data transformation SIMPLS A novel algorithm for partial least squares (PLS) regression, SIMPLS, is proposed which calculates the PLS factors directly as linear combinations of the original variables. The PLS factors are determined such as to maximize a covariance criterion, while obeying certain orthogonality and normalization restrictions. This approach follows that of other traditional multivariate methods. The construction of deflated data matrices as in the nonlinear iterative partial least squares (NIPALS)-PLS algorithm is avoided. For univariate y SIMPLS is equivalent to PLS1 and closely related to existing bidiagonalization algorithms. This follows from an analysis of PLS1 regression in terms of Krylov sequences. For multivariate Y there is a slight difference between the SIMPLS approach and NIPALS-PLS2. In practice the SIMPLS algorithm appears to be fast and easy to interpret as it does not involve a breakdown of the data sets. The acronym SIMPLS comes from 'straightforward implementation of a statistically inspired modification of the PLS method' simpls(X, Y, ncomp, stripped = FALSE, ...) https://www.rdocumentation.org/packages/cocorresp/versions/0.3-0/topics/simpls Philippe Rocca-Serra Alejandra Gonzalez-Beltran SIMPLS: An alternative approach to partial least squares regression Sijmen de Jong https://doi.org/10.1016/0169-7439(93)85002-X straightforward implementation of a statistically inspired modification of the PLS method term request from Ralf Weber and Gavin Lloyd, University of Birmingham metadata incomplete