checkRd: (-1) FDR.Rd:23-24: Lost braces
    23 | \item \code{out.FDR}{ \code{=TRUE}. If there are significative
       |                     ^
checkRd: (-1) FDR.Rd:25: Lost braces
    25 | \item \code{pv.FDR}{ p-value for False Discovery Rate test.}
       |                    ^
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    71 | \item \code{cor}{ the value of distance correlation for each covariate.} 
       |                 ^
checkRd: (-1) LMDC.select.Rd:72: Lost braces
    72 | \item \code{maxLocal}{ index or locations of local maxima distance correlations.}
       |                      ^
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checkRd: (-1) LMDC.select.Rd:77: Lost braces in \itemize; \value handles \item{}{} directly
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checkRd: (-1) Outliers.fdata.Rd:53: Lost braces
    53 |  \item \code{outliers}{ Indexes of functional outlier.}
       |                       ^
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    54 |  \item \code{dep.out}{ Depth value of functional outlier.}      
       |                      ^
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    55 |  \item \code{dep.out}{ Iteration in which the  functional outlier is detected.}        
       |                      ^
checkRd: (-1) Outliers.fdata.Rd:56: Lost braces
    56 |  \item \code{quantile}{ Threshold for outlier detection.}  
       |                       ^
checkRd: (-1) Outliers.fdata.Rd:57: Lost braces
    57 |  \item \code{dep}{ Depth value of functional data.}
       |                  ^
checkRd: (-1) Outliers.fdata.Rd:83: Lost braces
    83 | Cuevas A, Febrero M, Fraiman R. 2006.  \emph{On the use of bootstrap for estimating functions with functional data.} Computational Statistics and Data Analysis 51: 1063{-}1074.
       |                                                                                                                                                                         ^
checkRd: (-1) Outliers.fdata.Rd:85: Lost braces
    85 | Febrero-Bande, M., Galeano, P., and Gonzalez-Manteiga, W. (2008).  \emph{Outlier detection in functional data by depth measures with application to identify abnormal NOx levels}. Environmetrics 19, 4, 331{-}345. 
       |                                                                                                                                                                                                             ^
checkRd: (-1) Outliers.fdata.Rd:87: Lost braces
    87 | Febrero-Bande, M., Galeano, P. and Gonzalez-Manteiga, W.         (2007). \emph{A functional analysis of NOx levels: location and scale estimation and outlier detection}. Computational Statistics 22, 3, 411{-}427.
       |                                                                                                                                                                                                              ^
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checkRd: (-1) accuracy.Rd:46: Lost braces
    46 | \item \code{accuracy}{ the accuracy classification score}
       |                      ^
checkRd: (-1) accuracy.Rd:47: Lost braces
    47 | \item \code{recall}, \code{sensitivity,TPrate}{ \eqn{R=TP/(TP+FN)}}
       |                                               ^
checkRd: (-1) accuracy.Rd:48: Lost braces
    48 | \item \code{precision}{ \eqn{P=TP/(TP+FP)}}
       |                       ^
checkRd: (-1) accuracy.Rd:49: Lost braces
    49 | \item \code{specificity},\code{TNrate}{ \eqn{TN/(TN+FP)}}
       |                                       ^
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    50 | \item \code{FPrate}{ \eqn{FP/(TN+FP)}}
       |                    ^
checkRd: (-1) accuracy.Rd:51: Lost braces
    51 | \item \code{FNrate}{ \eqn{FN/(TP+FN)}}
       |                    ^
checkRd: (-1) accuracy.Rd:52: Lost braces
    52 | \item \code{Fmeasure}{ \eqn{2/(1/R+1/P)}}
       |                      ^
checkRd: (-1) accuracy.Rd:53: Lost braces
    53 | \item \code{Gmean}{ \eqn{sqrt(R*TN/(TN+FP))}}
       |                   ^
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    54 | \item \code{kappa}{ the kappa index}
       |                   ^
checkRd: (-1) accuracy.Rd:55: Lost braces
    55 | \item \code{cost}{ \eqn{sum(diag(tab)/rowSums(tab)*cost)/sum(cost)}}
       |                  ^
checkRd: (-1) accuracy.Rd:56: Lost braces
    56 | \item \code{IOU}{ \eqn{TP/(TP+FN+FP)}} mean of Intersection over Union 
       |                 ^
checkRd: (-1) accuracy.Rd:57: Lost braces
    57 | \item \code{IOU4class}{ \eqn{TP/(TP+FN+FP)}} Intersection over Union by level
       |                       ^
checkRd: (-1) accuracy.Rd:61: Lost braces
    61 | \item \code{MSE}{ Mean squared error, \eqn{\frac{\sum{(ypred- yobs)^2}}{n} }{\sum (ypred- yobs)^2 /n }}
       |                 ^
checkRd: (-1) accuracy.Rd:62: Lost braces
    62 | \item \code{RMSE}{ Root mean squared error \eqn{\sqrt{\frac{\sum{(ypred- yobs)^2}}{n} }}{\sqrt(\sum (ypred- yobs)^2 /n )}}
       |                  ^
checkRd: (-1) accuracy.Rd:63: Lost braces
    63 | \item \code{MAE}{ Mean Absolute Error, \eqn{\frac{\sum |yobs - ypred|}{n}}{\sum |yobs - ypred| /n}}
       |                 ^
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    50 | \item \code{group.est} {Estimated vector groups by classified method
       |                        ^
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    52 | \item \code{misclassification} { Probability of misclassification.} 
       |                                ^
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    53 | \item \code{prob.classification} { Probability of correct classification by group level.} 
       |                                  ^
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    54 | \item  \code{dep} { Data frame with the depth of the curves for functional data (or points for multivariate data) in
       |                   ^
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    56 | \item \code{depth} { Character vector specifying the type of depth functions used.} 
       |                    ^
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    57 | \item \code{par.depth} { List of parameters for \code{depth} function.} 
       |                        ^
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    58 | \item \code{classif} { Type of classifier used.} 
       |                      ^
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    59 | \item \code{par.classif}{ List of parameters for \code{classif} procedure.}
       |                         ^
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    60 | \item \code{w}{ Optional case weights.} 
       |               ^
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    61 | \item \code{fit}{ Fitted object by \code{classif} method using the depth as covariate.}
       |                 ^
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   103 | \item \code{formula}{ formula.}
       |                     ^
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   104 | \item \code{data}{ List that containing the variables in the model.} 
       |                  ^
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   105 | \item \code{group}{ Factor of length \emph{n}} 
       |                   ^
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   106 | \item \code{group.est}{ Estimated vector groups}
       |                       ^
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   107 | \item \code{prob.classification}{ Probability of correct classification by group.}
       |                                 ^
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   108 | \item \code{prob.group}{ Matrix of predicted class probabilities. For each
       |                        ^
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   110 | \item \code{max.prob}{ Highest probability of correct classification.}
       |                      ^
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checkRd: (-1) classif.gkam.Rd:65: Lost braces
    65 | \item \code{formula}{ formula.}
       |                     ^
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    66 | \item \code{data}{ List that containing the variables in the model.} 
       |                  ^
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    67 | \item \code{group}{ Factor of length \emph{n}} 
       |                   ^
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    68 | \item \code{group.est}{ Estimated vector groups}
       |                       ^
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    69 | \item \code{prob.classification}{ Probability of correct classification by group.}
       |                                 ^
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    70 | \item \code{prob.group}{ Matrix of predicted class probabilities. For each
       |                        ^
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    72 | \item \code{max.prob}{ Highest probability of correct classification.}
       |                      ^
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    57 | \item \code{formula}{ formula.}
       |                     ^
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    58 | \item \code{data}{ List that containing the variables in the model.} 
       |                  ^
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    59 | \item \code{group}{ Factor of length \emph{n}} 
       |                   ^
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    60 | \item \code{group.est}{ Estimated vector groups}
       |                       ^
checkRd: (-1) classif.glm.Rd:61: Lost braces
    61 | \item \code{prob.classification}{ Probability of correct classification by group.}
       |                                 ^
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    62 | \item \code{prob.group}{ Matrix of predicted class probabilities. For each
       |                        ^
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    64 | \item \code{max.prob}{ Highest probability of correct classification.}
       |                      ^
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    54 | \item \code{formula}{ formula.}
       |                     ^
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    55 | \item \code{data}{ List that containing the variables in the model.} 
       |                  ^
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    56 | \item \code{group}{ Factor of length \emph{n}} 
       |                   ^
checkRd: (-1) classif.gsam.Rd:57: Lost braces
    57 | \item \code{group.est}{ Estimated vector groups}
       |                       ^
checkRd: (-1) classif.gsam.Rd:58: Lost braces
    58 | \item \code{prob.classification}{ Probability of correct classification by group.}
       |                                 ^
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    59 | \item \code{prob.group}{ Matrix of predicted class probabilities. For each
       |                        ^
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    61 | \item \code{max.prob}{ Highest probability of correct classification.}
       |                      ^
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checkRd: (-1) cond.mode.Rd:21: Lost braces
    21 | \item \code{mode.cond}{ Conditional mode.} 
       |                       ^
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    22 | \item \code{x}{ Grid of length \code{n} where the the conditional density function is evaluated.} 
       |               ^
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    23 | \item \code{f}{ The conditional density function evaluated in \code{x}.}
       |               ^
checkRd: (-1) create.fdata.basis.Rd:72: Lost braces
    72 | \item \code{basis}{ basis} 
       |                   ^
checkRd: (-1) create.fdata.basis.Rd:73: Lost braces
    73 | \item \code{x}{ if \code{TRUE} the value of the rotated data (the centred data multiplied by the basis matrix) is returned}
       |               ^
checkRd: (-1) create.fdata.basis.Rd:74: Lost braces
    74 | \item \code{mean}{ functional mean of \code{fdataobj}} 
       |                  ^
checkRd: (-1) create.fdata.basis.Rd:75: Lost braces
    75 | \item \code{df}{ degree of freedom} 
       |                ^
checkRd: (-1) create.fdata.basis.Rd:76: Lost braces
    76 | \item \code{type}{ type of basis}
       |                  ^
checkRd: (-1) dcor.xy.Rd:44: Lost braces
    44 | \item \code{method}{ description of test} 
       |                    ^
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    45 | \item \code{statistic}{ observed value of the test statistic} 
       |                       ^
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    46 | \item \code{parameter}{ degrees of freedom} 
       |                       ^
checkRd: (-1) dcor.xy.Rd:47: Lost braces
    47 | \item \code{estimate}{ bias corrected distance correlation \code{bcdcor(x,y)}} 
       |                      ^
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    48 | \item \code{p.value}{ p-value of the t-test} 
       |                     ^
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    49 | \item \code{data.name}{ description of data}
       |                       ^
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    54 | \item \code{D1}{ the distance matrix of \code{x}} 
       |                ^
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    55 | \item \code{D2}{ the distance matrix of \code{y}}
       |                ^
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   190 | 10: 419{-}440. %Febrero-Bande, M., Oviedo de la Fuente, M. (2012).
       |        ^
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    75 | \item {\code{XYRP.test}}:{ \code{FDR.pv}: p-value using FDR, \code{proj.pv}: Matrix of p-values obtained for projections.} 
       |                          ^
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    76 | \item {\code{MMD.test},\code{MMDA.test}}: {\code{stat}: Statistic, \code{p.value}: p-value, \code{thresh}: Threshold at level \code{alpha}.}
       |                                           ^
checkRd: (-1) fEqDistrib.test.Rd:77-78: Lost braces
    77 | \item {\code{fEqDistrib.test}}: { \code{result}: \code{data.frame} with columns \code{Stat} and \code{p.value}, 
       |                                 ^
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    49 | \item{pr}{code{logical}. If \code{TRUE} prints intermediate results.}
       |               ^
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    34 | \item  \code{stat}{ statistic value of test.}
       |                   ^
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    35 | \item  \code{wm}{ statistic values of bootstrap resamples.}
       |                 ^
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checkRd: (-1) fregre.basis.fr.Rd:106: Lost braces
   106 | linear differential operator; {\code{lambda.s},\code{Lfdobj.s}} for
       |                               ^
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   108 | \cr {\code{lambda.t},\code{Lfdobj.t}} for penalization of
       |     ^
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    52 | \item \code{call}{ The matched call.} 
       |                  ^
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    53 | \item \code{a.est}{ Intercept parameter estimated.} 
       |                   ^
checkRd: (-1) fregre.basis.fr.Rd:54: Lost braces
    54 | \item \code{coefficientes}{ the matrix of the coefficients.}
       |                           ^
checkRd: (-1) fregre.basis.fr.Rd:55: Lost braces
    55 | \item \code{beta.est}{ A bivariate functional data object of class \code{bifd} with the estimated parameters of \eqn{\beta(s,t)}{\beta(s,t)}.}
       |                      ^
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    56 | \item \code{fitted.values}{ Estimated response.} 
       |                           ^
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    57 | \item \code{residuals}{ \code{y} minus \code{fitted values}.} 
       |                       ^
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    58 | \item \code{y}{ Functional response.} 
       |               ^
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    59 | \item \code{x}{ Functional explanatory data.} 
       |               ^
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    60 | \item \code{lambda.s}{ A roughness penalty with respect to \code{s} .} 
       |                      ^
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    61 | \item \code{lambda.t}{ A roughness penalty with respect to \code{t}.}
       |                      ^
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    62 | \item \code{Lfdobj.s}{ A linear differential operator with respect to \code{s}.}
       |                      ^
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    63 | \item \code{Lfdobj.t}{ A linear differential operator with respect to \code{t}.}
       |                      ^
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    64 | \item \code{weights}{ Weights.} 
       |                     ^
checkRd: (-1) fregre.bootstrap.Rd:59: Lost braces
    59 | \item \code{model}{ \code{fregre.pc}, \code{fregre.pls} or \code{fregre.basis} object.} 
       |                   ^
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    60 | \item \code{beta.boot}{ functional beta estimated by the \code{nb} bootstrap regressions.} 
       |                       ^
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    61 | \item \code{norm.boot}{ norm of diferences beetween the nboot betas estimated by bootstrap and beta estimated by regression model.} 
       |                       ^
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    62 | \item \code{coefs.boot}{ matrix with the bootstrap estimated basis coefficients.} 
       |                        ^
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    63 | \item \code{kn.boot}{ vector or list of length \code{nb} with index of the basis, PC or PLS factors selected in each bootstrap
       |                     ^
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    65 | \item \code{y.pred}{ predicted response values using \code{newX} covariates.} 
       |                    ^
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    66 | \item \code{y.boot}{ matrix of bootstrap predicted response values using \code{newX} covariates.} 
       |                    ^
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    67 | \item \code{newX}{ a \code{fdata} class containing the values of the model covariates at which predictions are required (only
       |                  ^
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    52 | \item \code{result:}{ List of non-parametric estimation by covariate.}
       |                     ^
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    53 | \item \code{fitted.values:}{ Estimated scalar response.} 
       |                            ^
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    54 | \item \code{residuals:}{ \code{y} minus \code{fitted values}.} 
       |                        ^
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    55 | \item \code{effects:}{ The residual degrees of freedom.} 
       |                      ^
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    56 | \item \code{alpha:}{ Hat matrix.} 
       |                    ^
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    57 | \item \code{family:}{ Coefficient of determination.} 
       |                     ^
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    58 | \item \code{linear.predictors:}{ Residual variance.}
       |                                ^
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    59 | \item \code{deviance:}{ Scalar response.} 
       |                       ^
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    60 | \item \code{aic:}{ Functional explanatory data.}
       |                  ^
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    61 | \item \code{null.deviance:}{ Non functional explanatory data.} 
       |                            ^
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    62 | \item \code{iter}:{ Distance matrix between curves.} 
       |                   ^
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    63 | \item \code{w:}{ beta coefficient estimated}
       |                ^
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    64 | \item \code{eqrank:}{ List that containing the variables in the model.}
       |                     ^
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    65 | \item \code{prior.weights:}{ Asymmetric kernel used.} 
       |                            ^
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    66 | \item \code{y:}{ Scalar response.}
       |                ^
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    67 | \item \code{H:}{ Hat matrix, see Opsomer and Ruppert(1997) for more details.}
       |                ^
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    68 | \item \code{converged:}{ conv.}
       |                        ^
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    44 | \item \code{basis.x}{ Basis used for \code{fdata} or \code{fd} covariates.} 
       |                     ^
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    45 | \item \code{basis.b}{ Basis used for beta parameter estimation.} 
       |                     ^
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    46 | \item \code{beta.l}{ List of estimated beta parameter of functional covariates.} 
       |                    ^
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    47 | \item \code{data}{ List that containing the variables in the model.} 
       |                  ^
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    48 | \item \code{formula}{ formula.} 
       |                     ^
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    88 | \item \code{sr2:}{ Residual variance.} 
       |                  ^
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    89 | \item \code{Vp:}{ Estimated covariance matrix for the parameters.} 
       |                 ^
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    90 | \item \code{lambda:}{ A roughness penalty.}
       |                     ^
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    91 | \item \code{basis.x:}{ Basis used for \code{fdata} or \code{fd} covariates.}
       |                      ^
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    92 | \item \code{basis.b:}{ Basis used for beta parameter estimation.} 
       |                      ^
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    93 | \item \code{beta.l:}{ List of estimated beta parameter of functional covariates.} 
       |                     ^
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    94 | \item \code{data:}{ List that containing the variables in the model.} 
       |                   ^
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    95 | \item \code{formula:}{ formula used in ajusted model.} 
       |                      ^
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    96 | \item \code{formula.ini:}{ formula in call.}
       |                          ^
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    97 | \item \code{W:}{ inverse of covariance matrix} 
       |                ^
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    98 | \item \code{correlation:}{ See glsObject for the components of the fit. }
       |                          ^
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    43 | \item \code{sr2}{ Residual variance.}
       |                 ^
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    44 | \item \code{Vp}{ Estimated covariance matrix for the parameters.} 
       |                ^
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    45 | \item \code{lambda}{ A roughness penalty.} 
       |                    ^
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    46 | \item \code{basis.x}{ Basis used for \code{fdata} or \code{fd} covariates.} 
       |                     ^
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    47 | \item \code{basis.b}{ Basis used for beta parameter estimation.}
       |                     ^
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    48 | \item \code{beta.l}{ List of estimated beta parameter of functional covariates.}
       |                    ^
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    49 | \item \code{data}{ List that containing the variables in the model.}
       |                  ^
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    50 | \item \code{formula}{ formula.}
       |                     ^
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checkRd: (-1) fregre.np.cv.Rd:52: Lost braces
    52 | \item \code{call}{ The matched call.} 
       |                  ^
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    53 | \item \code{residuals}{ \code{y} minus \code{fitted values}.} 
       |                       ^
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    54 | \item \code{fitted.values}{ Estimated scalar response.} 
       |                           ^
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    55 | \item \code{df.residual}{ The residual degrees of freedom.} 
       |                         ^
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    56 | \item \code{r2}{ Coefficient of determination.} 
       |                ^
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    57 | \item \code{sr2}{ Residual variance.} 
       |                 ^
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    58 | \item \code{H}{ Hat matrix.} 
       |               ^
checkRd: (-1) fregre.np.cv.Rd:59: Lost braces
    59 | \item \code{y}{ Response.} 
       |               ^
checkRd: (-1) fregre.np.cv.Rd:60: Lost braces
    60 | \item \code{fdataobj}{ Functional explanatory data.}
       |                      ^
checkRd: (-1) fregre.np.cv.Rd:61: Lost braces
    61 | \item \code{mdist}{ Distance matrix between \code{x} and \code{newx}.} 
       |                   ^
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    62 | \item \code{Ker}{ Asymmetric kernel used.} 
       |                 ^
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    63 | \item \code{gcv}{ CV or GCV values.} 
       |                 ^
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    64 | \item \code{h.opt}{ smoothing parameter or bandwidth that minimizes CV or GCV method.} 
       |                   ^
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    65 | \item \code{h}{ Vector of smoothing parameter or bandwidth.} 
       |               ^
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    66 | \item \code{cv}{ List with the fitted values and residuals estimated by CV, without the same curve.}
       |                ^
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    41 | \item \code{call}{ The matched call of \code{\link{fregre.pc}} function.} 
       |                  ^
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    42 | \item \code{coefficients}{ A named vector of coefficients.}
       |                          ^
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    43 | \item \code{residuals}{ \code{y}-\code{fitted values}.} 
       |                       ^
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    44 | \item \code{fitted.values}{ Estimated scalar response.} 
       |                           ^
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    45 | \item \code{beta.est}{ beta coefficient estimated of class \code{fdata}}
       |                      ^
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    46 | \item \code{df.residual}{ The residual degrees of freedom. In ridge regression, \code{df(rn)} is the effective degrees of freedom.} 
       |                         ^
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    47 | \item \code{r2}{ Coefficient of determination.}
       |                ^
checkRd: (-1) fregre.pc.Rd:48: Lost braces
    48 | \item \code{sr2}{ Residual variance.} 
       |                 ^
checkRd: (-1) fregre.pc.Rd:49: Lost braces
    49 | \item \code{Vp}{ Estimated covariance matrix for the parameters.} 
       |                ^
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    50 | \item \code{H}{ Hat matrix.}
       |               ^
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    51 | \item \code{l}{ Index of principal components selected.} 
       |               ^
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    52 | \item \code{lambda}{ Amount of shrinkage.} 
       |                    ^
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    53 | \item \code{P}{ Penalty matrix.} 
       |               ^
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    54 | \item \code{fdata.comp}{ Fitted object in \code{\link{fdata2pc}} function.} 
       |                        ^
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    55 | \item \code{lm}{ \code{lm} object.}
       |                ^
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    56 | \item \code{fdataobj}{ Functional explanatory data.} 
       |                      ^
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    57 | \item \code{y}{ Scalar response.}
       |               ^
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    47 | \item \code{fregre.pc}{ Fitted regression object by the best (\code{pc.opt}) components.} 
       |                       ^
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    48 | \item \code{pc.opt}{ Index of PC components selected.} 
       |                    ^
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    49 | \item \code{MSC.min}{ Minimum Model Selection Criteria (MSC) value for the (\code{pc.opt} components.} 
       |                     ^
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    50 | \item \code{MSC}{ Minimum Model Selection Criteria (MSC) value for \code{kmax} components.}
       |                 ^
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    51 | \item \code{call}{ The matched call.} 
       |                  ^
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    52 | \item \code{fitted.values}{ Estimated scalar response.} 
       |                           ^
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    53 | \item \code{residuals}{ \code{y} minus \code{fitted values}.}
       |                       ^
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    54 | \item \code{df.residual}{ The residual degrees of freedom.} 
       |                         ^
checkRd: (-1) fregre.plm.Rd:55: Lost braces
    55 | \item \code{H}{ Hat matrix.}
       |               ^
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    56 | \item \code{r2}{ Coefficient of determination.} 
       |                ^
checkRd: (-1) fregre.plm.Rd:57: Lost braces
    57 | \item \code{sr2}{ Residual variance.}
       |                 ^
checkRd: (-1) fregre.plm.Rd:58: Lost braces
    58 | \item \code{y}{ Scalar response.} 
       |               ^
checkRd: (-1) fregre.plm.Rd:59: Lost braces
    59 | \item \code{fdataobj}{ Functional explanatory data.}
       |                      ^
checkRd: (-1) fregre.plm.Rd:60: Lost braces
    60 | \item \code{XX}{ Non functional explanatory data.} 
       |                ^
checkRd: (-1) fregre.plm.Rd:61: Lost braces
    61 | \item \code{mdist}{ Distance matrix between curves.} 
       |                   ^
checkRd: (-1) fregre.plm.Rd:62: Lost braces
    62 | \item \code{betah}{ beta coefficient estimated} 
       |                   ^
checkRd: (-1) fregre.plm.Rd:63: Lost braces
    63 | \item \code{data}{ List that containing the variables in the model.} 
       |                  ^
checkRd: (-1) fregre.plm.Rd:64: Lost braces
    64 | \item \code{Ker}{ Asymmetric kernel used.} 
       |                 ^
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    65 | \item \code{h.opt}{ Value that minimizes CV or GCV method.} 
       |                   ^
checkRd: (-1) fregre.plm.Rd:66: Lost braces
    66 | \item \code{h}{ Smoothing parameter or bandwidth.}
       |               ^
checkRd: (-1) fregre.plm.Rd:67-68: Lost braces
    67 | \item \code{data}{ List that containing the
       |                  ^
checkRd: (-1) fregre.plm.Rd:69: Lost braces
    69 | \item \code{gcv}{ GCV values.} 
       |                 ^
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    70 | \item \code{formula}{ formula.}
       |                     ^
checkRd: (-1) fregre.pls.Rd:29: Lost braces
    29 | \item \code{call}{ The matched call of \code{\link{fregre.pls}} function.} 
       |                  ^
checkRd: (-1) fregre.pls.Rd:30: Lost braces
    30 | \item \code{beta.est}{ Beta coefficient estimated of class \code{fdata}.} 
       |                      ^
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    31 | \item \code{coefficients}{ A named vector of coefficients.}
       |                          ^
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    32 | \item \code{fitted.values}{ Estimated scalar response.} 
       |                           ^
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    33 | \item \code{residuals}{\code{y}-\code{fitted values}.} 
       |                       ^
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    34 | \item \code{H}{ Hat matrix.} 
       |               ^
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    35 | \item \code{df.residual}{ The residual degrees of freedom.} 
       |                         ^
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    36 | \item \code{r2}{ Coefficient of determination.}
       |                ^
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    37 | \item \code{GCV}{ GCV criterion.} 
       |                 ^
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    38 | \item \code{sr2}{ Residual variance.} 
       |                 ^
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    39 | \item \code{l}{ Index of components to include in the model.} 
       |               ^
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    40 | \item \code{lambda}{ Amount of shrinkage.}
       |                    ^
checkRd: (-1) fregre.pls.Rd:41: Lost braces
    41 | \item \code{fdata.comp}{ Fitted object in \code{\link{fdata2pls}} function.}
       |                        ^
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    42 | \item \code{lm}{ Fitted object in \code{\link{lm}} function} 
       |                ^
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    43 | \item \code{fdataobj}{ Functional explanatory data.} 
       |                      ^
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    44 | \item \code{y}{ Scalar response.}
       |               ^
checkRd: (-1) fregre.pls.cv.Rd:42: Lost braces
    42 | \item \code{fregre.pls}{ Fitted regression object by the best (\code{pls.opt}) components.} 
       |                        ^
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    43 | \item \code{pls.opt}{ Index of PLS components' selected.} 
       |                     ^
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    44 | \item \code{MSC.min}{ Minimum Model Selection Criteria (MSC) value for
       |                     ^
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    46 | \item \code{MSC}{ Minimum Model Selection Criteria (MSC) value for \code{kmax} components.}
       |                 ^
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    17 |  \item \code{DCP}{ Cook's Distance for Prediction.}
       |                  ^
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    18 |  \item \code{DCE}{ Cook's Distance for Estimation.}
       |                  ^
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    19 |  \item \code{DP}{ \eqn{\mbox{Pe}\tilde{\mbox{n}}\mbox{a's} }{} Distance.}
       |                 ^
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    33 | \item \code{quan.cook.for}{ Distance Cook Prediction Quantile.}
       |                           ^
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    34 | \item \code{quan.cook.est}{ Distance Cook Estimation Quantile.}
       |                           ^
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    35 | \item \code{quan.cook.Pena}{ Pena Distance Quantile.}
       |                            ^
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    36 | \item \code{mues.est}{ Sample Cook generated.} 
       |                      ^
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    37 | \item \code{mues.pena}{ Sample Pena generated.} 
       |                       ^
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    38 | \item \code{beta.boot}{ Functional beta estimated by bootstrap method.}
       |                       ^
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    69 |  \item \code{cluster}{ Indexes of groups assigned.}  
       |                      ^
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    70 | \item \code{centers}{ Curves centers.}
       |                     ^
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    46 | \item \code{gcv}{ Returns GCV values calculated for input parameters.}
       |                 ^
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    47 | \item \code{fdataobj}{ Matrix of set cases with dimension (\code{n} x \code{m}),
       |                      ^
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    50 | \item \code{fdata.est}{ Estimated \code{fdata} class object.}
       |                       ^
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    51 | \item \code{numbasis.opt}{ \code{numbasis} value that minimizes CV or GCV method.}
       |                          ^
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    52 | \item \code{lambda.opt}{ \code{lambda} value that minimizes CV or GCV method.}
       |                        ^
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    53 | \item \code{basis.opt}{ \code{basis} for the minimum CV or GCV method.}
       |                       ^
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    54 | \item \code{S.opt}{ Smoothing matrix for the minimum CV or GCV method.}
       |                   ^
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    55 | \item \code{gcv.opt}{ Minimum of CV or GCV method.} 
       |                     ^
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    56 | \item \code{lambda}{ A roughness penalty. By default, no penalty \code{lambda=0}.}
       |                    ^
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    57 | \item \code{numbasis}{ Number of basis to use.} 
       |                      ^
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    58 | \item \code{verbose}{ If \code{TRUE} information about GCV values
       |                     ^
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    59 | \item \code{gcv}{ GCV or CV for a vector of values of the smoothing parameter
       |                 ^
checkRd: (-1) optim.np.Rd:61: Lost braces
    61 | \item \code{fdataobj}{ \code{\link{fdata}} class object.}
       |                      ^
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    62 | \item \code{fdata.est}{ Estimated \code{fdata} class object.} 
       |                       ^
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    63 | \item \code{h.opt}{ \code{h} value that minimizes CV or GCV method.} 
       |                   ^
checkRd: (-1) optim.np.Rd:64: Lost braces
    64 | \item \code{S.opt}{ Smoothing matrix for the minimum CV or GCV method.} 
       |                   ^
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    65 | \item \code{gcv.opt}{ Minimum of CV or GCV method.} 
       |                     ^
checkRd: (-1) optim.np.Rd:66: Lost braces
    66 | \item \code{h}{ Smoothing parameter or bandwidth.}
       |               ^
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    51 | \item  \code{fit}{ A vector of predictions or a matrix of predictions and bounds as above} 
       |                  ^
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    52 | \item  \code{se.fit}{ Associated standard error estimates of predictions} 
       |                     ^
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    53 | \item  \code{residual.scale}{ Residual standard deviations} 
       |                             ^
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    54 | \item  \code{df}{ Degrees of freedom for residual}
       |                 ^
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