Changes in 5.0-3 ================ Bug fixes: ---------- -the perf and predict functions have been updated. The prediction values are calculated based on the regression coefficients of Y onto the latent variables associated to X. -scaling issues in perf/old-valid have been fixed -one warning on the plotIndiv.rcc has been fixed. -transition from valid() to perf() announced. Changes in 5.0-2 ================ New features: ------------- - The valid function has been superseded by the perf function. Although similar in essence, few bugs have been fixed to estimate the performance of the sPLS and sPLS-DA models with no selection bias. A variable stability frequency has been added to the output. Functions spls.model and pls.model have been removed. Bug fixes: ---------- -pls and spls function have been modified and harmonised w.r.t to scaling. Loading vectors a and b are now scaled to 1. Latent variables t and u are not scaled (following Table 21 of the Tenenhaus book - which is in French, sorry!). -the argument abline.line has been set to FALSE by default in all plotIndiv functions. - tune.multilevel for one factor has been fixed. Changes in 5.0-1 ================ New features: ------------- New dependency to RGCCA package to enable integration of multiple matching data sets - wrapping method wrapper.sgcca() and wrapper.rgcca() created - S3 methods plotIndiv, plotVar, select.var, print for rgcca and sgcca added Multilevel analysis - cross validation enabled in function tune.multilevel for one factor (previously, only loocv was available) RCC -the function estim.regul has been renamed tune.rcc -the function pcatune has been renamed tune.pca Bug fixes: ---------- -in plotIndiv: horizontal and vertical abline set as a default argument -a new argument in splsda() function added: near.zero.var = TRUE or FALSE to speed up computations (near.zero.var = FALSE to gain speed) -the valid() function has been updated to speed up the computations. There is no 'criterion' argument to choose anymore (by default, all are included in the computation) -in plotVar: matching arguments user-function to avoid additions of unused arguments -in plotIndiv, arguments 'x.label' and 'y.label' were replaced by 'X.label' and 'Y.label' -in pca, argument 'scale.' was changed to 'scale' Changes in 4.1 ================ New features: ------------- - New S3 method valid for objects of class psl, spls, plsda and splsda - New select.var function to directly extract the selected variables from spls, spca, sipca - New data set vac18 for multilevel data Changes in 4.0 ================ New features: ------------- - The multilevel methodology has been added as well as the associated S3 methods for the graphical outputs (plotVar, plotIndiv) - pheatmap clustering is available for multilevel analysis (borrowed from the pheatmap package) - tuning functions are available for multilevel analyses - a dependency to the package 'igraph0' has been created (instead of 'igraph' as the authors informed us of major changes in this package) Bug fixes: ---------- -pls and spls have been modified to better handle NA values Changes in 3.0 ================ New features: ------------- - The new methodology IPCA and sIPCA have been added as well as the associated S3 methods for the graphical outputs - GeneBank IDs and gene titles were added in the liver toxicity study Changes in 2.9-6 ================ New features: ------------- - Modifying the valid function: the Q2 criterion has been implemented - var.label argument is used in plotVar.plsda, plotVar.splsda, plot3dVar.plsda, plot3dVar.splsda instead of X.label - New S3 method network for pls - New code for valid function to PLS-DA and sPLS-DA models validation - New code for plot.valid to display the results of the valid function for PLS-DA and sPLS-DA models - cim and network were modified to obtain the simMat matrix as value - plotVar was modified to obtain the coordinates for X and Y variables as value - In predict function, several or all prediction methods are available simultaneously to predict the classes of test data with plsda and splsda - The argument 'mode' has been removed of plsda and splsda functions Changes in 2.9-5 ================ New features: ------------- - sPCA has been modified to get orthogonal principal components Changes in 2.9-4 ================ New features: ------------- - PCA has been modified to run either SVD (no missing values) or NIPALS (missing values) - print.pca has been added to display the results of PCA - pcatune has been added to guide the choice of the number of principal components Changes in 2.9-1 ================ New features: ------------- - New S3 methods plotIndiv and plotVar for PCA - New S3 method plot.valid to display the results of the valid function - New code for imgCor function for a nicer representation of the correlation matrix - In predict.plsda and predict.splsda functions the argument 'method' were replaced by method = c("max.dist", "class.dist", "centroids.dist", "mahalanobis.dist") - New arguments for the cim function: * dendrogram * ColSideColors, RowSideColors - Modifying the valid function: * missing data are allowed * Q2 criterion has been removed - Functions pls, plsda, spls and splsda were modified to identify zero- or near-zero variance predictors - Functions plotVar.plsda, plotVar.splsda, plot3dVar.plsda, plot3dVar.splsda were modified to represent only the X variables - New function: 'nearZeroVar' for identification of zero- or near-zero variance predictors Changes in 2.8-1 ================ New features: ------------- - New arguments ("axis.labelX", "axis.labelY") in the function imgCor, to indicate if the labels of axis have to be shown or not - New classes splsda and plsda for predict, print, plotIndiv, plot3dIndiv, plotVar, plot3dVar - Several prediction functions are avaiable to predict the classes of test data with plsda and splsda see predict (argument 'method' ("class.dist", "centroids.dist", "Sr.dist", "max.dist")) - New functions map & unmap borrowed from the mclust package Bug fixes: ---------- Changes in 2.7-1 ================ New features: ------------- - New functions pca, plsda and splsda, as well as extensions of plot3dVar and plot3dIndiv for pca - New network.default function which is called by network.rcc and network.spls - bin.color function added in network.default to color edges w.r.t. the values in the simMat matrix - nipals has been improved to be computationally more efficient - Missing values are treated as in Tenenhaus in pls, spls and valid functions - New argument 'ncomp' in rcc function, argument 'ncomp' has been removed from 'summary' and 'rcc' - New option ("XY-variate") for the argument 'rep.space' in the 'plot3dVar' Bug fixes: ---------- - 'tick marks' values have been corrected for color key in cim - Computation of the simMat matrix for pls and spls - canonical mode, and correction in plotVar, plot3dVar, cim and network - Correction of the default argument 'rep.space = "XY-variate"' in plotIndiv and plot3dIndiv - Correction of the manual Changes in 2.6-0 ================ New features: ------------- - Former R package integrOmics has been renamed mixOmics - In functions plotIndiv, plotVar, cim, network the arguments 'dim1', 'dim2', 'ncomp' were replaced by 'comp', a vector of length 2 (by default 'comp = 1:2') - Network has a new argument 'alpha' User-visible changes: --------------------- Bug fixes: ---------- Internal changes: -----------------