fdars Scientific References¶
Curated primary paper references for fdars functional data analysis methods.
Generated offline frompython/fdars/_references_map.json.
28/437 callables have at least one curated primary-paper entry.
Uncurated callables are absent; see the AI Capability Map for the full callable surface.
Coverage¶
28 of 437 public callables have at least one curated primary-paper entry. Remaining callables have contested attribution, no single primary paper, or are pending Phase-84 human DOI-landing-page verification. See AI Capability Map for all 437 callables.
Methods by Module¶
Fdata Class Methods¶
Ramsay, J.O., Dalzell, C.J. (1991) — Some tools for functional data analysis¶
| Field | Value |
|---|---|
| Authors | Ramsay, J.O., Dalzell, C.J. |
| Year | 1991 |
| DOI | doi:10.1111/j.2517-6161.1991.tb01844.x |
| URL | https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/j.251... |
| Type | journal |
| Status | Curated |
Callables implementing this method:
_Fdata.to_pc— Functional PCA representation of the curves (1-D only).regression.fregre_lm— Scalar-on-function linear regression via FPCs.regression.predict_fregre_lm— Predict new responses using a fitted functional linear model.regression.fregre_cv— Cross-validated selection of number of FPC components using K-fold CV.
fdars.alignment — Elastic alignment, SRSF registration, Karcher mean, elastic FPCA¶
Marron, J.S., Ramsay, J.O., Sangalli, L.M., Srivastava, A. (2015) — Functional Data Analysis of Amplitude and Phase Variation¶
| Field | Value |
|---|---|
| Authors | Marron, J.S., Ramsay, J.O., Sangalli, L.M., Srivastava, A. |
| Year | 2015 |
| DOI | doi:10.1214/15-STS524 |
| URL | https://projecteuclid.org/journals/statistical-science/vo... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
alignment.alignment_quality— Comprehensive alignment quality metrics.alignment.diagnose_alignment— Diagnose alignment quality for every curve after Karcher mean computation.
Srivastava, A., Wu, W., Kurtek, S., Klassen, E., Marron, J.S. (2011) — Registration of functional data using Fisher-Rao metric¶
| Field | Value |
|---|---|
| Authors | Srivastava, A., Wu, W., Kurtek, S., Klassen, E., Marron, J.S. |
| Year | 2011 |
| URL | https://arxiv.org/abs/1103.3817 |
| Type | preprint |
| Status | Curated |
Callables implementing this method:
alignment.karcher_mean— Karcher (Frechet) mean under the elastic metric.alignment.elastic_align_pair— Pairwise elastic alignment of two curves.alignment.srsf_transform— SRSF (Square Root Slope Function) transform.alignment.srsf_inverse— Inverse SRSF transform.alignment.elastic_distance— Elastic (Fisher-Rao) distance between two curves.alignment.elastic_self_distance_matrix— Elastic self distance matrix.alignment.elastic_cross_distance_matrix— Elastic cross distance matrix.alignment.amplitude_distance— Amplitude distance between two curves.alignment.phase_distance— Phase distance between two curves.alignment.elastic_decomposition— Elastic phase-amplitude decomposition of two curves.
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| Matlab | fdasrvf_MATLAB |
multiple_align_functions |
low |
| Python | fdasrsf |
fdawarp |
high |
| R | fdasrvf |
multiple_align_functions |
high |
Srivastava, A., Klassen, E.P. (2016) — Functional and Shape Data Analysis¶
| Field | Value |
|---|---|
| Authors | Srivastava, A., Klassen, E.P. |
| Year | 2016 |
| DOI | doi:10.1007/978-1-4939-4020-2 |
| URL | https://link.springer.com/book/10.1007/978-1-4939-4020-2 |
| Type | book |
| Status | Pending DOI verification |
Callables implementing this method:
alignment.tsrvf_transform— TSRVF (Transported SRSF) transform.alignment.tsrvf_transform_with_method— TSRVF transform with configurable transport method.alignment.karcher_mean_closed— Karcher mean for closed (periodic) curves.alignment.elastic_align_pair_closed— Align closed (periodic) curve f2 to f1 with rotation search.
Tucker, J.D., Wu, W., Srivastava, A. (2013) — Generative models for functional data using phase and amplitude separation¶
| Field | Value |
|---|---|
| Authors | Tucker, J.D., Wu, W., Srivastava, A. |
| Year | 2013 |
| DOI | doi:10.1016/j.csda.2012.12.001 |
| URL | https://www.sciencedirect.com/science/article/abs/pii/S01... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
alignment.vert_fpca— Vertical (amplitude) FPCA on aligned data.alignment.horiz_fpca— Horizontal (phase) FPCA.alignment.joint_fpca— Joint (amplitude + phase) FPCA.alignment.gauss_model— Generate random curves from a fitted Gaussian model on aligned data.alignment.joint_gauss_model— Generate random curves from a joint Gaussian model preserving amplitude-phase correlation.
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| Python | fdasrsf |
fdavpca |
high |
| R | fdasrvf |
jointFPCA |
high |
Tucker, J.D., Yarger, D. (2024) — Elastic functional changepoint detection of climate impacts from localized sources¶
| Field | Value |
|---|---|
| Authors | Tucker, J.D., Yarger, D. |
| Year | 2024 |
| DOI | doi:10.1002/env.2826 |
| URL | https://onlinelibrary.wiley.com/doi/10.1002/env.2826 |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
alignment.elastic_changepoint— Detect a distributional changepoint in a sequence of curves (elastic).
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| Python | fdasrsf |
ElasticChangePoint |
high |
| R | fdasrvf |
elastic.changepoint |
high |
fdars.basis — Basis representations — B-spline, Fourier, functional PCA¶
Eilers, P.H.C., Marx, B.D. (1996) — Flexible smoothing with B-splines and penalties¶
| Field | Value |
|---|---|
| Authors | Eilers, P.H.C., Marx, B.D. |
| Year | 1996 |
| DOI | doi:10.1214/ss/1038425655 |
| URL | https://projecteuclid.org/journals/statistical-science/vo... |
| Type | journal |
| Status | Curated |
Callables implementing this method:
basis.basis_nbasis_cv— Cross-validated selection of number of basis functions.basis.pspline_fit_1d— Fit P-splines to 1D functional data.basis.pspline_fit_gcv— P-spline fit with GCV-selected smoothing parameter.basis.smooth_basis_gcv— Smooth functional data using basis expansion with GCV.basis.smooth_basis_aic— Smooth functional data using basis expansion with AIC-optimal lambda.basis.fdata_to_basis_1d— Project functional data onto a B-spline or Fourier basis.basis.basis_to_fdata_1d— Reconstruct functional data from basis coefficients.
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| Python | scikit-fda |
BSplineSmoother |
high |
| R | mgcv |
s(..., bs="ps") |
high |
fdars.classification — Functional classifiers — k-nearest neighbours, SVM, centroid-based¶
Cuesta-Albertos, J.A., Fraiman, R. (2009) — Impartial trimmed means for functional data¶
| Field | Value |
|---|---|
| Authors | Cuesta-Albertos, J.A., Fraiman, R. |
| Year | 2009 |
| DOI | doi:10.1007/978-3-7908-2349-3_12 |
| URL | https://link.springer.com/chapter/10.1007/978-3-7908-2349... |
| Type | book_chapter |
| Status | Pending DOI verification |
Callables implementing this method:
classification.fclassif_dd— Depth-based DD-classifier for functional data.
fdars.clustering — Functional k-means, fuzzy, GMM, and elastic clustering¶
Bouveyron, C., Côme, E., Jacques, J. (2015) — The discriminative functional mixture model for a comparative analysis of bike sharing systems¶
| Field | Value |
|---|---|
| Authors | Bouveyron, C., Côme, E., Jacques, J. |
| Year | 2015 |
| DOI | doi:10.1214/15-AOAS861 |
| URL | https://projecteuclid.org/journals/annals-of-applied-stat... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
clustering.funfem_cluster— Fisher-EM discriminative functional clustering (FunFEM).
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| R | funFEM |
funFEM |
high |
Bouveyron, C., Jacques, J. (2011) — Model-based clustering of time series in group-specific functional subspaces¶
| Field | Value |
|---|---|
| Authors | Bouveyron, C., Jacques, J. |
| Year | 2011 |
| DOI | doi:10.1007/s11634-011-0095-6 |
| URL | https://link.springer.com/article/10.1007/s11634-011-0095-6 |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
clustering.gmm_cluster— GMM clustering for functional data (via basis projection).
Chiou, J.M., Li, P.L. (2007) — Functional clustering and identifying substructures of longitudinal data¶
| Field | Value |
|---|---|
| Authors | Chiou, J.M., Li, P.L. |
| Year | 2007 |
| DOI | doi:10.1111/j.1467-9868.2007.00605.x |
| URL | https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.14... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
clustering.kmeans_fd— K-means clustering for functional data.clustering.kcfc_cluster— K-means with per-cluster FPCA (KCFC) clustering for functional data.
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| R | fdapace |
kCFC |
high |
fdars.density_fda — Density-based functional data analysis and Wasserstein distances¶
Agueh, M., Carlier, G. (2011) — Barycenters in the Wasserstein space¶
| Field | Value |
|---|---|
| Authors | Agueh, M., Carlier, G. |
| Year | 2011 |
| DOI | doi:10.1137/100805741 |
| URL | https://epubs.siam.org/doi/10.1137/100805741 |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
density_fda.wasserstein_barycenter— Compute the Wasserstein Fréchet mean (barycenter) of a collection of densities.
Petersen, A., Müller, H.G. (2016) — Functional data analysis for density functions by transformation to a Hilbert space¶
| Field | Value |
|---|---|
| Authors | Petersen, A., Müller, H.G. |
| Year | 2016 |
| DOI | doi:10.1214/15-AOS1363 |
| URL | https://projecteuclid.org/journals/annals-of-statistics/v... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
density_fda.lqd_transform— Compute the log-quantile density (LQD) transform of a density function.density_fda.inverse_lqd— Compute the inverse LQD transform: reconstruct a density from its LQD representation.density_fda.lqd_fpca— Functional PCA of densities via the LQD transform.density_fda.normalize_density— Normalize a density function to integrate to 1.
fdars.depth — Functional depth measures — Fraiman-Muniz, band, modal, random-projection¶
Claeskens, G., Hubert, M., Slaets, L., Vakili, K. (2014) — Multivariate functional halfspace depth¶
| Field | Value |
|---|---|
| Authors | Claeskens, G., Hubert, M., Slaets, L., Vakili, K. |
| Year | 2014 |
| DOI | doi:10.1080/01621459.2013.856795 |
| URL | https://www.tandfonline.com/doi/abs/10.1080/01621459.2013... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
depth.random_projection_deriv_1d— Random-projection depth using curves and their derivatives (RPD).
Cuevas, A., Febrero, M., Fraiman, R. (2007) — Robust estimation and classification for functional data via projection-based depth notions¶
| Field | Value |
|---|---|
| Authors | Cuevas, A., Febrero, M., Fraiman, R. |
| Year | 2007 |
| DOI | doi:10.1007/s00180-007-0053-0 |
| URL | https://link.springer.com/article/10.1007/s00180-007-0053-0 |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
depth.modal_1d— Modal depth for 1D functional data.depth.modal_2d— Modal depth for 2D functional data.depth.random_projection_1d— Random projection depth for 1D functional data.depth.random_projection_2d— Random projection depth for 2D functional data.depth.random_tukey_1d— Random Tukey depth for 1D functional data.depth.random_tukey_2d— Random Tukey depth for 2D functional data.
Unknown (n.d.) — (title pending verification)¶
| Field | Value |
|---|---|
| Authors | Unknown |
| Year | n.d. |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
depth.functional_spatial_1d— Functional spatial depth for 1D data.depth.functional_spatial_2d— Functional spatial depth for 2D data.depth.kernel_functional_spatial_1d— Kernel functional spatial depth for 1D data.depth.kernel_functional_spatial_2d— Kernel functional spatial depth for 2D data.
Fraiman, R., Muniz, G. (2001) — Trimmed means for functional data¶
| Field | Value |
|---|---|
| Authors | Fraiman, R., Muniz, G. |
| Year | 2001 |
| DOI | doi:10.1007/BF02595706 |
| URL | https://link.springer.com/article/10.1007/BF02595706 |
| Type | journal |
| Status | Curated |
Callables implementing this method:
depth.fraiman_muniz_1d— Fraiman-Muniz depth for 1D functional data.depth.fraiman_muniz_2d— Fraiman-Muniz depth for 2D functional data._Fdata.depth— Compute depth values for each observation.
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| Python | scikit-fda |
fraiman_muniz_depth |
high |
| R | fda.usc |
depth.FM |
high |
López-Pintado, S., Romo, J. (2009) — On the concept of depth for functional data¶
| Field | Value |
|---|---|
| Authors | López-Pintado, S., Romo, J. |
| Year | 2009 |
| DOI | doi:10.1198/jasa.2009.0108 |
| URL | https://www.tandfonline.com/doi/abs/10.1198/jasa.2009.0108 |
| Type | journal |
| Status | Curated |
Callables implementing this method:
depth.band_1d— Band depth for 1D functional data.depth.modified_band_1d— Modified band depth for 1D functional data.
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| Python | scikit-fda |
BandDepth |
high |
| R | fda.usc |
depth.mode |
high |
Narisetty, N.N., Nair, V.N. (2016) — Extremal depth for functional data and applications¶
| Field | Value |
|---|---|
| Authors | Narisetty, N.N., Nair, V.N. |
| Year | 2016 |
| DOI | doi:10.1080/01621459.2015.1110033 |
| URL | https://www.tandfonline.com/doi/full/10.1080/01621459.201... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
depth.modified_epigraph_index_1d— Modified epigraph index for 1D functional data.
Sun, Y., Genton, M.G. (2011) — Functional boxplots¶
| Field | Value |
|---|---|
| Authors | Sun, Y., Genton, M.G. |
| Year | 2011 |
| DOI | doi:10.1198/jcgs.2011.09224 |
| URL | https://www.tandfonline.com/doi/abs/10.1198/jcgs.2011.09224 |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
depth.functional_boxplot— Canonical López-Pintado–Romo depth-fence functional boxplot (numeric only).
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| R | fdaoutlier |
functional_boxplot |
high |
fdars.famm — Functional additive mixed models (FAMM) for longitudinal data¶
Scheipl, F., Staicu, A.M., Greven, S. (2015) — Functional additive mixed models¶
| Field | Value |
|---|---|
| Authors | Scheipl, F., Staicu, A.M., Greven, S. |
| Year | 2015 |
| DOI | doi:10.1080/10618600.2014.901914 |
| URL | https://www.tandfonline.com/doi/abs/10.1080/10618600.2014... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
famm.dense_flmm— Fit a Functional Linear Mixed Model (FLMM) via REML-EM.famm.fast_fmm— Fit a fast Functional Mixed Model (FMM) with optional Wald inference.
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| R | refund |
pfr |
high |
Volkmann, A., Stöcker, A., Scheipl, F., Greven, S. (2023) — Multivariate functional additive mixed models¶
| Field | Value |
|---|---|
| Authors | Volkmann, A., Stöcker, A., Scheipl, F., Greven, S. |
| Year | 2023 |
| DOI | doi:10.1177/1471082X211056158 |
| URL | https://journals.sagepub.com/doi/full/10.1177/1471082X211... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
famm.multi_famm— Fit a multi-variable Functional Additive Mixed Model (multiFAMM).
fdars.fdata — Core functional data operations — mean, deriv, norm, integrate, reconstruct¶
Gervini, D. (2008) — Robust functional estimation using the median and spherical principal components¶
| Field | Value |
|---|---|
| Authors | Gervini, D. |
| Year | 2008 |
| DOI | doi:10.1093/biomet/asn031 |
| URL | https://academic.oup.com/biomet/article/95/3/587/217534 |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
fdata.geometric_median_1d— Compute the geometric (L1) median of 1D functional data.fdata.geometric_median_2d— Compute the geometric (L1) median of 2D functional data._Fdata.geometric_median— Compute the geometric (L1) median curve or surface.
Ramsay, J.O., Silverman, B.W. (2005) — Functional Data Analysis¶
| Field | Value |
|---|---|
| Authors | Ramsay, J.O., Silverman, B.W. |
| Year | 2005 |
| DOI | doi:10.1007/b98888 |
| URL | https://link.springer.com/book/10.1007/b98888 |
| Type | book |
| Status | Pending DOI verification |
Callables implementing this method:
fdata.mean_1d— Compute the pointwise mean of 1D functional data.fdata.mean_2d— Compute the pointwise mean of 2D functional data.fdata.functional_covariance— Compute the Bessel-corrected m×m covariance surface of 1D functional data.fdata.functional_variance— Compute the Bessel-corrected pointwise variance of 1D functional data.fdata.functional_std— Compute the Bessel-corrected pointwise standard deviation of 1D functional data.fdata.norm_lp_1d— Compute Lp norms of 1D functional data.fdata.deriv_1d— Compute numerical derivatives of 1D functional data.fdata.deriv_2d— Compute numerical derivatives of 2D functional data.fdata.center_1d— Center functional data by subtracting the pointwise mean.fdata.trim_mean— Compute the depth-trimmed mean of 1D functional data.fdata.normalize— Normalize functional data.regression.fpca— Functional principal component analysis (FPCA).regression.concurrent_regression— Concurrent (varying-coefficient) functional regression.regression.fosr— Function-on-scalar regression (FOSR).regression.fosr_fpc— Function-on-scalar regression via FPCs (FOSR-FPC).regression.predict_fosr— Predict new functional responses from a fitted FOSR model.simulation.sim_kl— Simulate functional data via Karhunen-Loeve expansion (low-level).simulation.simulate— Simulate functional data via Karhunen-Loeve expansion.simulation.eigenfunctions— Compute eigenfunctions.simulation.eigenvalues— Compute eigenvalues.alignment.landmark_register— Register curves to common landmark positions.alignment.landmark_detect_and_register— Detect landmarks and register in one call.alignment.least_squares_shift_registration— Register curves by a per-curve rigid horizontal shift (least-squares).alignment.detect_landmarks— Detect landmarks (peaks, valleys, zero-crossings, inflections) in a curve._Fdata.shift_register— Register curves by a per-curve rigid horizontal shift (least-squares).
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| Python | scikit-fda |
FPCA |
high |
| R | fda |
pca.fd |
high |
fdars.frechet — Fréchet regression for metric-space responses¶
Petersen, A., Müller, H.G. (2019) — Fréchet regression for random objects with Euclidean predictors¶
| Field | Value |
|---|---|
| Authors | Petersen, A., Müller, H.G. |
| Year | 2019 |
| DOI | doi:10.1214/17-AOS1624 |
| URL | https://projecteuclid.org/journals/annals-of-statistics/v... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
frechet.frechet_global_reg— Global Fréchet linear regression for density-response functional data.frechet.frechet_local_reg— Local Fréchet regression for density-response functional data.frechet.frechet_mean— Fréchet mean over a metric space, dispatched by space name.frechet.frechet_anova— Fréchet ANOVA: test equality of Fréchet means across groups.
fdars.fts — Functional time series — forecasting, correlation, spectral analysis¶
Hörmann, S., Kokoszka, P. (2010) — Weakly dependent functional data¶
| Field | Value |
|---|---|
| Authors | Hörmann, S., Kokoszka, P. |
| Year | 2010 |
| DOI | doi:10.1214/09-AOS768 |
| URL | https://projecteuclid.org/journals/annals-of-statistics/v... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
fts.functional_acf— Compute the functional autocorrelation function (ACF) with Monte Carlo bands.fts.functional_pacf— Compute the functional partial autocorrelation function (PACF) with Monte Carlo bands.fts.stationarity_test— Test stationarity of functional time series via permutation test.fts.functional_difference— Compute the first-order functional difference (lag-1 differencing).
Hyndman, R.J., Ullah, M.S. (2007) — Robust forecasting of mortality and fertility rates: a functional data approach¶
| Field | Value |
|---|---|
| Authors | Hyndman, R.J., Ullah, M.S. |
| Year | 2007 |
| DOI | doi:10.1016/j.csda.2006.07.028 |
| URL | https://www.sciencedirect.com/science/article/abs/pii/S01... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
fts.ftsm— Fit a Functional Time Series Model (FTSM) via FPCA + Yule-Walker AR fitting.fts.ftsm_forecast— Fit an FTSM and produce a single- or multi-horizon forecast (single-step variant).fts.ftsm_forecast_multistep— Fit an FTSM and produce a multi-step forecast (iterative multi-step variant).fts.ftsm_update— Online update of an FTSM with one or more new curves.
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| R | ftsa |
ftsm |
high |
Panaretos, V.M., Tavakoli, S. (2013) — Fourier analysis of stationary time series in function space¶
| Field | Value |
|---|---|
| Authors | Panaretos, V.M., Tavakoli, S. |
| Year | 2013 |
| DOI | doi:10.1214/13-AOS1086 |
| URL | https://projecteuclid.org/journals/annals-of-statistics/v... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
fts.dpca— Fit Dynamic Functional Principal Components Analysis (DPCA).fts.dpca_reconstruct— Fit DPCA and reconstruct the functional time series from dynamic components.fts.spectral_density— Estimate the spectral density operator of a functional time series.fts.long_run_covariance— Estimate the long-run covariance operator of a functional time series.
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| R | freqdom.fda |
dpca |
high |
fdars.inference — Functional inference — two-sample tests, interval-wise testing¶
Cuesta-Albertos, J.A., Febrero-Bande, M. (2010) — A simple multiway ANOVA for functional data¶
| Field | Value |
|---|---|
| Authors | Cuesta-Albertos, J.A., Febrero-Bande, M. |
| Year | 2010 |
| DOI | doi:10.1007/s11749-010-0185-3 |
| URL | https://link.springer.com/article/10.1007/s11749-010-0185-3 |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
inference.oneway_anova_vstat— One-way functional ANOVA V-statistic (asymptotic scaled-χ² test).
Cuevas, A., Febrero, M., Fraiman, R. (2004) — An ANOVA test for functional data¶
| Field | Value |
|---|---|
| Authors | Cuevas, A., Febrero, M., Fraiman, R. |
| Year | 2004 |
| DOI | doi:10.1016/j.csda.2003.10.021 |
| URL | https://www.sciencedirect.com/science/article/abs/pii/S01... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
inference.f_perm_test— Functional two-sample permutation F-test.inference.t_perm_test— Functional two-sample permutation t-test.inference.two_sample_mean_test— Functional two-sample mean-equality test via Hotelling-T² on a shared FPC
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| R | fda.usc |
fanova.onefactor |
high |
Degras, D. (2011) — Simultaneous confidence bands for nonparametric regression with functional data¶
| Field | Value |
|---|---|
| Authors | Degras, D. |
| Year | 2011 |
| DOI | doi:10.5705/ss.2009.207 |
| URL | https://www3.stat.sinica.edu.tw/sstest/j21n4/J21N412/J21N... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
inference.mean_scb— Simultaneous confidence band for the mean function (Degras).inference.scb_two_sample_test— Two-sample mean-equality test via a simultaneous confidence band for thetolerance.scb_mean_degras— Simultaneous confidence band (Degras method).
Pini, A., Vantini, S. (2016) — The interval testing procedure: A general framework for inference in functional data analysis¶
| Field | Value |
|---|---|
| Authors | Pini, A., Vantini, S. |
| Year | 2016 |
| DOI | doi:10.1111/biom.12476 |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.12476 |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
inference.itp_flm— Interval-wise testing procedure for the functional linear model.inference.itp_one_pop— Interval-wise testing procedure for a single functional population.inference.itp_two_pop— Interval-wise testing procedure for two functional populations.
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| R | fdatest |
ITP1bspline |
high |
fdars.metric — Functional distance and similarity metrics — L2, L1, Mahalanobis, DTW¶
Blondel, M., Mensch, A., Vert, J.P. (2021) — Differentiable Divergences Between Time Series¶
| Field | Value |
|---|---|
| Authors | Blondel, M., Mensch, A., Vert, J.P. |
| Year | 2021 |
| URL | https://arxiv.org/abs/2201.12484 |
| Type | conference |
| Status | Pending DOI verification |
Callables implementing this method:
metric.soft_dtw_div_self_1d— Soft-DTW divergence distance matrix (self) for 1D data.metric.soft_dtw_div_cross_1d— Soft-DTW divergence cross distance.
Cuturi, M. (2011) — Fast global alignment kernels¶
| Field | Value |
|---|---|
| Authors | Cuturi, M. |
| Year | 2011 |
| DOI | doi:10.5555/3104482.3104599 |
| URL | https://dl.acm.org/doi/10.5555/3104482.3104599 |
| Type | conference |
| Status | Pending DOI verification |
Callables implementing this method:
metric.gak— Global Alignment Kernel between two 1-D time series.metric.gak_gram_matrix— Global Alignment Kernel Gram matrix (one-shot, symmetric).metric.gak_gram_train— Fit a GAK Gram handle for incremental train/predict computation.metric.gak_gram_predict— Compute the GAK Gram matrix between new data and the training set.metric.sigma_gak— Heuristic GAK bandwidth: median pairwise Euclidean distance, floored at 1e-8.metric.PyGakGramTrain— Opaque handle wrapping fdars-core GakGramTrain for incremental GAK Gram computation.
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| Python | tslearn |
gak |
high |
| R | dtwclust |
GAK |
high |
Cuturi, M., Blondel, M. (2017) — Soft-DTW: a differentiable loss function for time-series¶
| Field | Value |
|---|---|
| Authors | Cuturi, M., Blondel, M. |
| Year | 2017 |
| URL | https://proceedings.mlr.press/v70/cuturi17a.html |
| Type | conference |
| Status | Curated |
Callables implementing this method:
metric.soft_dtw_self_1d— Soft-DTW distance matrix (self) for 1D data.metric.soft_dtw_cross_1d— Soft-DTW cross distance for 1D data.
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| Python | tslearn |
soft_dtw |
high |
Sakoe, H., Chiba, S. (1978) — Dynamic programming algorithm optimization for spoken word recognition¶
| Field | Value |
|---|---|
| Authors | Sakoe, H., Chiba, S. |
| Year | 1978 |
| DOI | doi:10.1109/TASSP.1978.1163055 |
| URL | https://ieeexplore.ieee.org/document/1163055 |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
metric.dtw_self_1d— DTW distance matrix (self) for 1D data.metric.dtw_cross_1d— DTW cross distance for 1D data.
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| Python | tslearn |
dtw |
high |
| R | dtw |
dtw |
high |
fdars.outliers — Functional outlier detection — depth-based, magnitude, shape outliers¶
Arribas-Gil, A., Romo, J. (2014) — Shape outlier detection and visualization for functional data: the outliergram¶
| Field | Value |
|---|---|
| Authors | Arribas-Gil, A., Romo, J. |
| Year | 2014 |
| DOI | doi:10.1093/biostatistics/kxu006 |
| URL | https://academic.oup.com/biostatistics/article/15/4/603/2... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
outliers.outliergram— Outliergram (MEI vs MBD plot).
Dai, W., Genton, M.G. (2019) — Directional outlyingness for multivariate functional data¶
| Field | Value |
|---|---|
| Authors | Dai, W., Genton, M.G. |
| Year | 2019 |
| DOI | doi:10.1016/j.csda.2018.03.017 |
| URL | https://www.sciencedirect.com/science/article/abs/pii/S01... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
outliers.magnitude_shape— Magnitude-shape outlyingness.outliers.muod— MUOD (Massive Unsupervised Outlier Detection) for functional data.
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| R | fdaoutlier |
msplot |
high |
Febrero, M., Galeano, P., González-Manteiga, W. (2008) — Outlier detection in functional data by depth measures with application to identify abnormal NOx levels¶
| Field | Value |
|---|---|
| Authors | Febrero, M., Galeano, P., González-Manteiga, W. |
| Year | 2008 |
| DOI | doi:10.1002/env.878 |
| URL | https://onlinelibrary.wiley.com/doi/abs/10.1002/env.878 |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
outliers.detect_outliers_lrt— LRT-based outlier detection with bootstrap.outliers.detect_outliers_lrt_with_dist— LRT-based outlier detection with bootstrap (returns threshold and null distribution).
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| R | fda.usc |
foutliers |
high |
fdars.pace_fpca — PACE: Principal Analysis by Conditional Expectation for sparse data¶
Yao, F., Müller, H.G., Wang, J.L. (2005) — Functional data analysis for sparse longitudinal data¶
| Field | Value |
|---|---|
| Authors | Yao, F., Müller, H.G., Wang, J.L. |
| Year | 2005 |
| DOI | doi:10.1198/016214504000001745 |
| URL | https://www.tandfonline.com/doi/abs/10.1198/0162145040000... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
pace_fpca.pace_fpca— Run PACE FPCA on irregular/sparse functional data.pace_fpca.irreg_fdata_from_lists— Build an IrregFdata handle from two Python lists of ragged 1-D arrays.pace_fpca.PyIrregFdata— Opaque handle wrapping fdars-core IrregFdata for irregular/sparse functional data.
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| Matlab | PACE |
FPCA |
low |
| Python | scikit-fda |
FPCA |
high |
| R | fdapace |
PACE |
high |
fdars.regression — Functional regression — scalar-on-function, function-on-scalar, GLM¶
Ferraty, F., Vieu, P. (2006) — Nonparametric Functional Data Analysis¶
| Field | Value |
|---|---|
| Authors | Ferraty, F., Vieu, P. |
| Year | 2006 |
| DOI | doi:10.1007/0-387-36620-2 |
| URL | https://link.springer.com/book/10.1007/0-387-36620-2 |
| Type | book |
| Status | Pending DOI verification |
Callables implementing this method:
regression.fregre_np— Nonparametric kernel regression for functional data (from distance matrix).regression.fregre_np_cv— Cross-validated bandwidth selection for nonparametric functional regression.regression.fregre_np_mixed— Nonparametric functional regression mixing functional and scalar predictors.classification.fclassif_kernel— Kernel classification for functional data.classification.kernel_classify_from_distances— Kernel classification from a precomputed functional distance matrix.
Müller, H.G., Stadtmüller, U. (2005) — Generalized functional linear models¶
| Field | Value |
|---|---|
| Authors | Müller, H.G., Stadtmüller, U. |
| Year | 2005 |
| DOI | doi:10.1214/009053604000001156 |
| URL | https://projecteuclid.org/journals/annals-of-statistics/v... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
regression.functional_logistic— Functional logistic regression.regression.functional_glm— Functional generalised linear model (GLM) via FPC scores.regression.predict_functional_logistic— Predict probabilities for new data using a fitted functional logistic model.
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| R | fda.usc |
fregre.glm |
high |
Preda, C., Saporta, G. (2005) — PLS regression on a stochastic process¶
| Field | Value |
|---|---|
| Authors | Preda, C., Saporta, G. |
| Year | 2005 |
| DOI | doi:10.1016/j.csda.2004.07.002 |
| URL | https://www.sciencedirect.com/science/article/abs/pii/S01... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
regression.fpls— Functional PLS (Partial Least Squares).regression.fregre_pls— Scalar-on-function PLS regression.regression.predict_fregre_pls— Predict new responses using a fitted PLS regression.fts.fplsr— Functional Partial Least Squares Regression (fPLSR) one-step-ahead forecast.
fdars.shapelet — Functional shapelets — pattern-based classification and feature learning¶
Ye, L., Keogh, E. (2009) — Time series shapelets: a new primitive for data mining¶
| Field | Value |
|---|---|
| Authors | Ye, L., Keogh, E. |
| Year | 2009 |
| DOI | doi:10.1145/1557019.1557122 |
| URL | https://dl.acm.org/doi/10.1145/1557019.1557122 |
| Type | conference |
| Status | Pending DOI verification |
Callables implementing this method:
shapelet.discover_shapelets— Discover shapelets in labeled functional data and return a summary dict.shapelet.shapelet_transform_fit— Fit a shapelet transform on labeled functional data.shapelet.shapelet_transform— Apply a fitted shapelet transform to new functional data.shapelet.shapelet_distance— Compute the shapelet distance between a z-normalized shapelet and a series.shapelet.shapelet_classifier_fit— Fit a shapelet-based classifier on labeled functional data.shapelet.PyShapeletFit— Opaque handle wrapping fdars-core ShapeletTransformFit.shapelet.PyShapeletClassifierFit— Opaque handle wrapping fdars-core ShapeletClassifierFit.
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| Python | sktime |
ShapeletTransform |
high |
fdars.smoothing — Functional smoothing — kernel, basis, and roughness-penalised methods¶
Craven, P., Wahba, G. (1979) — Smoothing noisy data with spline functions¶
| Field | Value |
|---|---|
| Authors | Craven, P., Wahba, G. |
| Year | 1979 |
| DOI | doi:10.1007/BF01404567 |
| URL | https://link.springer.com/article/10.1007/BF01404567 |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
smoothing.gcv_smoother— GCV score for a kernel smoother.smoothing.optim_bandwidth— Optimal bandwidth selection via cross-validation.
Nadaraya, E.A., Watson, G.S. (1964) — (title pending verification)¶
| Field | Value |
|---|---|
| Authors | Nadaraya, E.A., Watson, G.S. |
| Year | 1964 |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
smoothing.nadaraya_watson— Nadaraya-Watson kernel smoother.
fdars.spm — Statistical process monitoring — T2, SPE, control charts¶
Happ, C., Greven, S. (2018) — Multivariate functional principal component analysis for data observed on different (dimensional) domains¶
| Field | Value |
|---|---|
| Authors | Happ, C., Greven, S. |
| Year | 2018 |
| DOI | doi:10.1080/01621459.2016.1273115 |
| URL | https://www.tandfonline.com/doi/abs/10.1080/01621459.2016... |
| Type | journal |
| Status | Pending DOI verification |
Callables implementing this method:
spm.mfpca— Multivariate Functional Principal Component Analysis (MFPCA).multi_fdata.PyMultiFunData— Opaque handle wrapping fdars-core MultiFunData for multi-domain functional data.multi_fdata.multi_fdata_from_components— Build a PyMultiFunData handle from lists of 2-D data arrays and 1-D argvals vectors.
Cross-language implementations:
| Language | Package | Function | Confidence |
|---|---|---|---|
| R | MFPCA |
MFPCA |
high |