

The new version (0.8.0) brings a lot of new features, therefore it was decided to rewrite this tutorial completely and start this log from the scratch. Most of the things available in the previous version of the package will work without any changes. But if you have been using functions mdaplot() and mdaplotg() it makes sense to read how the new implementation works and rewrite your code. The use of plotting tools became much simpler and more efficient. The main changes in the package are:

• added a possibility to assign specific attributes to datasets, which makes plotting easier.
• added a possibility to exclude (hide) selected rows and columns when create a model.
• if a data frame has factor columns they will be automatically converted to a set of dummy variables.
• added several functions to make the operations with datasets containing specific attributes easier.
• plotting tools (mdaplot(), mdaplotg()) were rewritten to make the use of them easier and more efficient
• scores and loadings plots now show a percent of explained variance
• biplot is now available for PCA models (plotBiplot)
• added support for images, see a [specific chapter][Working with images] for details
• cross-validation procedures were optimized for most of the methods and now takes less time
• several bug fixes and small improvements