Version 2.0.2:
fdata.bootstrap and fregre.bootstrap functions addapted to parallel backend.
Corrected bug in kmeans.fd() function.
Bug corrected in predict.fregre.glm(), predict.fregre.lm() and predict.gsam(), now works with type="effects".
The "main", "xlab" and "ylab" arguments can be used in the plot.bifd() function.
plot.ldata() draws each curve according to the factor indicated in the argument "var.name".
Minor changes in ldata(), "na.rm = T" is removed in the sweep function.
The documentation for the argument '...' that is not included in the "usage" was deleted.
Modification in internal function wmestadis() used in fanova.onefactor(), now it replicates the statistic defined by Cuevas, 2004.
Deleted arguments "y" and "corplot" in summary.fdata.comp() function.
Deleted dev.new() in code of summary.fdata.comp() function.


Version 2.0.1:
1) Modification in fdata() function to avoid class()== and class()!= instead use is(), 
sog in R 4.0.0, will a matrix() be an "array", e.g., 'class(diag(1))' is c("matrix", "array")' 
which invalidates code (wrongly) assuming that 'length(class(obj)) == 1'.

2) kmeans.fd function:
a) "par.ini" argument is depreciated, the user can use "method" argument. 
b) "cluster.size" argument are added.
c) New internal function predict.kmeans.fd.

3) Bug corrected in internal function pred2glm2boost(), it is used for predictions of classiff.DD outputs

4) New functions: Ops.ldata, Math.ldata, Summary.ldata, mean.ldata and mean.fdata (deprecated ldata.mean, mfdata.mean)

5) Modifications in ldata.cen

6) A bug in S.LPR() has been fixed.

7) A bug in internal function wmestadis() used in fanova.onefactor() has been fixed.




Version 2.0.0 is a major release with several new features, including:

inprod.fdata and metric.lp funcitons addapted to parallel backend.

zzz.R file includes .onAttach() function (welcome package message)

ops.fda.usc.R file includes ops.fda.usc() function that control general parameters of packages such as ncores argument.

par.fda.usc.R file is deleted: par.fda.usc is now an internal object created and modified by ops.fda.usc()

New function metric.DTW computes distances between functional data using dynamic time warping (DTW)
metric.WDTW and metric.TWED are extended version. (not parallelized yet, pending to completed the Rd document)

New functions S.LPR and S.LCR for computing smoothing matrix S by nonparametric method.

The functions anova.hetero, anova.onefactor, anova.RPm, influence.fdata, influence.quan, min.basis, min.np and unlist.fdata
are renamed fanova.hetero, fanova.onefactor, fanova.RPm, influence.fregre.fd, influence_quan, optim.basis, optim.np and unlist_fdata

optim.np (deprecated min.np)  allows Local polynomial regression with correlated errors using the new parameter (correl=TRUE) 
Kernel.correlated new functions

New class: "ldata": ldata() class definition.
Redefined metric.ldata, it computes distance for ldata object: list with m functional data "mfdata" and univariate data included in a data frame called "df"
New function metric.mfdata: compute distance for mfdata class object:  list with m functional data

plot.ldata: plots for ldata object, it allows drawing using a color bar.
plot.mfdata: plot for  mfdata object (internal function, pending to completed the Rd document)
depth.modep, depth.mode  call metric.lp and metric.ldata propperly
New functions: subset.ldata, is.lfdata "[.lfdata"  "[.ldata" is.ldata names.ldata c.ldata

classic.tree() is replaced by the classic.rpart() (which requires the rpart library to be installed). 
The internal function classif.tree2boost() and the dependency of the rpart package are also removed

New functions and utilities in accuracy.r file.  

New functions related with Machine Learning procedures (rdepend on packages not included in "fda.usc"):
classif.svm and classif.naiveBayes(e1071 pkg), classif.ksvm (personalized pkg), 
classif.rpart (rpart pkg), classif.nnet (nnet pkg), classif.multinom (nnet pkg),
classif.randomForest (randomForest)
clasiff.univariante is used in classif.DD and allow multiclass labels
classif.kfold selects the parameters using k-fold cross-validation 

Minor changes in classif.gkam and fregre.gkam

Settings in fregre.np, fregre.np.cv with type.S = S.KNN

New script file: FDA_REviewClasif_V2 classification example

Bug corrected in h.default (specially using k-nearest neighbors smoothing, type.S="S.KNN")

"type.CV" and "par.CV" arguments are removed in classif.np, classif.kernel and classif.knn

dcor.xy.r/.Rd includes Rdnames

fdata2model.R shortcut to use in classif and fregre method 


Release 1.5.0:
This version was released in Jan. 2019 to accompany Manuel Oviedo de la Fuente PhD Thesis, 
see Minerva (University of Santiago de Compostela) repository.
 
New function implemented: fregre.gsam.vs() accompany paper: 
Febrero-Bande, M., Gonz\'{a}lez-Manteiga, W. and Oviedo de la Fuente, M. 
Variable selection in functional additive regression models, (2018).  
Computational Statistics, 1-19. DOI: \url{https://doi.org/10.1007/s00180-018-0844-5}

The current function "fregre.basis.cv" returns an object called fregre.basis 
(same output as if the "fregre.basis" function had been used) that uses the selected parameters
according to the indicated criteria (see example below).  The previous function version
(up to version 1.5.0) has been renamed in the function "fregre.basis.cv.old". 
It is marked as deprecated in the current version and will be deleted in the next 
version of the package, thanks to Beatriz Bueno.

New functions: plot.fregre.lm and summary.fregre.lm solve errors in the summary of the 
in fregre.lm function, thanks to Prof. Andros Kourtellos.
A bug in fregre.pc has been fixed (thanks to Prof. Eduardo Garcia-Portugues).




This version Release 1.4.0:
as released in Dec. 2017 to accompany paper: 

Ordonez, C., Oviedo de la Fuente, M., Roca-Pardinas, J.,  Rodriguez-Perez, J. R. (2017).
 Determining optimum wavelengths for leaf water content estimation from reflectance: A 
 distance correlation approach. \emph{Chemometrics and Intelligent Laboratory Systems},
 (2018)  173,41-50  \url{https://doi.org/10.1016/j.chemolab.2017.12.001}. 
New functions implemented: LMDC.select() and LMDC.regression().

'Oviedo de la Fuente M., Febrero-Bande M., Munoz M.P., Dominguez A.
Predicting seasonal influenza transmission using Regression Models with Temporal Dependence'
arXiv:1610.08718, URL  https://arxiv.org/pdf/1610.08718.pdf' 
The functions fregre.gls and fregre.igls implement the functional linear model
with dependent errors (functional GLs and functional iterative GLS respectively).
The "fda.usc/inst/script/" folder contains the code that reproduces the results
of the paper (see the scripts whose prefix is "PLOS").

This package version ALSO companion for the paper:
"Goodness-of-fit tests for the functional linear model
based on randomly projected empirical processes" 
Cuesta-Albertos et al., 2017). The package implements 
goodness-of-fit tests for the functional linear model with scalar response.

A bug in functional derivative by raw derivation 
(function fdata.deriv() with method="diff") has been fixed, thanks to Marcos Matabuena.

A bug in classif.knn() and  predict.classif() has been fixed, thanks to Ricardo Recarey.
A bug in CV.S() function has been fixed, thanks to Miquel Carbajo.
A bug in anova.hetero has been fixed, thanks to Beatriz Bueno.


Release 1.3.0:
This version was released in Nov. 2016 as the working version to
accompany paper: 
'Oviedo de la Fuente M., Febrero-Bande M., Munoz M.P., Dominguez A.
Predicting seasonal influenza transmission using Regression Models with Temporal Dependence'
arXiv:1610.08718, URL  https://arxiv.org/pdf/1610.08718.pdf'
  
Beta version functions to Fit Functional Linear Model Using Generalized Least Squares:
"fregre.gls", "fregre.igls", "GCCV.S", "predict.fregre.gls" and "predict.fregre.igls"
Internal function "auxiliar", "corSigma", "corStruct".

The functionality of the functions "+.fdata", "-.fdata", "*.fdata"
an "/.fdata" has been improved.

S3 functions for fdata class calculations: "is.na.fdata" and "anyNA.fdata".
Function "count.na.fdata"  returns a vector with the number of "NA" of each curve.

Internal function "count.na" is deprecated.

fdata function converts "xtab" and "ftable" class object into "fdata" class object.


Release 1.2.3:
Modification of classif.DD function for DDk classifier.
New functions length.fdata, NROW.fdata, NCOL.fdata, gridfdata and rcombfdata.
depth.KFSD  function implements a depth measure based on Kernelized Functional Spatial Depth.   
depth.FSD  function implements a depth measure based on Functional Spatial Depth.   
A bug in fregre.pc() function has been fixed.
A bug in depth.RPD() function has been fixed.

Release 1.2.2:
Warning message in fregre.basis.cv(), fregre.pc.cv() and fregre.pls.cv(), fregre.basis(),
fregre.pc() and fregre.pls() functions when system is computationally singular.
A bug in predict.classif() function using a fitted object by classif.knn() has been fixed.
The argument "trim" is modified from 0.1 to 0.25 in the function quantile.outliers.trim
The internal distance functions: euclidean, manhattan, minkowski and maximum allow a vector of weights.
In classif.DD function, the  polynomial classifier ("DD1", "DD2" and "DD3") uses the original procedure
 proposed by Li et al. (2012), rotating the DD-plot (to exchange abscise and ordinate). The procedure 
 extend to multi-class problems by incorporating the method of majority voting in the case of polynomial
 classifier and the method One vs the Rest in the logistic case ("glm" and "gam").
The fregre.gkam function only considers  functional covariates (not implemented for non-functional covariates). 
In the depth.FM function it has been renamed the argument "dfunc" by "dfunc2".
subset.fdata is a wrapper function of subset function.


Release 1.2.1:
The functions dcor.xy, dcor.test(), bcdcor.dist() and dcor.dist() 
(wrapper function of energy package) are added.
fregre.gsam() function can be used without smoothed vairables.
A bug in "selec" argument of summary.fregre.gkam() has been fixed.
A bug in "h" argument of fregre.plm() has been fixed.
A bug in sigma="vexponential" of rproc2fdata() has been fixed. 
The default values depth.RPp, depth.RP and rproc2fdata() have been modified.
A classif.DD function uses
 the same bandwidth "h" for k groups in modal depth.
 the same projections "proj" for k groups in RP depth.
A bug in predict.fregre.gsam() when PLS are previously estimated using norm=TRUE has been fixed.


Release 1.2.0:
New functions, 
classif.DD, fits Nonparametric Classification Procedure Based on DD–plot (depth-versus-depth plot) for G groups.
depth.FMp, depth.modep and depth.RPp functions provide the depth measure for a list of p--functional data objects.
metric.ldata, computes distance for a list of p--functional data objects.
metric.hausdorff, computes hausdorff distance.
Multivariate depth functions have been renamed: "depth." to "mdepth.".


Release 1.1.0:
New functions, 
fregre.basis.fr fits functional response model.
metric.kl computes Kullback--Leibler distance.
anova.onefactor: tests one--way anova model for functional data.
split.fdata, unlist.fdata: A wrapper functions of the split and unlist function for functional data.
func.mean.formula computes the mean curve for the each level of grouping variable.

New dataset, Mithochondiral calcium overload (MCO) data set.

New utilities, 
fdata converts arrays of 3 dimension in a functional data of 2 dimension plot.fdata allows functional data of 2 dimension.
The functions fdata2ppc, fdata2ppls, fregre.ppc, fregre.ppls, fregre.ppc.cv, fregre.ppls.cv 
are deprecated in favor of fdata2pc,fdata2pls, fregre.pc, fregre.pc.cv, fregre.pls, fregre.pls.cv. These latter functions include penalty arguments. 


Release 1.0.5:
"pls" package dependency has been removed.
A bug in outlier.ltr() function has been fixed in the case of the rownames (of fdata) can not be converted to numeric values.
A bug in fregre.lm() function has been fixed in the case of one of the covariates is a factor and penalization argument is required (rn or lambda greater than zero). 
The new argument "lambda" in fregre.lm() penalizes the derivative of second order of the functional data.
New arguments in predict.fregre.fd() and predict.fregre.lm() produce confidence or prediction intervals at the specified level mimicking predict.lm().
New argument "verbose" in min.basis(), min.np() and rproc2fdata().
The argument "mu" in rproc2fdata() allows vector and also fdata class object.

Release 1.0.4:
A bug in fregre.pls() function has been fixed in the case of the FPLS basis are created with the argument norm is TRUE (the curves are centred and scaled). 
A bug in outliers.depth.trim() function has been fixed in the case of the procedure requires more than one iteration step.

Release 1.0.3 introduces new function classif.depth() that fits a nonparametric classification procedure based on maximum depth measure.
Penalized FPC an FPLS basis are computed in create.pc.basis and create.pls.basis by the new arguments "lambda" and "P".
A bug in predict.fregre.fd() function has been fixed (in the case of the "object" is fitted using funtional partial least square basis). 

Release 1.0.2 introduces new functions:
New argument "se.fit" in predict.fregre.fd() and predict.fregre.lm() function.
A bug in CV.S() function when "y" argument is a fdata object has been fixed. 
This "bug" has involved the min.np() function.
In metric.lp() function NA values are returned, if the fdata has NA's values. 
In metric.lp() function supremum distance is computed, if "lp" argument is 0.

Release 1.0.1 
New functions:
New depth functions and its corresponding shortcut functions (see help(Descriptive) form more details):
 depth.SD() provides the simplicial depth measure for bivariate data.
 depth.PD() provides the depth measure using random projections for multivariate data.
 depth.MhD() provides the Mahalanobis depth measure for multivariate data.
 depth.HD() provides the half-space depth measure for multivariate data.
It introduces a new functions for functional PC and PLS regression:
fregre.ppc,fregre.ppls, fregre.ppc.cv,fregre.ppls.cv,
and the auxiliary functions:  fdata2ppc,fdata2ppls,P.penalty.
The function rber.gold() has been renamed by rwild() function. 
Now, rwild() contructs the Wild bootstrap residuals. 
order.fdata() is a wrapper function of order function.

New arguments and options:
New arguments "wild" and "type.wild" in fregre.bootstrap().
In fregre.glm(), fregre.gsam(), classif.glm2boost(), classif.gsam2boost() the
"fdataobj" argument allows a multivariate data or functional data.
fregre.lm() allows penalization by "rn" parameter (ridge regression).
fregre.pc() and fregre.basis() allow weighted least squares by "weights" argument.

Correction of bugs:
A bug in "draw" argument of fdata.bootstrap() has been fixed.
A bug in predict.classif() function ussing a fitted object by classif.knn() has been fixed.
A bug in create.pc.basis() function when "l" argument has length 1 has been fixed. 
This "bug" has involved the following functions: fregre.lm(), fregre.glm() and fregre.gsam().


Release 1.0.0 was released in Oct. 2012 as the working version to
accompany 'Febrero-Bande, M. and  Oviedo de la Fuente, M. (2012).
'Statistical Computing in Functional Data Analysis: The R Package fda.usc.
Journal of Statistical Software, 51(4), 1-28., URL http://www.jstatsoft.org/v51/i04/.'
New functions added: 
    depth.RT() implements a random Tukey depth (RT)
	and its corresponding  some shortcut functions.
    metric.dist() methods as wrappers for dist(),
New arguments in depth.FM(), depth.RP(), depth.mode() and fdata().
The functions: fregre.glm(), fregre.gsam(), fregre.gkam(), classif.np(),
classif.glm(), classi.glm() allow functional and multivariate analysis. 


Release 0.9.8.1 introduces new functions flm.Ftest() and 
dfv.test(). The first performs a functional F-test and the 
second implements the test of Delsol, Ferraty and Vieu (2010). 
Function flm.test() now has a better computational performance 
and function Aijr() has been replaced by Adot().
New argument "lambda" in fdata2fd() function.
New argument "rn" in create.pc.basis() function.
fregre.kgam() has been renamed to fregre.gkam().


Release 0.9.8 introduces a new function flm.test() that 
allows to test for the Functional Linear Model with scalar 
response for a given dataset. Is based on the new functions 
PCvM.statistic(), Aijr() and rber.gold().
A bug in fregre.kgam has been fixed.

Release 0.9.7
New functions: 
fregre.kgam(), classif.kgam(), dev.S(),
predict.fregre.kgam(), print.fregre.kgam(), 
summary.fregre.kgam(), fregre.gsam(), 
classif.np(), classif.kgam(), classif.gsam().

New argument "par.S" in: fregre.np(), 
fregre.np.cv(), fregre.plm(), S.NW(), 
S.KNN(), S.LLR().

New attributes for: metric.lp(), 
semimetric.basis() and semimetric.NPFDA()


Release 0.9.6 renames the functions: 
  pc.fdata()-->fdata2pc()
  pls.fdata()-->fdata2pls()
  pc.cor()-->summary.fdata.comp()
  pc.fdata()-->summary.fdata.comp()
It added create.pls.basis(), Math.fdata(), 
Ops.fdata(), Summary.fdata() and
dis.cos.cor() function.

New argument par.S in: fregre.np, 
fregre.np.cv, fregre.plm,
New argument cv in: S.NW,S.KNN, S.LLR,
In metric.lp the argument p now is called lp.



Release 0.9.5 improves fdata.bootstrap() function
(better computational efficiency). It introduces a new 
functions: for Partial Linear Square (pls.fdata(), fregre.pls() 
and fregre.pls.cv()) and Simpson integration (int.simpson()
and int.simpson2()). It modifies the functions metric.lp(), 
inprod.fdata(), summary.fregre.fd() and predict.fregre.fd().

 
Release 0.9.4 added 3 script files: Outliers_fdata.R, 
flm_beta_estimation_brownian_data.R and Classif_phoneme.R.
It has introduced the functions fregre.glm() and 
predict.fregre.glm() which allow fit and predict
respectively Functional Generalized Linear Models.
It has introduced the functions create.pc.basis and 
create.fdata.basis  which allow to create basis objects
for functional data of class "fdata".

Release 0.9 introduces a new function h.default() that 
simplifies the calculation of the bandwidth parameter
"h" in the functions: fregre.np(), fregre.np.cv() and fregre.plm().  
In most of the functions has added a stop control when the
dataset has missing data (NA's). It adds the attribute "call"
to the distance  matrix calculated in metric.lp(), 
semimetric.basis()  and semimetric.NPFDA() functions.