fda.usc 2.1.0

fda.usc 2.0.3

  1. summary.fdata.comp(), Several changes on summary.fdata.comp
  2. Now, we uses data.matrix (instead of as.matrix) to convert data.table in a matrix class object, option recommended when data.frame contains characters.
  3. classif.cv.glmnet() and classif.gbm(), functional basis classsification using cv.glmnet(), require glmnet package, and gbm(), require gbm package.
  4. h.default(), new argument ‘Ker’
  5. mfdata(), new class object for multivariate functional data
  6. fregre.basis.cv(), fregre.basis.cv() and fregre.pc() return df.residual object
  7. Bug corrected in S.LPR() and S.LLR()
  8. classif.gsam.vs, new function for variable selection in additive classifier
  9. fdata2basis() is used in fregre.lm() and predict.fregre.lm()
  10. summary for fdata2basis()

fda.usc 2.0.2

fda.usc 2.0.1

  1. Modification in fdata() function to avoid class()== and class()!= instead use is(),
  2. kmeans.fd function:
  1. “par.ini” argument is depreciated, the user can use “method” argument.
  2. “cluster.size” argument are added.
  3. New internal function predict.kmeans.fd.
  1. Bug corrected in internal function pred2glm2boost(), it is used for predictions of classiff.DD outputs

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

  3. Modifications in ldata.cen

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

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

fda.usc 2.0.0

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

  1. ldata() class definition.

  2. 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”

  3. New function metric.mfdata: compute distance for mfdata class object: list with m functional data

  4. plot.ldata: plots for ldata object, it allows drawing using a color bar.

  5. plot.mfdata: plot for mfdata object (internal function, pending to completed the Rd document)

  6. depth.modep, depth.mode call metric.lp and metric.ldata propperly

  7. New functions: subset.ldata, is.lfdata “[.lfdata” “[.ldata” is.ldata names.ldata c.ldata

fda.usc 1.5.0

fda.usc 1.4.0

  1. 173,41-50 DOI: 10.1016/j.chemolab.2017.12.001.

fda.usc 1.3.0

fda.usc 1.2.3

fda.usc 1.2.2

fda.usc 1.2.1

fda.usc 1.2.0

fda.usc 1.1.0

*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.

fda.usc 1.0.5

fda.usc 1.0.4

fda.usc 1.0.3

fda.usc 1.0.2

fda.usc 1.0.1

New functions:

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.

fda.usc 1.0.0

*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 https://www.jstatsoft.org/article/view/v051i04


Release 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().

fda.usc 0.9.8

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.

fda.usc 0.9.7

fda.usc 0.9.6

fda.usc 0.9.5

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().

fda.usc 0.9.4

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”.

fda.usc 0.9

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.