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- The valid function has been superseded by the perf function.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:
-----------------