Changed inbook references to book format + added chapter and pages in…

… the text

docs/refs.bib

@@ -223,29 +223,11 @@ @article{febrero-bande+oviedodelafuente_2012_statistical
   keywords = {depth measures,functional data regression,non-parametric kernel estimation,outlier,representation of functional data}
 }
 
-@inbook{ferraty+vieu_2006_computational,
-  title = {Computational Issues},
-  booktitle = {Nonparametric Functional Data Analysis: Theory and Practice},
+@book{ferraty+vieu_2006,
+  title = {Nonparametric Functional Data Analysis: Theory and Practice},
   author = {Ferraty, Fr{\'e}d{\'e}ric and Vieu, Philippe},
   year = {2006},
   series = {Springer {{Series}} in {{Statistics}}},
-  pages = {99--108},
-  publisher = {{Springer-Verlag}},
-  address = {{New York}},
-  url = {https://www.springer.com/gp/book/9780387303697},
-  urldate = {2019-09-10},
-  collaborator = {Ferraty, Fr{\'e}d{\'e}ric and Vieu, Philippe},
-  isbn = {978-0-387-30369-7},
-  langid = {english}
-}
-
-@inbook{ferraty+vieu_2006_functional,
-  title = {Functional Nonparametric Prediction Methodologies},
-  booktitle = {Nonparametric Functional Data Analysis: Theory and Practice},
-  author = {Ferraty, Fr{\'e}d{\'e}ric and Vieu, Philippe},
-  year = {2006},
-  series = {Springer {{Series}} in {{Statistics}}},
-  pages = {49--59},
   publisher = {{Springer-Verlag}},
   address = {{New York}},
   url = {https://www.springer.com/gp/book/9780387303697},
@@ -436,52 +418,12 @@ @inproceedings{ramos-carreno++_2022_scikitfda
   keywords = {Data analysis,data visualization,Data visualization,Documentation,Ecosystems,Extrapolation,functional data analysis,Interpolation,machine learning,Machine learning,Python toolbox}
 }
 
-@inbook{ramsay+silverman_2005_functionala,
-  title = {From Functional Data to Smooth Functions},
-  booktitle = {Functional Data Analysis},
-  author = {Ramsay, James and Silverman, Bernard W.},
-  year = {2005},
-  series = {Springer {{Series}} in {{Statistics}}},
-  edition = {Second},
-  pages = {37--58},
-  publisher = {{Springer-Verlag}},
-  address = {{New York}},
-  doi = {10.1007/b98888},
-  url = {https://www.springer.com/gp/book/9780387400808},
-  urldate = {2021-09-19},
-  collaborator = {Ramsay, James and Silverman, Bernard W.},
-  isbn = {978-0-387-40080-8},
-  langid = {english},
-  keywords = {Multivariate analysis}
-}
-
-@inbook{ramsay+silverman_2005_introduction,
-  title = {Introduction},
-  booktitle = {Functional Data Analysis},
-  author = {Ramsay, James and Silverman, Bernard W.},
-  year = {2005},
-  series = {Springer {{Series}} in {{Statistics}}},
-  edition = {Second},
-  pages = {1--18},
-  publisher = {{Springer-Verlag}},
-  address = {{New York}},
-  doi = {10.1007/b98888},
-  url = {https://www.springer.com/gp/book/9780387400808},
-  urldate = {2021-09-19},
-  collaborator = {Ramsay, James and Silverman, Bernard W.},
-  isbn = {978-0-387-40080-8},
-  langid = {english},
-  keywords = {Multivariate analysis}
-}
-
-@inbook{ramsay+silverman_2005_registration,
-  title = {The Registration and Display of Functional Data},
-  booktitle = {Functional Data Analysis},
+@book{ramsay+silverman_2005,
+  title = {Functional Data Analysis},
   author = {Ramsay, James and Silverman, Bernard W.},
   year = {2005},
   series = {Springer {{Series}} in {{Statistics}}},
   edition = {Second},
-  pages = {127--145},
   publisher = {{Springer-Verlag}},
   address = {{New York}},
   doi = {10.1007/b98888},
@@ -493,25 +435,6 @@ @inbook{ramsay+silverman_2005_registration
   keywords = {Multivariate analysis}
 }
 
-@inbook{ramsay+silverman_2005_basisfuncexp,
-  title = {Basis function expansion of the functions},
-  booktitle = {Functional Data Analysis},
-  author = {Ramsay, James and Silverman, Bernard W.},
-  year = {2005},
-  series = {Springer {{Series}} in {{Statistics}}},
-  edition = {Second},
-  pages = {161--164},
-  publisher = {{Springer-Verlag}},
-  address = {{New York}},
-  doi = {10.1007/b98888},
-  url = {https://www.springer.com/gp/book/9780387400808},
-  urldate = {2024-10-20},
-  collaborator = {Ramsay, James and Silverman, Bernard W.},
-  isbn = {978-0-387-40080-8},
-  langid = {english},
-  keywords = {Multivariate analysis}
-}
-
 @incollection{romeo+marzoljaen_2014_analisis,
   title = {{An\'alisis del viento y la niebla en el aeropuerto de Los Rodeos (Tenerife). Cambios y tendencias}},
   booktitle = {{Cambio clim\'atico y cambio global.}},
@@ -564,31 +487,12 @@ @article{serfling+zuo_2000_general
   keywords = {62G20,62H05,halfspace depth,multivariate symmetry,simplicial depth,Statistical depth functions}
 }
 
-@inbook{srivastava+klassen_2016_functionala,
-  title = {Functional Data and Elastic Registration},
-  booktitle = {Functional and Shape Data Analysis},
-  author = {Srivastava, Anuj and Klassen, Eric P.},
-  editor = {Bicke, Peter and Diggle, Peter and Fienberg, Stephen E. and Gather, Ursula and Olkin, Ingram and Zeger, Scott},
-  year = {2016},
-  series = {Springer {{Series}} in {{Statistics}}},
-  pages = {73--123},
-  publisher = {{Springer-Verlag}},
-  address = {{New York}},
-  doi = {10.1007/978-1-4939-4020-2},
-  url = {https://www.springer.com/gp/book/9781493940189},
-  urldate = {2020-01-12},
-  isbn = {978-1-4939-4018-9},
-  langid = {english}
-}
-
-@inbook{srivastava+klassen_2016_statistical,
-  title = {Statistical Modeling of Functional Data},
-  booktitle = {Functional and Shape Data Analysis},
+@book{srivastava+klassen_2016,
+  title = {Functional and Shape Data Analysis},
   author = {Srivastava, Anuj and Klassen, Eric P.},
   editor = {Bicke, Peter and Diggle, Peter and Fienberg, Stephen E. and Gather, Ursula and Olkin, Ingram and Zeger, Scott},
   year = {2016},
   series = {Springer {{Series}} in {{Statistics}}},
-  pages = {269--303},
   publisher = {{Springer-Verlag}},
   address = {{New York}},
   doi = {10.1007/978-1-4939-4020-2},
@@ -679,13 +583,11 @@ @article{wang+chiou+muller_2016_fpca
   keywords = {functional linear regression, functional principal component analysis, functional additive model, functional correlation, clustering and classification, time warping}
 }
 
-@inbook{wasserman_2006_nonparametric,
-  title = {Nonparametric Regression},
-  booktitle = {All of Nonparametric Statistics},
+@book{wasserman_2006,
+  title = {All of Nonparametric Statistics},
   author = {Wasserman, Larry},
   year = {2006},
   series = {Springer {{Texts}} in {{Statistics}}},
-  pages = {61--123},
   publisher = {{Springer-Verlag}},
   address = {{New York}},
   url = {https://www.springer.com/gp/book/9780387251455},

docs/sg_execution_times.rst

@@ -0,0 +1,151 @@
+
+:orphan:
+
+.. _sphx_glr_sg_execution_times:
+
+
+Computation times
+=================
+**00:00.682** total execution time for 39 files **from all galleries**:
+
+.. container::
+
+  .. raw:: html
+
+    <style scoped>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/5.3.0/css/bootstrap.min.css" rel="stylesheet" />
+    <link href="https://cdn.datatables.net/1.13.6/css/dataTables.bootstrap5.min.css" rel="stylesheet" />
+    </style>
+    <script src="https://code.jquery.com/jquery-3.7.0.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/jquery.dataTables.min.js"></script>
+    <script src="https://cdn.datatables.net/1.13.6/js/dataTables.bootstrap5.min.js"></script>
+    <script type="text/javascript" class="init">
+    $(document).ready( function () {
+        $('table.sg-datatable').DataTable({order: [[1, 'desc']]});
+    } );
+    </script>
+
+  .. list-table::
+   :header-rows: 1
+   :class: table table-striped sg-datatable
+
+   * - Example
+     - Time
+     - Mem (MB)
+   * - :ref:`sphx_glr_auto_examples_plot_pairwise_alignment.py` (``../examples/plot_pairwise_alignment.py``)
+     - 00:00.682
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_expand_skfda_plot_basis_subclass.py` (``../examples/expand_skfda/plot_basis_subclass.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_expand_skfda_plot_new_evaluator.py` (``../examples/expand_skfda/plot_new_evaluator.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_aemet_unsupervised.py` (``../examples/plot_aemet_unsupervised.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_boxplot.py` (``../examples/plot_boxplot.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_classification_methods.py` (``../examples/plot_classification_methods.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_clustering.py` (``../examples/plot_clustering.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_composition.py` (``../examples/plot_composition.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_depth_classification.py` (``../examples/plot_depth_classification.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_discrete_representation.py` (``../examples/plot_discrete_representation.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_elastic_registration.py` (``../examples/plot_elastic_registration.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_explore.py` (``../examples/plot_explore.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_extrapolation.py` (``../examples/plot_extrapolation.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_fpca.py` (``../examples/plot_fpca.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_fpca_inverse_transform_outl_detection.py` (``../examples/plot_fpca_inverse_transform_outl_detection.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_fpca_regression.py` (``../examples/plot_fpca_regression.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_functional_regression.py` (``../examples/plot_functional_regression.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_interpolation.py` (``../examples/plot_interpolation.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_k_neighbors_classification.py` (``../examples/plot_k_neighbors_classification.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_kernel_regression.py` (``../examples/plot_kernel_regression.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_kernel_smoothing.py` (``../examples/plot_kernel_smoothing.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_landmark_registration.py` (``../examples/plot_landmark_registration.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_landmark_shift.py` (``../examples/plot_landmark_shift.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_magnitude_shape.py` (``../examples/plot_magnitude_shape.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_magnitude_shape_synthetic.py` (``../examples/plot_magnitude_shape_synthetic.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_neighbors_functional_regression.py` (``../examples/plot_neighbors_functional_regression.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_neighbors_scalar_regression.py` (``../examples/plot_neighbors_scalar_regression.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_oneway.py` (``../examples/plot_oneway.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_oneway_synthetic.py` (``../examples/plot_oneway_synthetic.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_phonemes_classification.py` (``../examples/plot_phonemes_classification.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_radius_neighbors_classification.py` (``../examples/plot_radius_neighbors_classification.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_representation.py` (``../examples/plot_representation.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_shift_registration.py` (``../examples/plot_shift_registration.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_surface_boxplot.py` (``../examples/plot_surface_boxplot.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_examples_plot_tecator_regression.py` (``../examples/plot_tecator_regression.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_tutorial_plot_basis_representation.py` (``../tutorial/plot_basis_representation.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_tutorial_plot_getting_data.py` (``../tutorial/plot_getting_data.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_tutorial_plot_introduction.py` (``../tutorial/plot_introduction.py``)
+     - 00:00.000
+     - 0.0
+   * - :ref:`sphx_glr_auto_tutorial_plot_skfda_sklearn.py` (``../tutorial/plot_skfda_sklearn.py``)
+     - 00:00.000
+     - 0.0

examples/plot_pairwise_alignment.py

@@ -34,8 +34,9 @@
 #
 # In the case of elastic registration it is taken as energy function the
 # Fisher-Rao distance with a penalisation term, due to the property of
-# invariance to reparameterizations of warpings functions
-# :footcite:p:`srivastava+klassen_2016_functionala`.
+# invariance to reparameterizations of warpings functions,
+# as detailed in Srivastava and Klassen's chapter, *"Functional Data and 
+# Elastic Registration"*, pp. 73-123\ :footcite:p:`srivastava+klassen_2016`.
 #
 # .. math::
 #   E[f \circ \gamma, g] = d_{FR} (f \circ \gamma, g)

examples/plot_phonemes_classification.py

@@ -37,8 +37,9 @@
 # %%
 # We will first load the (binary) Phoneme dataset and plot the first 20
 # functions.
-# We restrict the data to the first 150 variables, as done in
-# :footcite:t:`ferraty+vieu_2006_computational`, because most of the
+# We restrict the data to the first 150 variables, as done in Ferraty and 
+# Vieu's chapter, *"Computational Issues"*, pp. 99.-108\
+# :footcite:ps:`ferraty+vieu_2006`, because most of the
 # useful information is in the lower frequencies.
 X, y = fetch_phoneme(return_X_y=True)
 

examples/plot_tecator_regression.py

@@ -48,7 +48,9 @@
 
 # %%
 # For spectrometric data, the relevant information of the curves can often
-# be found in the derivatives\ :footcite:`ferraty+vieu_2006_computational`.
+# be found in the derivatives, as discussed in Ferraty and Vieu's chapter, 
+# *"Computational Issues"*, pp. 99–108\ 
+# :footcite:`ferraty+vieu_2006`.
 # Thus, we compute numerically the second derivative and plot it.
 X_der = X.derivative(order=2)
 X_der.plot(gradient_criteria=y)

skfda/datasets/_real_datasets.py

@@ -1234,7 +1234,8 @@ def fetch_gait(
 
 if fetch_gait.__doc__ is not None:  # docstrings can be stripped off
     fetch_gait.__doc__ += _gait_template.format(
-        cite=":footcite:p:`ramsay+silverman_2005_introduction`",
+        cite="Ramsay and Silverman, *'Introduction'*, pp. 1-18"
+        ":footcite:p:`ramsay+silverman_2005`.",
         bibliography=".. footbibliography::",
     ) + _param_descr
 
@@ -1344,7 +1345,9 @@ def fetch_handwriting(
 
 if fetch_handwriting.__doc__ is not None:  # docstrings can be stripped off
     fetch_handwriting.__doc__ += _handwriting_template.format(
-        cite=":footcite:p:`ramsay+silverman_2005_functionala`",
+        cite="Ramsay and Silverman,"
+        "*'From Functional Data to Smooth Functions'*, pp. 37-58"
+        ":footcite:p:`ramsay+silverman_2005`.",
         bibliography=".. footbibliography::",
     ) + _param_descr
 

skfda/exploratory/stats/_fisher_rao.py

@@ -72,8 +72,10 @@ def _fisher_rao_warping_mean(
     \gamma_i(b)=b`.
 
     The karcher mean :math:`\bar \gamma` is defined as the warping that
-    minimises locally the sum of Fisher-Rao squared distances
-    :footcite:`srivastava+klassen_2016_statistical`.
+    minimises locally the sum of Fisher-Rao squared distances.
+    See Srivastava and Klassen's chapter, *"Statistical Modeling of Functional
+    Data"*, pp. 269-303\ :footcite:p:`srivastava+klassen_2016`
+    for a detailed explanation.
 
     .. math::
         \bar \gamma = argmin_{\gamma \in \Gamma} \sum_{i=1}^{n}
@@ -211,7 +213,8 @@ def fisher_rao_karcher_mean(
     equivalence class which makes the mean of the warpings employed be the
     identity.
 
-    See :footcite:`srivastava+klassen_2016_statistical` and
+    See Srivastava and Klassen's chapter, *"Statistical Modeling of Functional
+    Data"*, pp. 269-303\ :footcite:p:`srivastava+klassen_2016` and 
     :footcite:`srivastava++_2011_registration`.
 
     Args: