npfda: Nonparametric functional data analysis

Version 0.1.4

This package implements nonparametric methods for inference on (multidimensional) functional data using the tools available in package npsp.

Main functions

  • npf.data() defines a (multidimensional) functional data set.

Nonparametric methods for inference on the functional trend, the functional variance and the variogram:

  • npf.fit() (automatically) fits a nonparametric functional model by estimating the trend, the conditional variance and the variogram.

  • locpol(), np.var() and np.svar() methods use local polynomial kernel smoothing to compute nonparametric estimates of the functional trend, the functional variance and the (intra-curve) variogram (or their first derivatives), respectively.

See the Reference for the complete list of functions.

Installation

npfda is not available from CRAN, but you can install the development version from github with:

# install.packages("remotes")
remotes::install_github("rubenfcasal/npfda")

Authors

  • Rubén Fernández-Casal (Dep. Mathematics, University of A Coruña, Spain).

  • Sergio Castillo-Páez (Universidad de las Fuerzas Armadas ESPE, Ecuador).

  • Miguel Flores (Faculty of Administrative Sciences, Escuela Politécnica Nacional, Ecuador).

Please send comments, error reports or suggestions to .

Acknowledgments

This research has been supported by MICINN (Grant PID2020-113578RB-I00). The research of Rubén Fernández-Casal has been supported by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14 and Centro de Investigación del Sistema Universitario de Galicia ED431G 2019/01). All these grants were co-financed by the ERDF. The research of Sergio Castillo Páez has been supported by the Universidad de las Fuerzas Armadas ESPE, from Ecuador.

References

  • Fernández-Casal R. (2023) npsp: Nonparametric spatial (geo)statistics. R package version 0.7-11, https://rubenfcasal.github.io/npsp.

  • Fernández-Casal R., Castillo-Páez S. and García-Soidán P. (2017), Nonparametric estimation of the small-scale variability of heteroscedastic spatial processes, Spa. Sta., 22, 358-370, DOI.

  • Shapiro A. and Botha J.D. (1991) Variogram fitting with a general class of conditionally non-negative definite functions. Computational Statistics and Data Analysis, 11, 87-96.