# Bjrn-Helge Mevik

#### 5 packages on CRAN

Implements the LS-PLS (least squares - partial least squares) method described in for instance J<c3><b8>rgensen, K., Segtnan, V. H., Thyholt, K., N<c3><a6>s, T. (2004) "A Comparison of Methods for Analysing Regression Models with Both Spectral and Designed Variables" Journal of Chemometrics, 18(10), 451--464, <doi:10.1002/cem.890>.

Multivariate regression methods Partial Least Squares Regression (PLSR), Principal Component Regression (PCR) and Canonical Powered Partial Least Squares (CPPLS).

Functions used by other packages from Statistics Norway are gathered. General data manipulation functions, and functions for hierarchical computations are included. The hierarchy specification functions are useful within statistical disclosure control.

General linear modeling with multiple responses (MANCOVA). An overall p-value for each model term is calculated by the 50-50 MANOVA method by Langsrud (2002) <doi:10.1111/1467-9884.00320>, which handles collinear responses. Rotation testing, described by Langsrud (2005) <doi:10.1007/s11222-005-4789-5>, is used to compute adjusted single response p-values according to familywise error rates and false discovery rates (FDR). The approach to FDR is described in the appendix of Moen et al. (2005) <doi:10.1128/AEM.71.4.2086-2094.2005>. Unbalanced designs are handled by Type II sums of squares as argued in Langsrud (2003) <doi:10.1023/A:1023260610025>. Furthermore, the Type II philosophy is extended to continuous design variables as described in Langsrud et al. (2007) <doi:10.1080/02664760701594246>. This means that the method is invariant to scale changes and that common pitfalls are avoided.

Collection of baseline correction algorithms, along with a framework and a GUI for optimising baseline algorithm parameters. Typical use of the package is for removing background effects from spectra originating from various types of spectroscopy and spectrometry, possibly optimizing this with regard to regression or classification results. Correction methods include polynomial fitting, weighted local smoothers and many more.