version 2.2.3 on CRAN

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: resemble
 Type: Package
 Title: Memory-Based Learning in Spectral Chemometrics
-Version: 2.2.2
-Date: 2023-04-19
+Version: 2.2.3
+Date: 2024-02-16
 Author: Leonardo Ramirez-Lopez [aut, cre], 
     Antoine Stevens [aut, ctb],
     Claudio Orellano [ctb],
@@ -48,7 +48,7 @@ VignetteBuilder: knitr
 NeedsCompilation: yes
 LazyData: true
 Repository: CRAN
-RoxygenNote: 7.2.1
+RoxygenNote: 7.3.1
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-Config/VersionName: Sky
+Config/VersionName: embryo

LICENSE

@@ -1,2 +1,2 @@
-YEAR: 2014 - 2020
+YEAR: 2014 - 2024
 COPYRIGHT HOLDER: Leonardo Ramirez Lopez 

NEWS.md

@@ -1,4 +1,12 @@
-# `resemble`
+# `resemble 2.2.3 (embryo)`
+===============
+### Fixes
+
+* Fixed items in the documentation ([#50](https://github.com/l-ramirez-lopez/resemble/issues/50))
+
+* Fixed inconsistent score names between `ortho_projection()` and `ortho_projection.predict()` ([#49](https://github.com/l-ramirez-lopez/resemble/issues/49)). Many thanks to José Lucas Safanelli (zecojls) for reporting this issue. 
+
+
 `resemble 2.2.2 (sky)`
 ===============
 ### New features
@@ -8,11 +16,11 @@ avoid observations in the neighbrhoods.
 
 ### Fixes
 * A sanity check was added to `cor_diss()`. Correlation coefficients for 
-observations with an standard deviation of 0 cannot be computed ([#38]https://github.com/l-ramirez-lopez/resemble/issues/38).
+observations with an standard deviation of 0 cannot be computed ([#38](https://github.com/l-ramirez-lopez/resemble/issues/38)).
 
 * The `pls_projection()` did not work for more than one 
 response variable and with the methods `'cumvar'` or `'var'` for the selection 
-of the components. This has been fixed ([#37]https://github.com/l-ramirez-lopez/resemble/issues/37).
+of the components. This has been fixed ([#37](https://github.com/l-ramirez-lopez/resemble/issues/37)).
 
 
 `resemble 2.2.1 (Fix-Hodges)`

R/RcppExports.R

@@ -245,21 +245,21 @@ get_local_pls_weights <- function(projection_mat, xloadings, coefficients, new_x
 #' method. Only used if \code{algorithm = 'xls'}. Default is 15 (as in BUCHI NIRWise PLUS software).
 #' @return a list containing the following elements:
 #' \itemize{
-#' \item{\code{coefficients}}{ the matrix of regression coefficients.}
-#' \item{\code{bo}}{ a matrix of one row containing the intercepts for 
+#' \item{\code{coefficients}: the matrix of regression coefficients.}
+#' \item{\code{bo}: a matrix of one row containing the intercepts for 
 #' each component.}
-#' \item{\code{scores}}{ the matrix of scores.}
-#' \item{\code{X_loadings}}{ the matrix of X loadings.}
-#' \item{\code{Y_loadings}}{ the matrix of Y loadings.}
-#' \item{\code{projection_mat}}{ the projection matrix.}
-#' \item{\code{Y}}{ the \code{Y} input.}
-#' \item{\code{variance}}{ a \code{list} conating two objects: \code{x_var} 
+#' \item{\code{scores}: the matrix of scores.}
+#' \item{\code{X_loadings}: the matrix of X loadings.}
+#' \item{\code{Y_loadings}: the matrix of Y loadings.}
+#' \item{\code{projection_mat}: the projection matrix.}
+#' \item{\code{Y}: the \code{Y} input.}
+#' \item{\code{variance}: a \code{list} conating two objects: \code{x_var} 
 #' and \code{y_var}. 
 #' These objects contain information on the explained variance for the \code{X} 
 #' and \code{Y} matrices respectively.}
-#' \item{\code{transf}}{ a \code{list} conating two objects: \code{Xcenter} 
+#' \item{\code{transf}: a \code{list} conating two objects: \code{Xcenter} 
 #' and \code{Xscale}}. 
-#' \item{\code{weights}}{ the matrix of wheights.}
+#' \item{\code{weights}: the matrix of wheights.}
 #' }
 #' @author Leonardo Ramirez-Lopez
 #' @keywords internal 
@@ -300,19 +300,20 @@ opls_for_projection <- function(X, Y, ncomp, scale, maxiter, tol, pcSelmethod =
 #' method. Only used if \code{algorithm = 'xls'}. Default is 15 (as in BUCHI NIRWise PLUS software).
 #' @return a list containing the following elements:
 #' \itemize{
-#' \item{\code{ncomp}}{ the number of components used.}
-#' \item{\code{coefficients}}{ the matrix of regression coefficients.}
-#' \item{\code{bo}}{ a matrix of one row containing the intercepts for each component.}
-#' \item{\code{scores}}{ the matrix of scores.}
-#' \item{\code{X_loadings}}{ the matrix of X loadings.}
-#' \item{\code{Y_loadings}}{ the matrix of Y loadings.}
-#' \item{\code{vip}}{ the projection matrix.}
-#' \item{\code{selectivity_ratio}}{ the matrix of selectivity ratio (see Rajalahti, Tarja, et al. 2009).}
-#' \item{\code{Y}}{ the \code{Y} input.}
-#' \item{\code{variance}}{ a \code{list} conating two objects: \code{x_var} and \code{y_var}. 
+#' \item{\code{ncomp}: the number of components used.}
+#' \item{\code{coefficients}: the matrix of regression coefficients.}
+#' \item{\code{bo}: a matrix of one row containing the intercepts for each component.}
+#' \item{\code{scores}: the matrix of scores.}
+#' \item{\code{X_loadings}: the matrix of X loadings.}
+#' \item{\code{Y_loadings}: the matrix of Y loadings.}
+#' \item{\code{vip}: the projection matrix.}
+#' \item{\code{selectivity_ratio}: the matrix of selectivity ratio (see Rajalahti, Tarja, et al. 2009).}
+#' \item{\code{Y}: the \code{Y} input.}
+#' \item{\code{variance}: a \code{list} conating two objects: \code{x_var} and \code{y_var}. 
 #' These objects contain information on the explained variance for the \code{X} and \code{Y} matrices respectively.}
-#' \item{\code{transf}}{ a \code{list} conating two objects: \code{Xcenter} and \code{Xscale}}. 
-#' \item{\code{weights}}{ the matrix of wheights.}} 
+#' \item{\code{transf}: a \code{list} conating two objects: \code{Xcenter} and \code{Xscale}}. 
+#' \item{\code{weights}: the matrix of wheights.}
+#' } 
 #' @author Leonardo Ramirez-Lopez
 #' @keywords internal 
 #' @useDynLib resemble
@@ -353,15 +354,15 @@ opls_get_all <- function(X, Y, ncomp, scale, maxiter, tol, algorithm = "pls", xl
 #' method. Only used if \code{algorithm = 'xls'}. Default is 15 (as in BUCHI NIRWise PLUS software).
 #' @return a list containing the following elements:
 #' \itemize{
-#' \item{\code{coefficients}}{ the matrix of regression coefficients.}
-#' \item{\code{bo}}{ a matrix of one row containing the intercepts for each component.}
-#' \item{\code{scores}}{ the matrix of scores.}
-#' \item{\code{X_loadings}}{ the matrix of X loadings.}
-#' \item{\code{Y_loadings}}{ the matrix of Y loadings.}
-#' \item{\code{projection_mat}}{ the projection matrix.}
-#' \item{\code{Y}}{ the \code{Y} input.}
-#' \item{\code{transf}}{ a \code{list} conating two objects: \code{Xcenter} and \code{Xscale}}. 
-#' \item{\code{weights}}{ the matrix of wheights.}} 
+#' \item{\code{coefficients}: the matrix of regression coefficients.}
+#' \item{\code{bo}: a matrix of one row containing the intercepts for each component.}
+#' \item{\code{scores}: the matrix of scores.}
+#' \item{\code{X_loadings}: the matrix of X loadings.}
+#' \item{\code{Y_loadings}: the matrix of Y loadings.}
+#' \item{\code{projection_mat}: the projection matrix.}
+#' \item{\code{Y}: the \code{Y} input.}
+#' \item{\code{transf}: a \code{list} conating two objects: \code{Xcenter} and \code{Xscale}}. 
+#' \item{\code{weights}: the matrix of wheights.}} 
 #' @author Leonardo Ramirez-Lopez
 #' @keywords internal 
 #' @useDynLib resemble
@@ -372,7 +373,7 @@ opls <- function(X, Y, ncomp, scale, maxiter, tol, algorithm = "pls", xls_min_w
 #' @title fast orthogonal scores algorithn of partial leat squares (opls)
 #' @description Computes orthogonal socres partial least squares (opls) 
 #' regressions with the NIPALS algorithm. It allows multiple response variables. 
-#' In contrast to code{opls} function, this one does not compute unnecessary 
+#' In contrast to \code{opls} function, this one does not compute unnecessary 
 #' data for (local) regression.
 #' For internal use only!
 #' @usage 
@@ -398,11 +399,11 @@ opls <- function(X, Y, ncomp, scale, maxiter, tol, algorithm = "pls", xls_min_w
 #' method. Only used if \code{algorithm = 'xls'}. Default is 15 (as in BUCHI NIRWise PLUS software).
 #' @return a list containing the following elements:
 #' \itemize{
-#' \item{\code{coefficients}}{ the matrix of regression coefficients.}
-#' \item{\code{bo}}{ a matrix of one row containing the intercepts for each component.}
-#' \item{\code{Y_loadings}}{ the matrix of Y loadings.}
-#' \item{\code{projection_mat}}{ the projection matrix.}
-#' \item{\code{transf}}{ a \code{list} conating two objects: \code{Xcenter} and \code{Xscale}}. 
+#' \item{\code{coefficients}: the matrix of regression coefficients.}
+#' \item{\code{bo}: a matrix of one row containing the intercepts for each component.}
+#' \item{\code{Y_loadings}: the matrix of Y loadings.}
+#' \item{\code{projection_mat}: the projection matrix.}
+#' \item{\code{transf}: a \code{list} conating two objects: \code{Xcenter} and \code{Xscale}}. 
 #' } 
 #' @author Leonardo Ramirez-Lopez
 #' @keywords internal 
@@ -514,18 +515,18 @@ reconstruction_error <- function(x, projection_mat, xloadings, scale, Xcenter, X
 #' @return 
 #' if \code{statistics = true} a list containing the following one-row matrices:
 #' \itemize{
-#' \item{\code{rmse_seg}}{ the RMSEs.}
-#' \item{\code{st_rmse_seg}}{ the standardized RMSEs.}
-#' \item{\code{rsq_seg}}{ the coefficients of determination.}
+#' \item{\code{rmse_seg}: the RMSEs.}
+#' \item{\code{st_rmse_seg}: the standardized RMSEs.}
+#' \item{\code{rsq_seg}: the coefficients of determination.}
 #' } 
 #' 
 #' if \code{statistics = false} a list containing the following one-row matrices:
 #' \itemize{
-#' \item{\code{predictions}}{ the predictions of each of the validation 
+#' \item{\code{predictions}: the predictions of each of the validation 
 #' segments in \code{pindices}. Each column in \code{pindices} contains the 
 #' validation indices of a segment.}
-#' \item{\code{st_rmse_seg}}{ the standardized RMSEs.}
-#' \item{\code{rsq_seg}}{ the coefficients of determination.}
+#' \item{\code{st_rmse_seg}: the standardized RMSEs.}
+#' \item{\code{rsq_seg}: the coefficients of determination.}
 #' } 
 #' 
 #' If \code{method = "wapls"}, data of the pls weights are output in this 
@@ -574,10 +575,10 @@ opls_cv_cpp <- function(X, Y, scale, method, mindices, pindices, min_component,
 #' \code{'mpls'} for modified pls (using correlation between X and Y).
 #' @return a list containing the following elements:
 #' \itemize{
-#' \item{\code{ncomp}}{ the number of components.}
-#' \item{\code{pred_response}}{ the response predictions for \code{Xu}.}
-#' \item{\code{rmse_reconstruction}}{ the rmse of the reconstruction for \code{Xu}.}
-#' \item{\code{score_dissimilarity}}{ the distance score between \code{Xr} and \code{Xu}.}} 
+#' \item{\code{ncomp}: the number of components.}
+#' \item{\code{pred_response}: the response predictions for \code{Xu}.}
+#' \item{\code{rmse_reconstruction}: the rmse of the reconstruction for \code{Xu}.}
+#' \item{\code{score_dissimilarity}: the distance score between \code{Xr} and \code{Xu}.}} 
 #' @author Leonardo Ramirez-Lopez
 #' @keywords internal 
 #' @useDynLib resemble
@@ -595,14 +596,14 @@ opls_gs <- function(Xr, Yr, Xu, ncomp, scale, response = FALSE, reconstruction =
 #' and the response variable must be scaled to zero mean and unit variance.
 #' @return a list containing the following elements:
 #' \itemize{
-#' \item{\code{b}}{ the regression coefficients.}
-#' \item{\code{Xz}}{ the (final transformed) matrix of predictor variables.}
-#' \item{\code{alpha}}{ the alpha matrix.}
-#' \item{\code{is.scaled}}{ logical indicating whether both the predictors and response variable were scaled to zero mean and unit variance.}
-#' \item{\code{Xcenter}}{ if matrix of predictors was scaled, the centering vector used for \code{X}.}
-#' \item{\code{Xscale}}{ if matrix of predictors was scaled, the scaling vector used for \code{X}.}
-#' \item{\code{Ycenter}}{ if matrix of predictors was scaled, the centering vector used for \code{Y}.}
-#' \item{\code{Yscale}}{ if matrix of predictors was scaled, the scaling vector used for \code{Y}.}
+#' \item{\code{b}: the regression coefficients.}
+#' \item{\code{Xz}: the (final transformed) matrix of predictor variables.}
+#' \item{\code{alpha}: the alpha matrix.}
+#' \item{\code{is.scaled}: logical indicating whether both the predictors and response variable were scaled to zero mean and unit variance.}
+#' \item{\code{Xcenter}: if matrix of predictors was scaled, the centering vector used for \code{X}.}
+#' \item{\code{Xscale}: if matrix of predictors was scaled, the scaling vector used for \code{X}.}
+#' \item{\code{Ycenter}: if matrix of predictors was scaled, the centering vector used for \code{Y}.}
+#' \item{\code{Yscale}: if matrix of predictors was scaled, the scaling vector used for \code{Y}.}
 #' }
 #' @author Leonardo Ramirez-Lopez
 #' @keywords internal 
@@ -614,7 +615,6 @@ gaussian_process <- function(X, Y, noisev = 0.001, scale = TRUE) {
 #' @title Prediction function for the \code{gaussian_process} function (Gaussian process regression with dot product covariance)
 #' @description Predicts response values based on a model generated by the \code{gaussian_process} function (Gaussian process regression with dot product covariance). For internal use only!. 
 #' @usage predict_gaussian_process(Xz, alpha, newdata, scale, Xcenter, Xscale, Ycenter, Yscale)
-#' @param b the regression coefficients. 
 #' @param newdata a matrix containing the predictor variables
 #' @param scale a logical indicating whether the matrix of predictors used to create the regression model 
 #' (in the \code{gaussian_process} function) was scaled
@@ -643,17 +643,16 @@ predict_gaussian_process <- function(Xz, alpha, newdata, scale, Xcenter, Xscale,
 #' @param pindices a matrix with \code{k} rows and \code{m} columns where \code{m} is equivalent to the number of 
 #' resampling iterations. The elements of each column indicate the indices of the observations to be used for predicting at each 
 #' iteration.
-#' @param ncomp an integer indicating the number of pls components.
 #' @param scale a logical indicating whether both the predictors 
 #' and the response variable must be scaled to zero mean and unit variance.
 #' @param statistics a logical value indicating whether the precision and 
 #' accuracy statistics are to be returned, otherwise the predictions for each 
 #' validation segment are retrieved.
 #' @return a list containing the following one-row matrices:
 #' \itemize{
-#' \item{\code{rmse.seg}}{ the RMSEs.}
-#' \item{\code{st.rmse.seg}}{ the standardized RMSEs.}
-#' \item{\code{rsq.seg}}{ the coefficients of determination.}
+#' \item{\code{rmse.seg}: the RMSEs.}
+#' \item{\code{st.rmse.seg}: the standardized RMSEs.}
+#' \item{\code{rsq.seg}: the coefficients of determination.}
 #' } 
 #' @author Leonardo Ramirez-Lopez
 #' @keywords internal 
@@ -671,7 +670,6 @@ gaussian_process_cv <- function(X, Y, mindices, pindices, noisev = 0.001, scale
 #'            pcSelmethod = "var",
 #'            pcSelvalue = 0.01)
 #' @param X a matrix of predictor variables.
-#' @param Y a matrix of either a single or multiple response variables.
 #' @param ncomp the number of pls components.
 #' @param scale logical indicating whether \code{X} must be scaled.
 #' @param maxiter maximum number of iterations.
@@ -688,10 +686,10 @@ gaussian_process_cv <- function(X, Y, mindices, pindices, noisev = 0.001, scale
 #' specifying the desired number of principal components to retain. Default is 0.01.
 #' @return a list containing the following elements:
 #' \itemize{
-#' \item{\code{pc_scores}}{ a matrix of principal component scores.}
-#' \item{\code{pc_loadings}}{ a matrix of of principal component loadings.}
-#' \item{\code{variance}}{ a matrix of the variance of the principal components.} 
-#' \item{\code{scale}}{ a \code{list} conating two objects: \code{center} and \code{scale}, which correspond to the vectors used to center and scale the input matrix.} 
+#' \item{\code{pc_scores}: a matrix of principal component scores.}
+#' \item{\code{pc_loadings}: a matrix of of principal component loadings.}
+#' \item{\code{variance}: a matrix of the variance of the principal components.} 
+#' \item{\code{scale}: a \code{list} conating two objects: \code{center} and \code{scale}, which correspond to the vectors used to center and scale the input matrix.} 
 #' } 
 #' @author Leonardo Ramirez-Lopez
 #' @keywords internal