Functional-Data

slides of the tutorial at the 2026 Training school 2026 on Learning from Complex Data: Statistical and AI Perspectives, organized at University of Calabria, Cosenza, Italy: FDA-tutorial.pdf

Regression with functional covariates

Functional output and functional covariates

1. FFR, PenFFR: code available here

Corresponding paper:
J.S. Tamo Tchomgui, J. Jacques, V. Barriac, G. Fraysse, S. Chrétien (2023). A Penalized Spline Estimator for Functional Linear Regression with Functional Response. HAL

2. FFMoE / PenFFMoE,a mixture approach : code available here

Corresponding paper:
J.S. Tamo Tchomgui, J. Jacques, V. Barriac, G. Fraysse, S. Chrétien (2024). A mixture of experts regression model for functional response with functional covariates. To appear in Statistics and Computing HAL

Continuous, ordinal or nominal categorical output and functional covariates

1. FREG : R package for linear, ordinal and logistic regression with functional covariates : code available here

Corresponding paper for the ordinal case:
J. Jacques, S. Samardzic (2022). Analyzing cycling sensors data through ordinal logistic regression with functional covariates. Journal of the Royal Statistical Society, Series C, 71[4], 969-986. HAL

Functional data clustering

1. FunHDDC : R package for clustering functional data, available on CRAN

Corresponding papers:
A. Schmutz, J. Jacques, C. Bouveyron, L. Chèze and P. Martin (2020). Clustering multivariate functional data in group-specific functional subspaces, Computational Statistics, 35, 1101-1131. HAL

C.Bouveyron and J.Jacques (2011), Model-based Clustering of Time Series in Group-specific Functional Subspaces, Advances in Data Analysis and Classification, 5[4], 281-300.

2. FunFEM : R package for co-clustering functional data, available on CRAN

Corresponding papers:
C. Bouveyron, E. Côme and J. Jacques (2015), The discriminative functional mixture model for the analysis of bike sharing systems, Annals of Applied Statistics, 9[4], 1726-1760. HAL

Functional data co-clustering

1. FunLBM : R package for co-clustering functional data, available on CRAN

Corresponding papers:
C. Bouveyron, L. Bozzi L., J. Jacques J. and F-X. Jollois (2018). The Functional Latent Block Model for the Co-Clustering of Electricity Consumption Curves, Journal of the Royal Statistical Society, Series C, 67 [4], 897-915. HAL

Multivariate functional data set

1. The cycling data set provides 216 observations of 9 curves. Each curve is sampled at 1800 regular time points. To use it, please tell me and cite:

J. Jacques, S. Samardzic (2022). Analyzing cycling sensors data through ordinal logistic regression with functional covariates. Journal of the Royal Statistical Society, Series C, 71[4], 969-986.

Publications using this data set:
S. Weinberger , J. Cugliari , A. Le Cain (2025). Ordinal regression for preference learning in wearables using sensor data. Expert Systems with Applications, In press, https://hal.science/hal-05038326v1