Merge branch 'develop'

DESCRIPTION

@@ -1,17 +1,17 @@
 Package: mdatools
+Version: 0.14.0
 Title: Multivariate Data Analysis for Chemometrics
-Version: 0.13.1
-Date: 2022-11-09
-Author: Sergey Kucheryavskiy (<https://orcid.org/0000-0002-3145-7244>)
+Date: 2023-03-28
+Authors@R: c(person("Sergey", "Kucheryavskiy", role = c("aut", "cre"), email="[email protected]", comment = c(ORCID = "0000-0002-3145-7244")))
 Maintainer: Sergey Kucheryavskiy <[email protected]>
-Description: Projection based methods for preprocessing,
-    exploring and analysis of multivariate data used in chemometrics.
-    S. Kucheryavskiy (2020) <doi: 10.1016/j.chemolab.2020.103937>.
+Description: Projection based methods for preprocessing, exploring and analysis of multivariate data used in chemometrics. S. Kucheryavskiy (2020) <doi:10.1016/j.chemolab.2020.103937>.
 Encoding: UTF-8
 License: MIT + file LICENSE
 Imports: methods, graphics, grDevices, stats, Matrix
-RoxygenNote: 7.2.1
+RoxygenNote: 7.2.3
 Suggests: testthat
 NeedsCompilation: no
 Packaged: 2019-05-24 11:03:33 UTC; svkucheryavski
 Depends: R (>= 3.5.0)
+URL: https://github.com/svkucheryavski/mdatools
+BugReports: https://github.com/svkucheryavski/mdatools/issues

NAMESPACE

@@ -173,7 +173,6 @@ export(ddrobust.param)
 export(ellipse)
 export(employ.constraint)
 export(employ.prep)
-export(eye)
 export(fprintf)
 export(getCalibrationData)
 export(getConfusionMatrix)
@@ -236,7 +235,6 @@ export(pca)
 export(pca.mvreplace)
 export(pca.run)
 export(pcares)
-export(pcv)
 export(pinv)
 export(plotBars)
 export(plotBiplot)

NEWS.md

@@ -1,3 +1,19 @@
+v. 0.14.0
+==========
+
+The changes are relatively small, but some of them can be potentially breaking, hence the version is bumped up to 0.14.0.
+
+* Procrustes cross-validation method, `pcv()`, has been recently improved and extended. It was decided to move it to a separate dedicated R package, `pcv`. Check [GitHub repo](https://github.com/svkucheryavski/pcv) for details. The documentation chapter has been updated accordingly.
+
+* Fixed a bug related to generating segment indices for Venetian blinds cross-validation for regression. In case of regression, the indices are generating by taking into account the order of the response values. There was a small bug in this implementation, now it is fixed. Remember, that you can always provide manually generated vector og segment indices as value of `cv` argument.
+
+* Made small changes in `prep.alsbasecorr()` to meet new requirements of the `Matrix` package. So if you saw warning message from this package last couple of month, this update will fix this.
+
+* fixed bug [#109](https://github.com/svkucheryavski/mdatools/issues/109)
+
+* small improvements in documentation.
+
+
 v. 0.13.1
 ==========
 

R/crossval.R

@@ -107,7 +107,8 @@ crossval <- function(cv = 1, nobj = NULL, resp = NULL) {
    }
 
    if (p$type == "ven") {
-      ind <- if (is.null(resp)) seq_len(nobj) else order(resp)
+
+      ind <- if (is.null(resp)) seq_len(nobj) else order(order(resp))
       return(matrix(rep(seq_len(p$nseg), length.out = nobj)[ind], ncol = 1))
    }
 

R/prep.R

@@ -413,15 +413,17 @@ prep.alsbasecorr <- function(data, plambda = 5, p = 0.1, max.niter = 10) {
 
    m <- ncol(data)
    baseline <- matrix(0, nrow(data), ncol(data))
+
    LDD <- Matrix::Matrix((10^plambda) * crossprod(diff(diag(m), difference = 2)), sparse = TRUE)
    w.ini <- matrix(rep(1, m))
 
    for (i in seq_len(nrow(data))) {
       y <- data[i, ]
       w <- w.ini
+
       for (j in seq_len(max.niter)) {
          W <- Matrix::Diagonal(x = as.numeric(w))
-         z <- Matrix::solve(as(W + LDD, "dgCMatrix"), w * y, sparse = TRUE)
+         z <- Matrix::solve(as(W + LDD, "generalMatrix"), w * y, sparse = TRUE)
          w.old <- w
          w <- p * (y > z) + (1 - p) * (y < z)
 

R/simcares.R

@@ -117,7 +117,7 @@ as.matrix.simcares <- function(x, ncomp = NULL, ...) {
    out[, 1:2] <- round(out[, 1:2], 2)
 
    colnames(out) <- c("Expvar", "Cumexpvar", "TP", "FP", "TN", "FN",
-      "Spec.", "Sens.", "Accuracy")
+      "Spec.", "Sens.", "Accuracy")[seq_len(ncol(out))]
 
    return(out)
 }

README.md

@@ -18,16 +18,16 @@ If you want to cite the package, please use the following: Sergey Kucheryavskiy,
 What is new
 -----------
 
-Latest release (0.13.1) is available both from GitHub and CRAN. You can see the full list of changes [here](NEWS.md). The Bookdown tutorial has been also updated and contains the description of new methods added in the last release.
+Latest release (0.14.0) is available both from GitHub and CRAN. You can see the full list of changes [here](NEWS.md). The Bookdown tutorial has been also updated and contains the description of new methods added in the last release.
 
 
 How to install
 --------------
 
-The package is available from CRAN by usual installing procedure. However, due to restrictions in CRAN politics regarding number of submissions (one in 3-4 month), mostly major releases will be published there (with 2-3 weeks delay after GitHub release as more thorough testing is needed). You can [download](https://github.com/svkucheryavski/mdatools/releases) a zip-file with source package and install it using the `install.packages` command, e.g. if the downloaded file is `mdatools_0.13.1.tar.gz` and it is located in a current working directory, just run the following:
+The package is available from CRAN by usual installing procedure. However, due to restrictions in CRAN politics regarding number of submissions (one in 3-4 month), mostly major releases will be published there (with 2-3 weeks delay after GitHub release as more thorough testing is needed). You can [download](https://github.com/svkucheryavski/mdatools/releases) a zip-file with source package and install it using the `install.packages` command, e.g. if the downloaded file is `mdatools_0.14.0.tar.gz` and it is located in a current working directory, just run the following:
 
 ```
-install.packages("mdatools_0.13.1.tar.gz")
+install.packages("mdatools_0.14.0.tar.gz")
 ```
 
 If you have `devtools` package installed, the following command will install the current developer version from the master branch of GitHub repository (do not forget to load the `devtools` package first):

man/mdatools.Rd

@@ -27,7 +27,6 @@ So far the following modelling and validation methods are implemented:
       \code{\link{ipls}} \tab Interval PLS variable.\cr
       \code{\link{mcrals}} \tab Multivariate Curve Resolution with Alternating Least Squares.\cr
       \code{\link{mcrpure}} \tab Multivariate Curve Resolution with Purity approach.\cr
-      \code{\link{pcv}} \tab Procrustes Cross Validation.\cr
 }
 
 Methods for data preprocessing: