diff --git a/.buildinfo b/.buildinfo
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--- /dev/null
+++ b/.buildinfo
@@ -0,0 +1,4 @@
+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 5a8a84bd6f586082e026f00c7c5b76ef
+tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/.nojekyll b/.nojekyll
new file mode 100644
index 00000000..e69de29b
diff --git a/_sources/api/algorithms.rst.txt b/_sources/api/algorithms.rst.txt
new file mode 100644
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--- /dev/null
+++ b/_sources/api/algorithms.rst.txt
@@ -0,0 +1,6 @@
+fedeca.algorithms
+=========================
+
+.. currentmodule:: fedeca.algorithms
+
+.. autoclass:: fedeca.algorithms.TorchWebDiscoAlgo
diff --git a/_sources/api/competitors.rst.txt b/_sources/api/competitors.rst.txt
new file mode 100644
index 00000000..bd08e3eb
--- /dev/null
+++ b/_sources/api/competitors.rst.txt
@@ -0,0 +1,8 @@
+fedeca.competitors
+=========================
+
+.. autoclass:: fedeca.PooledIPTW
+
+.. autoclass:: fedeca.MatchingAjudsted
+
+.. autoclass:: fedeca.NaiveComparison
diff --git a/_sources/api/iptw.rst.txt b/_sources/api/iptw.rst.txt
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--- /dev/null
+++ b/_sources/api/iptw.rst.txt
@@ -0,0 +1,4 @@
+fedeca.fedeca_core
+=========================
+
+.. autoclass:: fedeca.FedECA
diff --git a/_sources/api/metrics.rst.txt b/_sources/api/metrics.rst.txt
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--- /dev/null
+++ b/_sources/api/metrics.rst.txt
@@ -0,0 +1,4 @@
+fedeca.metrics
+=========================
+
+.. automodule:: fedeca.metrics.metrics
diff --git a/_sources/api/scripts.rst.txt b/_sources/api/scripts.rst.txt
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index 00000000..7bb4b48e
--- /dev/null
+++ b/_sources/api/scripts.rst.txt
@@ -0,0 +1,4 @@
+fedeca.scripts
+=========================
+
+.. autoclass:: fedeca.scripts.substra_assets.csv_opener.CSVOpener
diff --git a/_sources/api/strategies.rst.txt b/_sources/api/strategies.rst.txt
new file mode 100644
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--- /dev/null
+++ b/_sources/api/strategies.rst.txt
@@ -0,0 +1,12 @@
+fedeca.strategies
+=========================
+
+.. currentmodule:: fedeca.strategies.webdisco
+
+.. autoclass:: fedeca.strategies.WebDisco
+
+.. automodule:: fedeca.strategies.bootstraper
+
+.. automodule:: fedeca.strategies.webdisco_utils
+
+
diff --git a/_sources/api/utils.rst.txt b/_sources/api/utils.rst.txt
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--- /dev/null
+++ b/_sources/api/utils.rst.txt
@@ -0,0 +1,14 @@
+fedeca.utils
+=========================
+
+.. automodule:: fedeca.utils.data_utils
+
+.. automodule:: fedeca.utils.experiments_utils
+
+.. automodule:: fedeca.utils.moments_utils
+
+.. automodule:: fedeca.utils.substrafl_utils
+
+.. automodule:: fedeca.utils.tensor_utils
+
+.. automodule:: fedeca.utils.typing
diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt
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--- /dev/null
+++ b/_sources/index.rst.txt
@@ -0,0 +1,67 @@
+FedECA documentation
+======================
+
+This package allows to perform both simulations and deployments of federated
+external control arms (FedECA) analyses.
+
+Before using this code make sure to: 
+
+#. read and accept the terms of the license license.md that can be found at the root of the repository.
+#. read `substra's privacy strategy <https://docs.substra.org/en/stable/additional/privacy-strategy.html>`_
+#. read our `companion article <https://arxiv.org/abs/2311.16984>`_
+#. `activate secure rng in Opacus <https://opacus.ai/docs/faq#:~:text=What%20is%20the%20secure_rng,the%20security%20this%20brings.>`_ if you plan on using differential privacy.
+
+
+
+Citing this work
+----------------
+
+::
+
+  @ARTICLE{terrail2023fedeca,
+       author = {{Ogier du Terrail}, Jean and {Klopfenstein}, Quentin and {Li}, Honghao and {Mayer}, Imke and {Loiseau}, Nicolas and {Hallal}, Mohammad and {Debouver}, Michael and {Camalon}, Thibault and {Fouqueray}, Thibault and {Arellano Castro}, Jorge and {Yanes}, Zahia and {Dahan}, Laetitia and {Ta{\"\i}eb}, Julien and {Laurent-Puig}, Pierre and {Bachet}, Jean-Baptiste and {Zhao}, Shulin and {Nicolle}, Remy and {Cros}, J{\'e}rome and {Gonzalez}, Daniel and {Carreras-Torres}, Robert and {Garcia Velasco}, Adelaida and {Abdilleh}, Kawther and {Doss}, Sudheer and {Balazard}, F{\'e}lix and {Andreux}, Mathieu},
+       title = "{FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings}",
+       journal = {arXiv e-prints},
+       keywords = {Statistics - Methodology, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning},
+       year = 2023,
+       month = nov,
+       eid = {arXiv:2311.16984},
+       pages = {arXiv:2311.16984},
+       doi = {10.48550/arXiv.2311.16984},
+       archivePrefix = {arXiv},
+       eprint = {2311.16984},
+       primaryClass = {stat.ME},
+       adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv231116984O},
+       adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+  }
+
+
+License
+-------
+
+FedECA is released under a custom license that can be found under license.md at the root of the repository.
+
+.. toctree::
+   :maxdepth: 0
+   :caption: Installation
+   
+   installation
+
+.. toctree::
+   :maxdepth: 0
+   :caption: Getting Started Instructions
+   
+   quickstart
+
+.. toctree::
+   :hidden:
+   :maxdepth: 4
+   :caption: API
+
+   api/fedeca
+   api/competitors
+   api/algorithms
+   api/metrics
+   api/scripts
+   api/strategies
+   api/utils
diff --git a/_sources/installation.rst.txt b/_sources/installation.rst.txt
new file mode 100644
index 00000000..a3281c00
--- /dev/null
+++ b/_sources/installation.rst.txt
@@ -0,0 +1,22 @@
+
+Installation
+============
+
+To install the package, create an env with python ``3.9`` with conda
+
+.. code-block:: bash
+
+   conda create -n fedeca python=3.9
+   conda activate fedeca
+
+Within the environment, install the package by running:
+
+.. code-block::
+
+   git clone https://github.com/owkin/fedeca.git
+   pip install -e ".[all_extra]"
+
+If you plan developing, you should also install the pre-commit hooks
+
+```bash
+pre-commit install
diff --git a/_sources/quickstart.rst.txt b/_sources/quickstart.rst.txt
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+++ b/_sources/quickstart.rst.txt
@@ -0,0 +1,178 @@
+
+Quickstart
+----------
+
+FedECA tries to mimic scikit-learn API as much as possible with the constraints
+of distributed learning.
+The first step in data science is always the data.
+We need to first use or generate some survival data in pandas.dataframe format.
+Note that fedeca should work on any data format, provided that the
+return type of the substra opener is indeed a pandas.dataframe but let's keep
+it simple in this quickstart.
+
+Here we will use fedeca utils which will generate some synthetic survival data
+following CoxPH assumptions:
+
+.. code-block:: python
+
+   import pandas as pd
+   from fedeca.utils.survival_utils import CoxData
+   # Let's generate 1000 data samples with 10 covariates
+   data = CoxData(seed=42, n_samples=1000, ndim=10)
+   df = data.generate_dataframe()
+
+   # We remove the true propensity score
+   df = df.drop(columns=["propensity_scores"], axis=1)
+
+Let's inspect the data that we have here.
+
+.. code-block:: python
+
+   print(df.info())
+   # <class 'pandas.core.frame.DataFrame'>
+   # RangeIndex: 1000 entries, 0 to 999
+   # Data columns (total 13 columns):
+   #  #   Column     Non-Null Count  Dtype
+   # ---  ------     --------------  -----
+   #  0   X_0        1000 non-null   float64
+   #  1   X_1        1000 non-null   float64
+   #  2   X_2        1000 non-null   float64
+   #  3   X_3        1000 non-null   float64
+   #  4   X_4        1000 non-null   float64
+   #  5   X_5        1000 non-null   float64
+   #  6   X_6        1000 non-null   float64
+   #  7   X_7        1000 non-null   float64
+   #  8   X_8        1000 non-null   float64
+   #  9   X_9        1000 non-null   float64
+   #  10  time       1000 non-null   float64
+   #  11  event      1000 non-null   uint8
+   #  12  treatment  1000 non-null   uint8
+   # dtypes: float64(11), uint8(2)
+   # memory usage: 88.0 KB
+   print(df.head())
+   #         X_0       X_1       X_2       X_3       X_4       X_5       X_6       X_7       X_8       X_9      time  event  treatment
+   # 0 -0.918373 -0.814340 -0.148994  0.482720 -1.130384 -1.254769 -0.462002  1.451622  1.199705  0.133197  2.573516      1          1
+   # 1  0.360051 -0.863619  0.198673  0.330630 -0.189184 -0.802424 -1.694990 -0.989009 -0.421245 -0.112665  0.519108      1          1
+   # 2  0.442502  0.024682  0.069500 -0.398015 -0.521236 -0.824907  0.373018  1.016843  0.765661  0.858817  0.652803      1          1
+   # 3 -0.783965 -1.116391 -1.482413 -2.039827 -1.639304 -0.500380 -0.298467 -1.801688 -0.743004 -0.724039  0.074925      1          1
+   # 4 -0.199620 -0.652347 -0.018776  0.004630 -0.122242 -0.413490 -0.450718 -0.761894 -1.323135 -0.234899  0.006951      1          1
+   print(df["treatment"].unique())
+   # array([1, 0], dtype=uint8)
+   df["treatment"].sum()
+   # 500
+
+So we have survival data with covariates and a binary treatment variable.
+Let's inspect it using proper survival plots using the great survival analysis
+package `lifelines <https://github.com/CamDavidsonPilon/lifelines>`_ that was a
+source of inspiration for fedeca:
+
+.. code-block:: python
+
+   from lifelines import KaplanMeierFitter as KMF
+   import matplotlib.pyplot as plt
+   treatments = [0, 1]
+   kms = [KMF().fit(durations=df.loc[df["treatment"] == t]["time"], event_observed=df.loc[df["treatment"] == t]["event"]) for t in treatments]
+
+   axs = [km.plot(label="treated" if t == 1 else "untreated") for km, t in zip(kms, treatments)]
+   axs[-1].set_ylabel("Survival Probability")
+   plt.xlim(0, 1500)
+   plt.savefig("treated_vs_untreated.pdf", bbox_inches="tight")
+
+Open ``treated_vs_untreated.pdf`` in your favorite pdf viewer and see for yourself.
+
+Pooled IPTW analysis
+--------------------
+
+The treatment seems to improve survival but it's hard to say for sure as it might
+simply be due to chance or sampling bias.
+Let's perform an IPTW analysis to be sure:
+
+.. code-block:: python
+
+   from fedeca.competitors import PooledIPTW
+   pooled_iptw = PooledIPTW(treated_col="treatment", event_col="event", duration_col="time")
+   # Targets is the propensity weights
+   pooled_iptw.fit(data=df, targets=None)
+   print(pooled_iptw.results_)
+   #                coef  exp(coef)  se(coef)  coef lower 95%  coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%  cmp to         z         p  -log2(p)
+   # covariate
+   # treatment  0.041727    1.04261  0.070581       -0.096609        0.180064             0.907911             1.197294     0.0  0.591196  0.554389   0.85103
+
+When looking at the ``p-value=0.554389 > 0.05``\ , thus judging by what we observe we
+cannot say for sure that there is a treatment effect. We say the ATE is non significant.
+
+Distributed Analysis
+--------------------
+
+However in practice data is private and held by different institutions. Therefore
+in practice each client holds a subset of the rows of our dataframe.
+We will simulate this using a realistic scenario where a "pharma" node is developing
+a new drug and thus holds all treated and the rest of the data is split across
+3 other institutions where patients were treated with the old drug.
+We will use the split utils of FedECA.
+
+.. code-block:: python
+
+   from fedeca.utils.data_utils import split_dataframe_across_clients
+
+   clients, train_data_nodes, _, _, _ = split_dataframe_across_clients(
+       df,
+       n_clients=4,
+       split_method= "split_control_over_centers",
+       split_method_kwargs={"treatment_info": "treatment"},
+       data_path="./data",
+       backend_type="simu",
+   )
+
+Note that you can replace split_method by any callable with the signature
+``pd.DataFrame -> list[int]`` where the list of ints is the split of the indices
+of the df across the different institutions.
+To convince you that the split was effective you can inspect the folder "./data".
+You will find different subfolders ``center0`` to ``center3`` each with different
+parts of the data.
+To unpack a bit what is going on in more depth, we have created a dict of client
+'clients',
+which is a dict with 4 keys containing substra API handles towards the different
+institutions and their data.
+``train_data_nodes`` is a list of handles towards the datasets of the different institutions
+that were registered through the substra interface using the data in the different
+folders.
+You might have noticed that we did not talk about the ``backend_type`` argument. 
+This argument is used to choose on which network will experiments be run.
+"simu" means in-RAM. If you finish this tutorial do try other values such as:
+"docker" or "subprocess" but expect a significant slow-down as experiments
+get closer and closer to a real distributed system.
+
+Now let's try to see if we can reproduce the pooled anaysis in this much more
+complicated distributed setting:
+
+.. code-block:: python
+
+   from fedeca import FedECA
+   # We use the first client as the node, which launches order
+   ds_client = clients[list(clients.keys())[0]]
+   fed_iptw = FedECA(ndim=10, ds_client=ds_client, train_data_nodes=train_data_nodes, treated_col="treatment", duration_col="time", event_col="event", variance_method="robust")
+   fed_iptw.run()
+   # Final partial log-likelihood:
+   # [-11499.19619422]
+   #        coef  se(coef)  coef lower 95%  coef upper 95%         z         p  exp(coef)  exp(coef) lower 95%  exp(coef) upper 95%
+   # 0  0.041718  0.070581       -0.096618        0.180054  0.591062  0.554479     1.0426             0.907902             1.197282
+
+In fact what we did above is both quite verbose. For simulation purposes we
+advise to use directly the scikit-learn inspired syntax:
+
+.. code-block:: python
+
+   from fedeca import FedECA
+
+   fed_iptw = FedECA(ndim=10, treated_col="treatment", event_col="event", duration_col="time")
+   fed_iptw.fit(df, n_clients=4, split_method="split_control_over_centers", split_method_kwargs={"treatment_info": "treatment"}, data_path="./data", variance_method="robust", backend_type="simu")
+   #        coef  se(coef)  coef lower 95%  coef upper 95%         z         p  exp(coef)  exp(coef) lower 95%  exp(coef) upper 95%
+   # 0  0.041718  0.070581       -0.096618        0.180054  0.591062  0.554479     1.0426             0.907902             1.197282
+
+We find a similar p-value ! The distributed analysis is working as expected.
+We recommend to users that made it to here as a next step to use their own data
+and write custom split functions and to test this pipeline under various
+heterogeneity settings.
+Another interesting avenue is to try adding differential privacy to the training
+of the propensity model but that is outside the scope of this quickstart. 
diff --git a/_static/basic.css b/_static/basic.css
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+++ b/_static/basic.css
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+    overflow-x: auto;
+}
+
+p.sidebar-title {
+    font-weight: bold;
+}
+
+nav.contents,
+aside.topic,
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+
+nav.contents,
+aside.topic,
+div.topic {
+    border: 1px solid #ccc;
+    padding: 7px;
+    margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+    font-size: 1.1em;
+    font-weight: bold;
+    margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    padding: 7px;
+}
+
+div.admonition dt {
+    font-weight: bold;
+}
+
+p.admonition-title {
+    margin: 0px 10px 5px 0px;
+    font-weight: bold;
+}
+
+div.body p.centered {
+    text-align: center;
+    margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+aside.sidebar > :last-child,
+nav.contents > :last-child,
+aside.topic > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+    margin-bottom: 0;
+}
+
+div.sidebar::after,
+aside.sidebar::after,
+nav.contents::after,
+aside.topic::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+    display: block;
+    content: '';
+    clear: both;
+}
+
+/* -- tables ---------------------------------------------------------------- */
+
+table.docutils {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    border: 0;
+    border-collapse: collapse;
+}
+
+table.align-center {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.align-default {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table caption span.caption-number {
+    font-style: italic;
+}
+
+table caption span.caption-text {
+}
+
+table.docutils td, table.docutils th {
+    padding: 1px 8px 1px 5px;
+    border-top: 0;
+    border-left: 0;
+    border-right: 0;
+    border-bottom: 1px solid #aaa;
+}
+
+th {
+    text-align: left;
+    padding-right: 5px;
+}
+
+table.citation {
+    border-left: solid 1px gray;
+    margin-left: 1px;
+}
+
+table.citation td {
+    border-bottom: none;
+}
+
+th > :first-child,
+td > :first-child {
+    margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+    margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure, figure {
+    margin: 0.5em;
+    padding: 0.5em;
+}
+
+div.figure p.caption, figcaption {
+    padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
+    font-style: italic;
+}
+
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+    border: 0 !important;
+}
+
+.field-list ul {
+    margin: 0;
+    padding-left: 1em;
+}
+
+.field-list p {
+    margin: 0;
+}
+
+.field-name {
+    -moz-hyphens: manual;
+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+    margin: 1em 0;
+}
+
+table.hlist td {
+    vertical-align: top;
+}
+
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+.sig-name {
+	font-size: 1.1em;
+}
+
+code.descname {
+    font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+    background-color: transparent;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
+.sig-param.n {
+	font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+    list-style: decimal;
+}
+
+ol.loweralpha {
+    list-style: lower-alpha;
+}
+
+ol.upperalpha {
+    list-style: upper-alpha;
+}
+
+ol.lowerroman {
+    list-style: lower-roman;
+}
+
+ol.upperroman {
+    list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+    margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+    margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+    margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+    margin-top: 0;
+}
+
+ol.simple p,
+ul.simple p {
+    margin-bottom: 0;
+}
+
+aside.footnote > span,
+div.citation > span {
+    float: left;
+}
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+  padding-right: 0.5em;
+}
+aside.footnote > p {
+  margin-left: 2em;
+}
+div.citation > p {
+  margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
+    margin-bottom: 0em;
+}
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
+    content: "";
+    clear: both;
+}
+
+dl.field-list {
+    display: grid;
+    grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+    font-weight: bold;
+    word-break: break-word;
+    padding-left: 0.5em;
+    padding-right: 5px;
+}
+
+dl.field-list > dd {
+    padding-left: 0.5em;
+    margin-top: 0em;
+    margin-left: 0em;
+    margin-bottom: 0em;
+}
+
+dl {
+    margin-bottom: 15px;
+}
+
+dd > :first-child {
+    margin-top: 0px;
+}
+
+dd ul, dd table {
+    margin-bottom: 10px;
+}
+
+dd {
+    margin-top: 3px;
+    margin-bottom: 10px;
+    margin-left: 30px;
+}
+
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+    margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+    background-color: #fbe54e;
+}
+
+rect.highlighted {
+    fill: #fbe54e;
+}
+
+dl.glossary dt {
+    font-weight: bold;
+    font-size: 1.1em;
+}
+
+.versionmodified {
+    font-style: italic;
+}
+
+.system-message {
+    background-color: #fda;
+    padding: 5px;
+    border: 3px solid red;
+}
+
+.footnote:target  {
+    background-color: #ffa;
+}
+
+.line-block {
+    display: block;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.line-block .line-block {
+    margin-top: 0;
+    margin-bottom: 0;
+    margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
+    font-family: sans-serif;
+}
+
+.accelerator {
+    text-decoration: underline;
+}
+
+.classifier {
+    font-style: oblique;
+}
+
+.classifier:before {
+    font-style: normal;
+    margin: 0 0.5em;
+    content: ":";
+    display: inline-block;
+}
+
+abbr, acronym {
+    border-bottom: dotted 1px;
+    cursor: help;
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
+span.pre {
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+    white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+    margin: 1em 0;
+}
+
+td.linenos pre {
+    border: 0;
+    background-color: transparent;
+    color: #aaa;
+}
+
+table.highlighttable {
+    display: block;
+}
+
+table.highlighttable tbody {
+    display: block;
+}
+
+table.highlighttable tr {
+    display: flex;
+}
+
+table.highlighttable td {
+    margin: 0;
+    padding: 0;
+}
+
+table.highlighttable td.linenos {
+    padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+    flex: 1;
+    overflow: hidden;
+}
+
+.highlight .hll {
+    display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+    margin: 0;
+}
+
+div.code-block-caption + div {
+    margin-top: 0;
+}
+
+div.code-block-caption {
+    margin-top: 1em;
+    padding: 2px 5px;
+    font-size: small;
+}
+
+div.code-block-caption code {
+    background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
+}
+
+div.code-block-caption span.caption-number {
+    padding: 0.1em 0.3em;
+    font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
+
+div.literal-block-wrapper {
+    margin: 1em 0;
+}
+
+code.xref, a code {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+    background-color: transparent;
+}
+
+.viewcode-link {
+    float: right;
+}
+
+.viewcode-back {
+    float: right;
+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    margin: -1px -10px;
+    padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+    vertical-align: middle;
+}
+
+div.body div.math p {
+    text-align: center;
+}
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+    div.document,
+    div.documentwrapper,
+    div.bodywrapper {
+        margin: 0 !important;
+        width: 100%;
+    }
+
+    div.sphinxsidebar,
+    div.related,
+    div.footer,
+    #top-link {
+        display: none;
+    }
+}
\ No newline at end of file
diff --git a/_static/css/badge_only.css b/_static/css/badge_only.css
new file mode 100644
index 00000000..323730ae
--- /dev/null
+++ b/_static/css/badge_only.css
@@ -0,0 +1 @@
+.fa:before{-webkit-font-smoothing:antialiased}.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-weight:normal;font-style:normal;src:url("../fonts/fontawesome-webfont.eot");src:url("../fonts/fontawesome-webfont.eot?#iefix") format("embedded-opentype"),url("../fonts/fontawesome-webfont.woff") format("woff"),url("../fonts/fontawesome-webfont.ttf") format("truetype"),url("../fonts/fontawesome-webfont.svg#FontAwesome") format("svg")}.fa:before{display:inline-block;font-family:FontAwesome;font-style:normal;font-weight:normal;line-height:1;text-decoration:inherit}a .fa{display:inline-block;text-decoration:inherit}li .fa{display:inline-block}li .fa-large:before,li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-0.8em}ul.fas li .fa{width:.8em}ul.fas li .fa-large:before,ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before{content:""}.icon-book:before{content:""}.fa-caret-down:before{content:""}.icon-caret-down:before{content:""}.fa-caret-up:before{content:""}.icon-caret-up:before{content:""}.fa-caret-left:before{content:""}.icon-caret-left:before{content:""}.fa-caret-right:before{content:""}.icon-caret-right:before{content:""}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;font-family:"Lato","proxima-nova","Helvetica Neue",Arial,sans-serif;z-index:400}.rst-versions a{color:#2980B9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27AE60;*zoom:1}.rst-versions .rst-current-version:before,.rst-versions .rst-current-version:after{display:table;content:""}.rst-versions .rst-current-version:after{clear:both}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book{float:left}.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#E74C3C;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#F1C40F;color:#000}.rst-versions.shift-up{height:auto;max-height:100%}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:gray;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:solid 1px #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px}.rst-versions.rst-badge .icon-book{float:none}.rst-versions.rst-badge .fa-book{float:none}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book{float:left}.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge .rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width: 768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}
diff --git a/_static/css/theme.css b/_static/css/theme.css
new file mode 100644
index 00000000..b19dbfe5
--- /dev/null
+++ b/_static/css/theme.css
@@ -0,0 +1,6 @@
+/* sphinx_rtd_theme version 0.4.2 | MIT license */
+/* Built 20181005 13:10 */
+*{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block}audio,canvas,video{display:inline-block;*display:inline;*zoom:1}audio:not([controls]){display:none}[hidden]{display:none}*{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}body{margin:0}a:hover,a:active{outline:0}abbr[title]{border-bottom:1px dotted}b,strong{font-weight:bold}blockquote{margin:0}dfn{font-style:italic}ins{background:#ff9;color:#000;text-decoration:none}mark{background:#ff0;color:#000;font-style:italic;font-weight:bold}pre,code,.rst-content tt,.rst-content code,kbd,samp{font-family:monospace,serif;_font-family:"courier 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diff --git a/_static/doctools.js b/_static/doctools.js
new file mode 100644
index 00000000..d06a71d7
--- /dev/null
+++ b/_static/doctools.js
@@ -0,0 +1,156 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
+  }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
+  },
+
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
+  },
+
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
+  },
+
+  /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
+  },
+
+  /**
+   * Initialise the domain index toggle buttons
+   */
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  },
+
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
+            }
+            break;
+        }
+      }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
+    });
+  },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/_static/documentation_options.js b/_static/documentation_options.js
new file mode 100644
index 00000000..48f5f06b
--- /dev/null
+++ b/_static/documentation_options.js
@@ -0,0 +1,14 @@
+var DOCUMENTATION_OPTIONS = {
+    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
+    VERSION: 'fa9652cb6090a9a41b7cae679e71f929a1e4dd42',
+    LANGUAGE: 'en',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
diff --git a/_static/favicon.ico b/_static/favicon.ico
new file mode 100644
index 00000000..b20ed84b
Binary files /dev/null and b/_static/favicon.ico differ
diff --git a/_static/file.png b/_static/file.png
new file mode 100644
index 00000000..a858a410
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diff --git a/_static/fonts.css b/_static/fonts.css
new file mode 100644
index 00000000..0f048c33
--- /dev/null
+++ b/_static/fonts.css
@@ -0,0 +1,35 @@
+@font-face {
+    font-family: "averta";
+    src: url("./Averta/AvertaDemo-Regular.otf") format("truetype");
+}
+
+@font-face {
+    font-family: "averta";
+    src: url("./Averta/AvertaDemo-Bold.otf") format("truetype");
+    font-weight: bold;
+}
+
+@font-face {
+    font-family: "averta";
+    src: url("./Averta/AvertaDemo-ExtraBoldItalic.otf") format("truetype");
+    font-weight: bolder;
+    font-style: italic;
+}
+
+@font-face {
+    font-family: "futuralt";
+    src: url("./Futura/FuturaLT.ttf") format("truetype");
+}
+
+@font-face {
+    font-family: "futuralt";
+    src: url("./Futura/FuturaLT-Bold.ttf") format("truetype");
+    font-weight: bold;
+}
+
+@font-face {
+    font-family: "futuralt";
+    src: url("./Futura/FuturaLT-Heavy.otf") format("truetype");
+    font-weight: bolder;
+    font-style: italic;
+}
diff --git a/_static/fonts/Inconsolata-Bold.ttf b/_static/fonts/Inconsolata-Bold.ttf
new file mode 100644
index 00000000..809c1f58
Binary files /dev/null and b/_static/fonts/Inconsolata-Bold.ttf differ
diff --git a/_static/fonts/Inconsolata-Regular.ttf b/_static/fonts/Inconsolata-Regular.ttf
new file mode 100644
index 00000000..fc981ce7
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diff --git a/_static/fonts/Inconsolata.ttf b/_static/fonts/Inconsolata.ttf
new file mode 100644
index 00000000..4b8a36d2
Binary files /dev/null and b/_static/fonts/Inconsolata.ttf differ
diff --git a/_static/fonts/Lato-Bold.ttf b/_static/fonts/Lato-Bold.ttf
new file mode 100644
index 00000000..1d23c706
Binary files /dev/null and b/_static/fonts/Lato-Bold.ttf differ
diff --git a/_static/fonts/Lato-Regular.ttf b/_static/fonts/Lato-Regular.ttf
new file mode 100644
index 00000000..0f3d0f83
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diff --git a/_static/fonts/Lato/lato-bold.eot b/_static/fonts/Lato/lato-bold.eot
new file mode 100644
index 00000000..3361183a
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diff --git a/_static/fonts/Lato/lato-bold.ttf b/_static/fonts/Lato/lato-bold.ttf
new file mode 100644
index 00000000..29f691d5
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diff --git a/_static/fonts/Lato/lato-bold.woff b/_static/fonts/Lato/lato-bold.woff
new file mode 100644
index 00000000..c6dff51f
Binary files /dev/null and b/_static/fonts/Lato/lato-bold.woff differ
diff --git a/_static/fonts/Lato/lato-bold.woff2 b/_static/fonts/Lato/lato-bold.woff2
new file mode 100644
index 00000000..bb195043
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diff --git a/_static/fonts/Lato/lato-bolditalic.eot b/_static/fonts/Lato/lato-bolditalic.eot
new file mode 100644
index 00000000..3d415493
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diff --git a/_static/fonts/Lato/lato-bolditalic.ttf b/_static/fonts/Lato/lato-bolditalic.ttf
new file mode 100644
index 00000000..f402040b
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diff --git a/_static/fonts/Lato/lato-bolditalic.woff b/_static/fonts/Lato/lato-bolditalic.woff
new file mode 100644
index 00000000..88ad05b9
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diff --git a/_static/fonts/Lato/lato-bolditalic.woff2 b/_static/fonts/Lato/lato-bolditalic.woff2
new file mode 100644
index 00000000..c4e3d804
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diff --git a/_static/fonts/Lato/lato-italic.eot b/_static/fonts/Lato/lato-italic.eot
new file mode 100644
index 00000000..3f826421
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diff --git a/_static/fonts/Lato/lato-italic.ttf b/_static/fonts/Lato/lato-italic.ttf
new file mode 100644
index 00000000..b4bfc9b2
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diff --git a/_static/fonts/Lato/lato-italic.woff b/_static/fonts/Lato/lato-italic.woff
new file mode 100644
index 00000000..76114bc0
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diff --git a/_static/fonts/Lato/lato-italic.woff2 b/_static/fonts/Lato/lato-italic.woff2
new file mode 100644
index 00000000..3404f37e
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diff --git a/_static/fonts/Lato/lato-regular.eot b/_static/fonts/Lato/lato-regular.eot
new file mode 100644
index 00000000..11e3f2a5
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diff --git a/_static/fonts/Lato/lato-regular.ttf b/_static/fonts/Lato/lato-regular.ttf
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diff --git a/_static/js/theme.js b/_static/js/theme.js
new file mode 100644
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+++ b/_static/js/theme.js
@@ -0,0 +1,3 @@
+/* sphinx_rtd_theme version 0.4.2 | MIT license */
+/* Built 20181005 13:10 */
+require=function r(s,a,l){function c(e,n){if(!a[e]){if(!s[e]){var i="function"==typeof require&&require;if(!n&&i)return i(e,!0);if(u)return u(e,!0);var t=new Error("Cannot find module '"+e+"'");throw t.code="MODULE_NOT_FOUND",t}var o=a[e]={exports:{}};s[e][0].call(o.exports,function(n){return c(s[e][1][n]||n)},o,o.exports,r,s,a,l)}return a[e].exports}for(var u="function"==typeof require&&require,n=0;n<l.length;n++)c(l[n]);return c}({"sphinx-rtd-theme":[function(n,e,i){var jQuery="undefined"!=typeof window?window.jQuery:n("jquery");e.exports.ThemeNav={navBar:null,win:null,winScroll:!1,winResize:!1,linkScroll:!1,winPosition:0,winHeight:null,docHeight:null,isRunning:!1,enable:function(e){var i=this;void 0===e&&(e=!0),i.isRunning||(i.isRunning=!0,jQuery(function(n){i.init(n),i.reset(),i.win.on("hashchange",i.reset),e&&i.win.on("scroll",function(){i.linkScroll||i.winScroll||(i.winScroll=!0,requestAnimationFrame(function(){i.onScroll()}))}),i.win.on("resize",function(){i.winResize||(i.winResize=!0,requestAnimationFrame(function(){i.onResize()}))}),i.onResize()}))},enableSticky:function(){this.enable(!0)},init:function(i){i(document);var t=this;this.navBar=i("div.wy-side-scroll:first"),this.win=i(window),i(document).on("click","[data-toggle='wy-nav-top']",function(){i("[data-toggle='wy-nav-shift']").toggleClass("shift"),i("[data-toggle='rst-versions']").toggleClass("shift")}).on("click",".wy-menu-vertical .current ul li a",function(){var n=i(this);i("[data-toggle='wy-nav-shift']").removeClass("shift"),i("[data-toggle='rst-versions']").toggleClass("shift"),t.toggleCurrent(n),t.hashChange()}).on("click","[data-toggle='rst-current-version']",function(){i("[data-toggle='rst-versions']").toggleClass("shift-up")}),i("table.docutils:not(.field-list,.footnote,.citation)").wrap("<div class='wy-table-responsive'></div>"),i("table.docutils.footnote").wrap("<div class='wy-table-responsive footnote'></div>"),i("table.docutils.citation").wrap("<div class='wy-table-responsive citation'></div>"),i(".wy-menu-vertical ul").not(".simple").siblings("a").each(function(){var e=i(this);expand=i('<span class="toctree-expand"></span>'),expand.on("click",function(n){return t.toggleCurrent(e),n.stopPropagation(),!1}),e.prepend(expand)})},reset:function(){var n=encodeURI(window.location.hash)||"#";try{var e=$(".wy-menu-vertical"),i=e.find('[href="'+n+'"]');if(0===i.length){var t=$('.document [id="'+n.substring(1)+'"]').closest("div.section");0===(i=e.find('[href="#'+t.attr("id")+'"]')).length&&(i=e.find('[href="#"]'))}0<i.length&&($(".wy-menu-vertical .current").removeClass("current"),i.addClass("current"),i.closest("li.toctree-l1").addClass("current"),i.closest("li.toctree-l1").parent().addClass("current"),i.closest("li.toctree-l1").addClass("current"),i.closest("li.toctree-l2").addClass("current"),i.closest("li.toctree-l3").addClass("current"),i.closest("li.toctree-l4").addClass("current"))}catch(o){console.log("Error expanding nav for anchor",o)}},onScroll:function(){this.winScroll=!1;var n=this.win.scrollTop(),e=n+this.winHeight,i=this.navBar.scrollTop()+(n-this.winPosition);n<0||e>this.docHeight||(this.navBar.scrollTop(i),this.winPosition=n)},onResize:function(){this.winResize=!1,this.winHeight=this.win.height(),this.docHeight=$(document).height()},hashChange:function(){this.linkScroll=!0,this.win.one("hashchange",function(){this.linkScroll=!1})},toggleCurrent:function(n){var e=n.closest("li");e.siblings("li.current").removeClass("current"),e.siblings().find("li.current").removeClass("current"),e.find("> ul li.current").removeClass("current"),e.toggleClass("current")}},"undefined"!=typeof window&&(window.SphinxRtdTheme={Navigation:e.exports.ThemeNav,StickyNav:e.exports.ThemeNav}),function(){for(var r=0,n=["ms","moz","webkit","o"],e=0;e<n.length&&!window.requestAnimationFrame;++e)window.requestAnimationFrame=window[n[e]+"RequestAnimationFrame"],window.cancelAnimationFrame=window[n[e]+"CancelAnimationFrame"]||window[n[e]+"CancelRequestAnimationFrame"];window.requestAnimationFrame||(window.requestAnimationFrame=function(n,e){var i=(new Date).getTime(),t=Math.max(0,16-(i-r)),o=window.setTimeout(function(){n(i+t)},t);return r=i+t,o}),window.cancelAnimationFrame||(window.cancelAnimationFrame=function(n){clearTimeout(n)})}()},{jquery:"jquery"}]},{},["sphinx-rtd-theme"]);
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diff --git a/_static/language_data.js b/_static/language_data.js
new file mode 100644
index 00000000..250f5665
--- /dev/null
+++ b/_static/language_data.js
@@ -0,0 +1,199 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+
+
+/* Non-minified version is copied as a separate JS file, is available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
diff --git a/_static/minus.png b/_static/minus.png
new file mode 100644
index 00000000..d96755fd
Binary files /dev/null and b/_static/minus.png differ
diff --git a/_static/owkin.css b/_static/owkin.css
new file mode 100644
index 00000000..38041b21
--- /dev/null
+++ b/_static/owkin.css
@@ -0,0 +1,186 @@
+:root {
+    --color-blue: #3c73c4;
+    --color-dark-grey: #262c3c;
+
+    --font-title: "futuralt", Trebuchet MS, arial, sans-serif;
+
+    --font-size-xxl: 62px;
+    --font-size-xl: 4rem;
+    --font-size-large: 2.4rem;
+    --font-size-normal: 17px;
+    --font-size-small: 14px;
+}
+
+html {
+    font-size: 62.5%;
+}
+
+body {
+    font-family: "averta", "Helvetica Neue", helvetica, arial, sans-serif;
+    color: black;
+    text-align:justify;
+    font-size: var(--font-size-normal);
+}
+
+a {
+    color: var(--color-blue);
+}
+
+a:hover {
+    color: black;
+}
+
+h1, h2, h3,
+.rst-content .toctree-wrapper > p.caption {
+    font-family: var(--font-title);
+    font-weight: bolder;
+    color: var(--color-dark-grey);
+}
+
+h1 {
+    font-size: var(--font-size-xxl);
+    line-height: 1.1;
+    text-align: left;
+    /*text-transform: uppercase;*/
+}
+
+h2,
+.rst-content .toctree-wrapper > p.caption {
+    font-size: var(--font-size-xl);
+    line-height: 1.4;
+    margin: 6rem auto 3rem;
+}
+
+h3 {
+    font-size: var(--font-size-large);
+    line-height: 1.4;
+    margin: 4.5rem auto 2.5rem;
+}
+
+p, ul, ol {
+    font-size: var(--font-size-normal);
+    line-height: 1.4;
+    margin: 0 0 1.5em 0;
+}
+
+.wy-nav-content {
+    max-width: 900px;
+}
+
+@media screen and (min-width: 1100px) {
+    .wy-nav-content {
+        margin-left: 75px;
+        background-color: white;
+    }
+
+    .rst-content div[class^=highlight] {
+        max-width: calc(100vw - 300px - 2 * 75px - 3.236em);
+        min-width: 100%;
+        width: fit-content;
+        overflow-x: hidden;
+
+    }
+}
+
+.wy-side-nav-search,
+.wy-nav-content-wrap,
+.wy-body-for-nav,
+.wy-nav-side {
+    color: black;
+    background-color: white !important;
+}
+
+.wy-side-nav-search {
+    padding: 2.8rem 0 0 0;
+
+}
+
+
+.wy-breadcrumbs {
+    /*text-transform: uppercase;*/
+    font-weight: bold;
+    font-size: var(--font-size-small);
+}
+
+a,
+a:visited {
+    color: var(--color-blue);
+}
+
+a:hover,
+a:visited:hover {
+    color: black;
+}
+
+.btn-download {
+    display: flex;
+    max-width: 260px;
+    align-items: center;
+    text-transform: uppercase;
+    padding-bottom: 0.625rem;
+    border-bottom: 1px solid #f3f4f7;
+}
+
+.btn-download:hover {
+    border-bottom: 1px solid;
+}
+
+.btn-download-image {
+    height: 40px !important;
+    margin-right: 8px;
+}
+
+.btn {
+    display: inline-block;
+    padding: 8px 20px;
+    border-radius: 30px;
+    border: 1px solid;
+    text-transform: uppercase;
+    font-size: var(--font-size-small);
+    text-decoration: none;
+    margin: 0 10px 12px 0;
+    line-height: 2;
+    text-align: center;
+    color: var(--color-blue);
+}
+
+.btn:active {
+    padding: 8px 20px;
+}
+
+.btn-neutral,
+.btn-neutral:visited {
+    color: var(--color-blue) !important;
+    background-color: transparent !important;
+}
+
+.btn-neutral:hover,
+.btn-neutral:visited:hover {
+    color: black !important;
+    background-color: transparent !important;
+}
+
+.rst-content .admonition-title,
+.wy-alert-title {
+    font-size: var(--font-size-small);
+}
+
+html.writer-html4 .rst-content dl:not(.docutils) > dt,
+html.writer-html5 .rst-content dl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple) > dt {
+    font-size: var(--font-size-small);
+}
+
+html.writer-html5 .rst-content table.docutils td > p,
+html.writer-html5 .rst-content table.docutils th > p {
+    font-size: var(--font-size-small);
+}
+
+.wy-nav-top {
+    background-color: var(--color-dark-grey);
+}
+
+.wy-nav-top a,
+.wy-nav-top a:visited,
+.wy-nav-top a:hover {
+    color: white;
+}
diff --git a/_static/plus.png b/_static/plus.png
new file mode 100644
index 00000000..7107cec9
Binary files /dev/null and b/_static/plus.png differ
diff --git a/_static/pygments.css b/_static/pygments.css
new file mode 100644
index 00000000..84ab3030
--- /dev/null
+++ b/_static/pygments.css
@@ -0,0 +1,75 @@
+pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: #ffffcc }
+.highlight { background: #f8f8f8; }
+.highlight .c { color: #3D7B7B; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #008000; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #9C6500 } /* Comment.Preproc */
+.highlight .cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #3D7B7B; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
+.highlight .gr { color: #E40000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #008400 } /* Generic.Inserted */
+.highlight .go { color: #717171 } /* Generic.Output */
+.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #008000 } /* Keyword.Pseudo */
+.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #B00040 } /* Keyword.Type */
+.highlight .m { color: #666666 } /* Literal.Number */
+.highlight .s { color: #BA2121 } /* Literal.String */
+.highlight .na { color: #687822 } /* Name.Attribute */
+.highlight .nb { color: #008000 } /* Name.Builtin */
+.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
+.highlight .no { color: #880000 } /* Name.Constant */
+.highlight .nd { color: #AA22FF } /* Name.Decorator */
+.highlight .ni { color: #717171; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #0000FF } /* Name.Function */
+.highlight .nl { color: #767600 } /* Name.Label */
+.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #19177C } /* Name.Variable */
+.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #666666 } /* Literal.Number.Bin */
+.highlight .mf { color: #666666 } /* Literal.Number.Float */
+.highlight .mh { color: #666666 } /* Literal.Number.Hex */
+.highlight .mi { color: #666666 } /* Literal.Number.Integer */
+.highlight .mo { color: #666666 } /* Literal.Number.Oct */
+.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
+.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
+.highlight .sc { color: #BA2121 } /* Literal.String.Char */
+.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
+.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
+.highlight .se { color: #AA5D1F; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
+.highlight .si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */
+.highlight .sx { color: #008000 } /* Literal.String.Other */
+.highlight .sr { color: #A45A77 } /* Literal.String.Regex */
+.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
+.highlight .ss { color: #19177C } /* Literal.String.Symbol */
+.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #0000FF } /* Name.Function.Magic */
+.highlight .vc { color: #19177C } /* Name.Variable.Class */
+.highlight .vg { color: #19177C } /* Name.Variable.Global */
+.highlight .vi { color: #19177C } /* Name.Variable.Instance */
+.highlight .vm { color: #19177C } /* Name.Variable.Magic */
+.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/_static/searchtools.js b/_static/searchtools.js
new file mode 100644
index 00000000..97d56a74
--- /dev/null
+++ b/_static/searchtools.js
@@ -0,0 +1,566 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
+    // and returns the new score.
+    /*
+    score: result => {
+      const [docname, title, anchor, descr, score, filename] = result
+      return score
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2,
+  };
+}
+
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+
+  const [docName, title, anchor, descr, score, _filename] = item;
+
+  let listItem = document.createElement("li");
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = docUrlRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = docUrlRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr)
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms)
+          );
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = _(
+      `Search finished, found ${resultCount} page(s) matching the search query.`
+    );
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms),
+      5
+    );
+  }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent !== undefined) return docContent.textContent;
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
+    );
+    return "";
+  },
+
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
+  },
+
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
+
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
+    }
+  },
+
+  hasIndex: () => Search._index !== null,
+
+  deferQuery: (query) => (Search._queued_query = query),
+
+  stopPulse: () => (Search._pulse_status = -1),
+
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch: (query) => {
+    // create the required interface elements
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  query: (query) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
+
+      // stem the word
+      let word = stemmer.stemWord(queryTermLower);
+      // select the correct list
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      else {
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
+      }
+    });
+
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
+
+    // array of [docname, title, anchor, descr, score, filename]
+    let results = [];
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          let score = Math.round(100 * queryLower.length / title.length)
+          results.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id] of foundEntries) {
+          let score = Math.round(100 * queryLower.length / entry.length)
+          results.push([
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // lookup as object
+    objectTerms.forEach((term) =>
+      results.push(...Search.performObjectSearch(term, objectTerms))
+    );
+
+    // lookup as search terms in fulltext
+    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
+
+    // now sort the results by score (in opposite order of appearance, since the
+    // display function below uses pop() to retrieve items) and then
+    // alphabetically
+    results.sort((a, b) => {
+      const leftScore = a[4];
+      const rightScore = b[4];
+      if (leftScore === rightScore) {
+        // same score: sort alphabetically
+        const leftTitle = a[1].toLowerCase();
+        const rightTitle = b[1].toLowerCase();
+        if (leftTitle === rightTitle) return 0;
+        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+      }
+      return leftScore > rightScore ? 1 : -1;
+    });
+
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    results = results.reverse();
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    // console.info("search results:", Search.lastresults);
+
+    // print the results
+    _displayNextItem(results, results.length, searchTerms);
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
+      }
+
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
+    return results;
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+
+    const scoreMap = new Map();
+    const fileMap = new Map();
+
+    // perform the search on the required terms
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        const escapedWord = _escapeRegExp(word);
+        Object.keys(terms).forEach((term) => {
+          if (term.match(escapedWord) && !terms[word])
+            arr.push({ files: terms[term], score: Scorer.partialTerm });
+        });
+        Object.keys(titleTerms).forEach((term) => {
+          if (term.match(escapedWord) && !titleTerms[word])
+            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
+        });
+      }
+
+      // no match but word was a required one
+      if (arr.every((record) => record.files === undefined)) return;
+
+      // found search word in contents
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
+      });
+
+      // create the mapping
+      files.forEach((file) => {
+        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
+          fileMap.get(file).push(word);
+        else fileMap.set(file, [word]);
+      });
+    });
+
+    // now check if the files don't contain excluded terms
+    const results = [];
+    for (const [file, wordList] of fileMap) {
+      // check if all requirements are matched
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
+      if (
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
+
+      // ensure that none of the excluded terms is in the search result
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
+
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+      ]);
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words.
+   */
+  makeSearchSummary: (htmlText, keywords) => {
+    const text = Search.htmlToText(htmlText);
+    if (text === "") return null;
+
+    const textLower = text.toLowerCase();
+    const actualStartPosition = [...keywords]
+      .map((k) => textLower.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("p");
+    summary.classList.add("context");
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+    return summary;
+  },
+};
+
+_ready(Search.init);
diff --git a/_static/sidebar.css b/_static/sidebar.css
new file mode 100644
index 00000000..cb263504
--- /dev/null
+++ b/_static/sidebar.css
@@ -0,0 +1,92 @@
+.wy-side-nav-search > a {
+    font-family: var(--font-title);
+    font-weight: bolder;
+    color: var(--color-dark-grey);
+    display: flex;
+    flex-direction: column-reverse;
+    font-size: 2rem;
+    /*text-transform: uppercase;*/
+    align-items: center;
+    padding: 0;
+    margin-bottom: 0.5em;
+}
+
+.wy-side-nav-search .wy-dropdown>a img.logo,
+.wy-side-nav-search>a img.logo {
+    width: 200px;
+    margin: 0 !important;
+}
+
+.wy-side-nav-search > a:before {
+    display: none;
+}
+
+.wy-side-nav-search>div.version {
+    font-family: var(--font-title);
+    color: black;
+    font-size: 1.4rem;
+    font-weight: bolder;
+}
+
+.wy-side-nav-search input[type=text] {
+    padding: 8px 20px;
+    margin: 2.8rem 1rem 0 1rem;
+    border-radius: 30px;
+    border: 1px solid;
+    font-size: 17px;
+    text-decoration: none;
+    line-height: 2;
+    color: var(--color-blue);
+    width: calc(100% - 2rem);
+}
+
+.wy-menu-vertical header,
+.wy-menu-vertical p.caption {
+    font-size: 1.4rem;
+    margin-top: 2.8rem;
+    /*text-transform: uppercase;*/
+    color: black;
+    opacity: 0.4;
+}
+
+.wy-menu-vertical a,
+.wy-menu-vertical li.current a {
+    color: black;
+    text-align: left;
+    border-right: none;
+}
+.wy-menu-vertical a:hover,
+.wy-menu-vertical li.current a:hover {
+    background-color: transparent;
+}
+.wy-menu-vertical li.current,
+.wy-menu-vertical li.toctree-l1.current > a,
+.wy-menu-vertical li.toctree-l2.current > a,
+.wy-menu-vertical li.toctree-l2.current li.toctree-l3 > a {
+    color: var(--color-blue);
+    background-color: transparent;
+    border-bottom: none;
+    border-top: none;
+}
+
+.rst-versions {
+    background: var(--color-dark-grey);
+    color: white;
+    font-family: inherit;
+}
+
+.rst-versions .rst-current-version {
+    background-color: var(--color-dark-grey);
+    color: white;
+    font-size: var(--font-size-small);
+}
+
+.rst-versions .rst-other-versions {
+    background-color: var(--color-dark-grey);
+    color: white;
+    font-size: var(--font-size-small);
+}
+
+.rst-versions .rst-other-versions dd a {
+    color: white;
+}
diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js
new file mode 100644
index 00000000..aae669d7
--- /dev/null
+++ b/_static/sphinx_highlight.js
@@ -0,0 +1,144 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          document.createTextNode(val.substr(pos + text.length)),
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+            _("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(SphinxHighlight.highlightSearchWords);
+_ready(SphinxHighlight.initEscapeListener);
diff --git a/api/algorithms.html b/api/algorithms.html
new file mode 100644
index 00000000..8454a64a
--- /dev/null
+++ b/api/algorithms.html
@@ -0,0 +1,416 @@
+
+
+<!DOCTYPE html>
+<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
+<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
+<head>
+  <meta charset="utf-8">
+  <meta name="generator" content="Docutils 0.19: https://docutils.sourceforge.io/" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  
+  <title>fedeca.algorithms &mdash; FedECA fa9652cb6090a9a41b7cae679e71f929a1e4dd42 documentation</title>
+  
+
+  
+  
+  
+  
+
+  
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+  
+  
+    
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+  
+
+  <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
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+  <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
+  <link rel="stylesheet" href="../_static/fonts.css" type="text/css" />
+  <link rel="stylesheet" href="../_static/owkin.css" type="text/css" />
+  <link rel="stylesheet" href="../_static/sidebar.css" type="text/css" />
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="fedeca.metrics" href="metrics.html" />
+    <link rel="prev" title="fedeca.competitors" href="competitors.html" /> 
+
+  
+  <script src="../_static/js/modernizr.min.js"></script>
+
+</head>
+
+<body class="wy-body-for-nav">
+
+   
+  <div class="wy-grid-for-nav">
+
+    
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
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+          
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+          
+          </a>
+
+          
+            
+            
+              <div class="version">
+                fa9652cb6090a9a41b7cae679e71f929a1e4dd42
+              </div>
+            
+          
+
+          
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
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+        </div>
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+        <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
+          
+            
+            
+              
+            
+            
+              <p class="caption" role="heading"><span class="caption-text">Installation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Getting Started Instructions</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../quickstart.html">Quickstart</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quickstart.html#pooled-iptw-analysis">Pooled IPTW analysis</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quickstart.html#distributed-analysis">Distributed Analysis</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">API</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="competitors.html">fedeca.competitors</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">fedeca.algorithms</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.algorithms.TorchWebDiscoAlgo"><code class="docutils literal notranslate"><span class="pre">TorchWebDiscoAlgo</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.algorithms.TorchWebDiscoAlgo.compute_X_y_and_propensity_weights"><code class="docutils literal notranslate"><span class="pre">TorchWebDiscoAlgo.compute_X_y_and_propensity_weights()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.algorithms.TorchWebDiscoAlgo.compute_local_phi_stats"><code class="docutils literal notranslate"><span class="pre">TorchWebDiscoAlgo.compute_local_phi_stats()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.algorithms.TorchWebDiscoAlgo.local_uncentered_moments"><code class="docutils literal notranslate"><span class="pre">TorchWebDiscoAlgo.local_uncentered_moments()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.algorithms.TorchWebDiscoAlgo.predict"><code class="docutils literal notranslate"><span class="pre">TorchWebDiscoAlgo.predict()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.algorithms.TorchWebDiscoAlgo.summary"><code class="docutils literal notranslate"><span class="pre">TorchWebDiscoAlgo.summary()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.algorithms.TorchWebDiscoAlgo.train"><code class="docutils literal notranslate"><span class="pre">TorchWebDiscoAlgo.train()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.algorithms.TorchWebDiscoAlgo.strategies"><code class="docutils literal notranslate"><span class="pre">TorchWebDiscoAlgo.strategies</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="metrics.html">fedeca.metrics</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="utils.html">fedeca.utils</a></li>
+</ul>
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+  <section id="fedeca-algorithms">
+<h1>fedeca.algorithms<a class="headerlink" href="#fedeca-algorithms" title="Permalink to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="fedeca.algorithms.TorchWebDiscoAlgo">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">TorchWebDiscoAlgo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">duration_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'T'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">event_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'E'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">treated_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_step_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.95</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'lifelines'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standardize_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">penalizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l1_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">propensity_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'iptw'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cox_fit_cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">propensity_fit_cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_hessian</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_batch_norm_parameters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_gpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">robust</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.algorithms.TorchWebDiscoAlgo" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">TorchAlgo</span></code></p>
+<p>WebDiscoAlgo class.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>model</strong> (<em>Module</em>) – </p></li>
+<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>duration_col</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – </p></li>
+<li><p><strong>event_col</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – </p></li>
+<li><p><strong>treated_col</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – </p></li>
+<li><p><strong>initial_step_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – </p></li>
+<li><p><strong>learning_rate_strategy</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – </p></li>
+<li><p><strong>standardize_data</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – </p></li>
+<li><p><strong>tol</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – </p></li>
+<li><p><strong>penalizer</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – </p></li>
+<li><p><strong>l1_ratio</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – </p></li>
+<li><p><strong>propensity_model</strong> (<em>Module</em>) – </p></li>
+<li><p><strong>training_strategy</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – </p></li>
+<li><p><strong>cox_fit_cols</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a>) – </p></li>
+<li><p><strong>propensity_fit_cols</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a>) – </p></li>
+<li><p><strong>store_hessian</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – </p></li>
+<li><p><strong>with_batch_norm_parameters</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – </p></li>
+<li><p><strong>use_gpu</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – </p></li>
+<li><p><strong>robust</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – </p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.algorithms.TorchWebDiscoAlgo.compute_X_y_and_propensity_weights">
+<span class="sig-name descname"><span class="pre">compute_X_y_and_propensity_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_from_opener</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_state</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.algorithms.TorchWebDiscoAlgo.compute_X_y_and_propensity_weights" title="Permalink to this definition">¶</a></dt>
+<dd><p>Build appropriate X, y and weights from raw output of opener.</p>
+<p>Uses the helper function build_X_y and the propensity model to build the
+weights.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data_from_opener</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">pd.DataFrame</span></code>) – Raw output from opener</p></li>
+<li><p><strong>shared_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>, <em>optional</em>) – Outmodel containing global means and stds, by default {}</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>X input to the Cox model, y target of Cox model, weights propensity weights</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.algorithms.TorchWebDiscoAlgo.compute_local_phi_stats">
+<span class="sig-name descname"><span class="pre">compute_local_phi_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_from_opener</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.algorithms.TorchWebDiscoAlgo.compute_local_phi_stats" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute local updates.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data_from_opener</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Any</span></code>) – _description_</p></li>
+<li><p><strong>shared_state</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Optional[WebDiscoAveragedStates]</span></code>, <em>optional</em>) – _description_. Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>_description_</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">WebDiscoSharedState</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.algorithms.TorchWebDiscoAlgo.local_uncentered_moments">
+<span class="sig-name descname"><span class="pre">local_uncentered_moments</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_from_opener</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.algorithms.TorchWebDiscoAlgo.local_uncentered_moments" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the local uncentered moments.</p>
+<p>This method is transformed by the decorator to meet Substra API,
+and is executed in the training nodes. See build_compute_plan.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data_from_opener</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">pd.DataFrame</span></code>) – Dataframe returned by the opener.</p></li>
+<li><p><strong>shared_state</strong> (<a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a>, <em>optional</em>) – Given by the aggregation node, here nothing, by default None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Local results to be shared via shared_state to the aggregation node.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.algorithms.TorchWebDiscoAlgo.predict">
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_from_opener</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.algorithms.TorchWebDiscoAlgo.predict" title="Permalink to this definition">¶</a></dt>
+<dd><p>Predict function.</p>
+<p>Execute the following operations:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>Create the test torch dataset.</p></li>
+<li><p>Execute and return the results of the <code class="docutils literal notranslate"><span class="pre">self._local_predict</span></code> method</p></li>
+</ul>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data_from_opener</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">typing.Any</span></code>) – Input data</p></li>
+<li><p><strong>shared_state</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">typing.Any</span></code>) – Latest train task shared state (output of the train method)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><code class="xref py py-data docutils literal notranslate"><span class="pre">Any</span></code></a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.algorithms.TorchWebDiscoAlgo.summary">
+<span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.algorithms.TorchWebDiscoAlgo.summary" title="Permalink to this definition">¶</a></dt>
+<dd><p>Summary of the class to be exposed in the experiment summary file.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>A json-serializable dict with the attributes the user wants to store</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.algorithms.TorchWebDiscoAlgo.train">
+<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_from_opener</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.algorithms.TorchWebDiscoAlgo.train" title="Permalink to this definition">¶</a></dt>
+<dd><p>Local train function.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data_from_opener</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Any</span></code>) – _description_</p></li>
+<li><p><strong>shared_state</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Optional[WebDiscoAveragedStates]</span></code>, <em>optional</em>) – <a href="#id1"><span class="problematic" id="id2">description_</span></a>. Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#NotImplementedError" title="(in Python v3.13)"><strong>NotImplementedError</strong></a> – _description_</p>
+</dd>
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>_description_</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">WebDiscoSharedState</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="fedeca.algorithms.TorchWebDiscoAlgo.strategies">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">strategies</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.13)"><span class="pre">List</span></a><span class="p"><span class="pre">[</span></span><span class="pre">StrategyName</span><span class="p"><span class="pre">]</span></span></em><a class="headerlink" href="#fedeca.algorithms.TorchWebDiscoAlgo.strategies" title="Permalink to this definition">¶</a></dt>
+<dd><p>List of compatible strategies.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>List of compatible strategies.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">typing.List[StrategyName]</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
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+<li class="toctree-l1"><a class="reference internal" href="../quickstart.html#pooled-iptw-analysis">Pooled IPTW analysis</a></li>
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+<li class="toctree-l1 current"><a class="current reference internal" href="#">fedeca.competitors</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.PooledIPTW"><code class="docutils literal notranslate"><span class="pre">PooledIPTW</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.PooledIPTW.fit"><code class="docutils literal notranslate"><span class="pre">PooledIPTW.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.PooledIPTW.point_estimate"><code class="docutils literal notranslate"><span class="pre">PooledIPTW.point_estimate()</span></code></a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="#fedeca.MatchingAjudsted"><code class="docutils literal notranslate"><span class="pre">MatchingAjudsted</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.MatchingAjudsted.bootstrap_sample"><code class="docutils literal notranslate"><span class="pre">MatchingAjudsted.bootstrap_sample()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.MatchingAjudsted.fit"><code class="docutils literal notranslate"><span class="pre">MatchingAjudsted.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.MatchingAjudsted.point_estimate"><code class="docutils literal notranslate"><span class="pre">MatchingAjudsted.point_estimate()</span></code></a></li>
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+<li class="toctree-l3"><a class="reference internal" href="#fedeca.NaiveComparison.point_estimate"><code class="docutils literal notranslate"><span class="pre">NaiveComparison.point_estimate()</span></code></a></li>
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+  <section id="fedeca-competitors">
+<h1>fedeca.competitors<a class="headerlink" href="#fedeca-competitors" title="Permalink to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="fedeca.PooledIPTW">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">PooledIPTW</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">treated_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'treated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">event_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'E'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">duration_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'T'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ps_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'propensity_scores'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">effect</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ATE'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">variance_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'naive'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cox_fit_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.PooledIPTW" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseSurvivalEstimator</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">BootstrapMixin</span></code></p>
+<p>Class for the Pooled IPTW.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>variance_method</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Literal" title="(in Python v3.13)"><em>Literal</em></a><em>[</em><em>'naive'</em><em>, </em><em>'robust'</em><em>, </em><em>'bootstrap'</em><em>]</em>) – </p></li>
+<li><p><strong>n_bootstrap</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a>) – </p></li>
+<li><p><strong>seed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><em>Sequence</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>] </em><em>| </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/bit_generators/generated/numpy.random.BitGenerator.html#numpy.random.BitGenerator" title="(in NumPy v2.1)"><em>BitGenerator</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/bit_generators/generated/numpy.random.SeedSequence.html#numpy.random.SeedSequence" title="(in NumPy v2.1)"><em>SeedSequence</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v2.1)"><em>Generator</em></a><em> | </em><em>None</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.PooledIPTW.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.PooledIPTW.fit" title="Permalink to this definition">¶</a></dt>
+<dd><p>Estimate the treatment effect via Inverse Probability Treatment Weighting.</p>
+<p>Option to estimate the variance of estimation by bootstrapping.</p>
+<dl class="simple">
+<dt>targets: pd.DataFrame, optional</dt><dd><p>pre-computed propensity scores.
+It is possible to pass pre-computed propensity scores to the fit
+function to use in the IPTW estimator instead of estimating the
+scores using logistic regression.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.3)"><em>DataFrame</em></a>) – </p></li>
+<li><p><strong>targets</strong> (<a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.3)"><em>DataFrame</em></a><em> | </em><em>None</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.PooledIPTW.point_estimate">
+<span class="sig-name descname"><span class="pre">point_estimate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.PooledIPTW.point_estimate" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return a point estimate of the treatment effect.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
+</dd>
+<dt class="field-even">Parameters<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>data</strong> (<a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.3)"><em>DataFrame</em></a>) – </p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="fedeca.MatchingAjudsted">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">MatchingAjudsted</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">treated_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'treated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">event_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'E'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">duration_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'T'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ps_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'propensity_scores'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">variance_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'naive'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cox_fit_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.MatchingAjudsted" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseSurvivalEstimator</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">BootstrapMixin</span></code></p>
+<p>Implement Matching-Adjusted Indirect Comparisons class.</p>
+<p>We consider that we have access to individual patients data for one of the centers
+and that for the other centers we only have access to aggregated data. This method
+proposes a way to balance the distribution of the indivual patients data to match
+the mean (and std) of a list of covariates available in both studies.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>variance_method</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Literal" title="(in Python v3.13)"><em>Literal</em></a><em>[</em><em>'naive'</em><em>, </em><em>'robust'</em><em>, </em><em>'bootstrap'</em><em>]</em>) – </p></li>
+<li><p><strong>n_bootstrap</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a>) – </p></li>
+<li><p><strong>seed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><em>Sequence</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>] </em><em>| </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/bit_generators/generated/numpy.random.BitGenerator.html#numpy.random.BitGenerator" title="(in NumPy v2.1)"><em>BitGenerator</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/bit_generators/generated/numpy.random.SeedSequence.html#numpy.random.SeedSequence" title="(in NumPy v2.1)"><em>SeedSequence</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v2.1)"><em>Generator</em></a><em> | </em><em>None</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.MatchingAjudsted.bootstrap_sample">
+<span class="sig-name descname"><span class="pre">bootstrap_sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.MatchingAjudsted.bootstrap_sample" title="Permalink to this definition">¶</a></dt>
+<dd><p>Resampling only the individual patient data (IPD) with replacement.</p>
+<p>In the setting of an estimation using MAIC, the caller is suppposed to have
+access only to the individual patient data, assumed here to be marked by non-
+zero treatment allocations in the data. Therefore during the resampling, only
+accessible data should be resampled.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.3)"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataFrame</span></code></a></p>
+</dd>
+<dt class="field-even">Parameters<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>data</strong> (<a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.3)"><em>DataFrame</em></a>) – </p></li>
+<li><p><strong>seed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><em>Sequence</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>] </em><em>| </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/bit_generators/generated/numpy.random.BitGenerator.html#numpy.random.BitGenerator" title="(in NumPy v2.1)"><em>BitGenerator</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/bit_generators/generated/numpy.random.SeedSequence.html#numpy.random.SeedSequence" title="(in NumPy v2.1)"><em>SeedSequence</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v2.1)"><em>Generator</em></a><em> | </em><em>None</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.MatchingAjudsted.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.MatchingAjudsted.fit" title="Permalink to this definition">¶</a></dt>
+<dd><p>Estimate the treatment effect via Inverse Probability Treatment Weighting.</p>
+<p>Option to estimate the variance of estimation by bootstrapping.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
+</dd>
+<dt class="field-even">Parameters<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>data</strong> (<a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.3)"><em>DataFrame</em></a>) – </p></li>
+<li><p><strong>targets</strong> (<a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.3)"><em>DataFrame</em></a><em> | </em><em>None</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+<dl class="simple">
+<dt>targets: pd.DataFrame, optional</dt><dd><p>pre-computed propensity scores.
+It is possible to pass pre-computed propensity scores to the fit
+function to use in the IPTW estimator instead of estimating the
+scores using logistic regression.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.MatchingAjudsted.point_estimate">
+<span class="sig-name descname"><span class="pre">point_estimate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.MatchingAjudsted.point_estimate" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return a point estimate of the treatment effect.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
+</dd>
+<dt class="field-even">Parameters<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>data</strong> (<a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.3)"><em>DataFrame</em></a>) – </p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="fedeca.NaiveComparison">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">NaiveComparison</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">treated_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'treated'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">event_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'E'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">duration_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'T'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ps_col</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'propensity_scores'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">variance_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'naive'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cox_fit_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.NaiveComparison" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseSurvivalEstimator</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">BootstrapMixin</span></code></p>
+<p>Naive comparison as if in a randomized setting.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>variance_method</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Literal" title="(in Python v3.13)"><em>Literal</em></a><em>[</em><em>'naive'</em><em>, </em><em>'robust'</em><em>, </em><em>'bootstrap'</em><em>]</em>) – </p></li>
+<li><p><strong>n_bootstrap</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a>) – </p></li>
+<li><p><strong>seed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.13)"><em>Sequence</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>] </em><em>| </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/bit_generators/generated/numpy.random.BitGenerator.html#numpy.random.BitGenerator" title="(in NumPy v2.1)"><em>BitGenerator</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/bit_generators/generated/numpy.random.SeedSequence.html#numpy.random.SeedSequence" title="(in NumPy v2.1)"><em>SeedSequence</em></a><em> | </em><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v2.1)"><em>Generator</em></a><em> | </em><em>None</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.NaiveComparison.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.NaiveComparison.fit" title="Permalink to this definition">¶</a></dt>
+<dd><p>Estimate the treatment effect via Inverse Probability Treatment Weighting.</p>
+<p>Option to estimate the variance of estimation by bootstrapping.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a></p>
+</dd>
+<dt class="field-even">Parameters<span class="colon">:</span></dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>data</strong> (<a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.3)"><em>DataFrame</em></a>) – </p></li>
+<li><p><strong>targets</strong> (<a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.3)"><em>DataFrame</em></a><em> | </em><em>None</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+<dl class="simple">
+<dt>targets: pd.DataFrame, optional</dt><dd><p>pre-computed propensity scores.
+It is possible to pass pre-computed propensity scores to the fit
+function to use in the IPTW estimator instead of estimating the
+scores using logistic regression.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.NaiveComparison.point_estimate">
+<span class="sig-name descname"><span class="pre">point_estimate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.NaiveComparison.point_estimate" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return a point estimate of the treatment effect.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code></a></p>
+</dd>
+<dt class="field-even">Parameters<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>data</strong> (<a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.3)"><em>DataFrame</em></a>) – </p>
+</dd>
+</dl>
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+<li class="toctree-l1"><a class="reference internal" href="../quickstart.html#pooled-iptw-analysis">Pooled IPTW analysis</a></li>
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+  <section id="fedeca-fedeca-core">
+<h1>fedeca.fedeca_core<a class="headerlink" href="#fedeca-fedeca-core" title="Permalink to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="fedeca.FedECA">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">FedECA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">ndim,</span> <span class="pre">ds_client=None,</span> <span class="pre">train_data_nodes=None,</span> <span class="pre">treated_col='treated',</span> <span class="pre">event_col='E',</span> <span class="pre">duration_col='T',</span> <span class="pre">ps_col='propensity_scores',</span> <span class="pre">propensity_fit_cols=None,</span> <span class="pre">cox_fit_cols=None,</span> <span class="pre">num_rounds_list=[10,</span> <span class="pre">10],</span> <span class="pre">damping_factor_nr=0.8,</span> <span class="pre">l2_coeff_nr=0.0,</span> <span class="pre">standardize_data=True,</span> <span class="pre">penalizer=0.0,</span> <span class="pre">l1_ratio=1.0,</span> <span class="pre">initial_step_size=0.95,</span> <span class="pre">learning_rate_strategy='lifelines',</span> <span class="pre">dtype='float64',</span> <span class="pre">training_strategy='iptw',</span> <span class="pre">variance_method='naïve',</span> <span class="pre">n_bootstrap=200,</span> <span class="pre">bootstrap_seeds=None,</span> <span class="pre">bootstrap_function='global',</span> <span class="pre">clients_sizes=None,</span> <span class="pre">indices_in_global_dataset=None,</span> <span class="pre">client_identifier='client',</span> <span class="pre">clients_names=None,</span> <span class="pre">dp_target_epsilon=None,</span> <span class="pre">dp_target_delta=None,</span> <span class="pre">dp_max_grad_norm=None,</span> <span class="pre">dp_propensity_model_optimizer_class=&lt;class</span> <span class="pre">'torch.optim.sgd.SGD'&gt;,</span> <span class="pre">dp_propensity_model_optimizer_kwargs=None,</span> <span class="pre">dp_propensity_model_training_params=None,</span> <span class="pre">seed=42,</span> <span class="pre">aggregation_node=None,</span> <span class="pre">experiment_folder='./iptw_experiment',</span> <span class="pre">clean_models=False,</span> <span class="pre">dependencies=None,</span> <span class="pre">timeout=3600,</span> <span class="pre">sleep_time=30,</span> <span class="pre">fedeca_path=None,</span> <span class="pre">evaluation_frequency=None,</span> <span class="pre">partner_client=None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.FedECA" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="utils.html#fedeca.utils.substrafl_utils.Experiment" title="fedeca.utils.substrafl_utils.Experiment"><code class="xref py py-class docutils literal notranslate"><span class="pre">Experiment</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseSurvivalEstimator</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">BootstrapMixin</span></code></p>
+<p>FedECA class that performs Federated IPTW or AIPTW.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>ndim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a>) – </p></li>
+<li><p><strong>train_data_nodes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em>[</em><em>substrafl.nodes.train_data_node.TrainDataNode</em><em>] </em><em>| </em><em>None</em>) – </p></li>
+<li><p><strong>treated_col</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – </p></li>
+<li><p><strong>event_col</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – </p></li>
+<li><p><strong>duration_col</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – </p></li>
+<li><p><strong>propensity_fit_cols</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a>) – </p></li>
+<li><p><strong>cox_fit_cols</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a>) – </p></li>
+<li><p><strong>num_rounds_list</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>]</em>) – </p></li>
+<li><p><strong>damping_factor_nr</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – </p></li>
+<li><p><strong>l2_coeff_nr</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – </p></li>
+<li><p><strong>standardize_data</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – </p></li>
+<li><p><strong>penalizer</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – </p></li>
+<li><p><strong>l1_ratio</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – </p></li>
+<li><p><strong>initial_step_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – </p></li>
+<li><p><strong>learning_rate_strategy</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – </p></li>
+<li><p><strong>dtype</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – </p></li>
+<li><p><strong>training_strategy</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – </p></li>
+<li><p><strong>variance_method</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – </p></li>
+<li><p><strong>n_bootstrap</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>bootstrap_seeds</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>] </em><em>| </em><em>None</em>) – </p></li>
+<li><p><strong>bootstrap_function</strong> (<a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.13)"><em>Callable</em></a><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – </p></li>
+<li><p><strong>clients_sizes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>indices_in_global_dataset</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>client_identifier</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – </p></li>
+<li><p><strong>clients_names</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>dp_target_epsilon</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>dp_target_delta</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>dp_max_grad_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>dp_propensity_model_optimizer_class</strong> (<em>Optimizer</em>) – </p></li>
+<li><p><strong>dp_propensity_model_optimizer_kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><em>dict</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>dp_propensity_model_training_params</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><em>dict</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>seed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a>) – </p></li>
+<li><p><strong>aggregation_node</strong> (<em>AggregationNode</em><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>experiment_folder</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – </p></li>
+<li><p><strong>clean_models</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – </p></li>
+<li><p><strong>dependencies</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>timeout</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a>) – </p></li>
+<li><p><strong>sleep_time</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a>) – </p></li>
+<li><p><strong>fedeca_path</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – </p></li>
+<li><p><strong>partner_client</strong> (<em>None</em><em> | </em><em>Client</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.FedECA.check_cp_status">
+<span class="sig-name descname"><span class="pre">check_cp_status</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.FedECA.check_cp_status" title="Permalink to this definition">¶</a></dt>
+<dd><p>Check the status of the process.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.FedECA.compute_propensity_scores">
+<span class="sig-name descname"><span class="pre">compute_propensity_scores</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.FedECA.compute_propensity_scores" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute propensity scores and corresponding weights.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>data</strong> (<a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.3)"><em>DataFrame</em></a>) – </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.FedECA.compute_summary">
+<span class="sig-name descname"><span class="pre">compute_summary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.FedECA.compute_summary" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute summary for a given threshold.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">(default=0.05)</span></code>) – Confidence level for computing CIs</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.FedECA.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clients</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_method_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">variance_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap_seeds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap_function</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dp_target_epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dp_target_delta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dp_max_grad_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dp_propensity_model_training_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dp_propensity_model_optimizer_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dp_propensity_model_optimizer_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'subprocess'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">urls</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">server_org_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokens</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.FedECA.fit" title="Permalink to this definition">¶</a></dt>
+<dd><p>Fit strategies on global data split across clients.</p>
+<p>For test if provided we use test_data_nodes from int or the
+train_data_nodes in the latter train=test.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">pd.DataFrame</span></code>) – The global data to be split has to be a dataframe as we only support
+one opener type.</p></li>
+<li><p><strong>targets</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Optional[pd.DataFrame]</span></code>, <em>optional</em>) – A dataframe with propensity score or nothing.</p></li>
+<li><p><strong>nb_clients</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[int</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>, <em>optional</em>) – The number of clients used to split data across, by default None</p></li>
+<li><p><strong>split_method</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[Callable</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>, <em>optional</em>) – How to split data across the nb_clients, by default None</p></li>
+<li><p><strong>split_method_kwargs</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[Callable</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>, <em>optional</em>) – Argument of the function used to split data, by default None</p></li>
+<li><p><strong>data_path</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[str</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>) – Where to store the data on disk when backend is not remote.</p></li>
+<li><p><strong>variance_method</strong> (:class:<code class="docutils literal notranslate"><span class="pre">```{&quot;naive&quot;,</span> <span class="pre">&quot;robust&quot;,</span> <span class="pre">&quot;bootstrap&quot;}</span></code>:class:<a href="#id1"><span class="problematic" id="id2">``</span></a><a href="#id3"><span class="problematic" id="id4">`</span></a>) – <p>Method for estimating the variance, and therefore the p-value of the
+estimated treatment effect.
+* “naive”: Inverse of the Fisher information.
+* “robust”: The robust sandwich estimator [1] computed in FL thanks</p>
+<blockquote>
+<div><p>to FedECA. Useful when samples are reweighted.</p>
+</div></blockquote>
+<ul>
+<li><p>”bootstrap”: Bootstrap the given data by sampling each patient
+with replacement, each time estimate the treatment effect, then
+use all repeated estimations to compute the variance. The implementation
+is efficient in substra and shouldn’t induce too much overhead.</p></li>
+</ul>
+<p>Defauts to naïve.
+[1] David A Binder. Fitting cox’s proportional hazards models from survey data. Biometrika, 79(1):139–147, 1992.  # noqa: E501</p>
+</p></li>
+<li><p><strong>n_bootstraps</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[int</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>) – Number of bootstrap to be performed. If None will use
+len(bootstrap_seeds) instead. If bootstrap_seeds is given
+seeds those seeds will be used for the generation
+otherwise seeds are generated randomly.</p></li>
+<li><p><strong>bootstrap_seeds</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[list[int]</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>) – The list of seeds used for bootstrapping random states.
+If None will generate n_bootstraps randomly, in the presence
+of both allways use bootstrap_seeds.</p></li>
+<li><p><strong>bootstrap_function</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[Callable</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>) – The bootstrap function to use for instance if it is necessary to mimic
+a global sampling.</p></li>
+<li><p><strong>dp_target_epsilon</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>) – The target epsilon for (epsilon, delta)-differential
+private guarantee. Defaults to None.</p></li>
+<li><p><strong>dp_target_delta</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>) – The target delta for (epsilon, delta)-differential
+private guarantee. Defaults to None.</p></li>
+<li><p><strong>dp_max_grad_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>) – The maximum L2 norm of per-sample gradients;
+used to enforce differential privacy. Defaults to None.</p></li>
+<li><p><strong>dp_propensity_model_optimizer_class</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.optim.Optimizer</span></code>) – The optimizer to use for the training of the propensity model.
+Defauts to Adam.</p></li>
+<li><p><strong>dp_propensity_model_optimizer_class_kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>) – The params to give to optimizer class.</p></li>
+<li><p><strong>dp_propensity_model_training_params</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>) – A dict with keys batch_size and num_updates for the DP-SGD training.
+Defaults to None.</p></li>
+<li><p><strong>backend_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>) – The backend to use for substra. Can be either:
+[“subprocess”, “docker”, “remote”]. Defaults to “subprocess”.</p></li>
+<li><p><strong>urls</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[list[str]</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>) – Urls corresponding to clients API if using remote backend_type.
+Defaults to None.</p></li>
+<li><p><strong>server_org_id</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[str</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>) – Url corresponding to server API if using remote backend_type.
+Defaults to None.</p></li>
+<li><p><strong>tokens</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[list[str]</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>) – Tokens necessary to authenticate each client API if backend_type
+is remote. Defauts to None.</p></li>
+<li><p><strong>n_clients</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>n_bootstrap</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>dp_propensity_model_optimizer_kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><em>dict</em></a><em> | </em><em>None</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.FedECA.get_final_cox_model">
+<span class="sig-name descname"><span class="pre">get_final_cox_model</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.FedECA.get_final_cox_model" title="Permalink to this definition">¶</a></dt>
+<dd><p>Retrieve final cox model.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.FedECA.print_summary">
+<span class="sig-name descname"><span class="pre">print_summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.FedECA.print_summary" title="Permalink to this definition">¶</a></dt>
+<dd><p>Print a summary of the FedECA estimation.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.FedECA.reset_experiment">
+<span class="sig-name descname"><span class="pre">reset_experiment</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.FedECA.reset_experiment" title="Permalink to this definition">¶</a></dt>
+<dd><p>Remove the propensity model just in case.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.FedECA.run">
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">targets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.FedECA.run" title="Permalink to this definition">¶</a></dt>
+<dd><p>Run the federated iptw algorithms.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>targets</strong> (<a class="reference external" href="https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.2.3)"><em>DataFrame</em></a><em> | </em><em>None</em>) – </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.FedECA.set_propensity_model_strategy">
+<span class="sig-name descname"><span class="pre">set_propensity_model_strategy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.FedECA.set_propensity_model_strategy" title="Permalink to this definition">¶</a></dt>
+<dd><p>Set FedECA to use DP.</p>
+<p>At the end it sets the parameter self.propensity_model_strateg</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="fedeca.FedECA.clients_names">
+<span class="sig-name descname"><span class="pre">clients_names</span></span><a class="headerlink" href="#fedeca.FedECA.clients_names" title="Permalink to this definition">¶</a></dt>
+<dd><p>of clients for global bootstrap by passing num_clients</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+           
+          </div>
+          <footer>
+  
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>
+        &copy; Copyright 2024, OWKIN
+
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+<li class="toctree-l1"><a class="reference internal" href="../quickstart.html#pooled-iptw-analysis">Pooled IPTW analysis</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="#fedeca.metrics.metrics.standardized_mean_diff"><code class="docutils literal notranslate"><span class="pre">standardized_mean_diff()</span></code></a></li>
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+  <section id="module-fedeca.metrics.metrics">
+<span id="fedeca-metrics"></span><h1>fedeca.metrics<a class="headerlink" href="#module-fedeca.metrics.metrics" title="Permalink to this heading">¶</a></h1>
+<p>Define metrics for ECA analysis.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.metrics.metrics.standardized_mean_diff">
+<span class="sig-name descname"><span class="pre">standardized_mean_diff</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">confounders</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">treated</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_unweighted_variance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.metrics.metrics.standardized_mean_diff" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the Standardized Mean Differences (SMD).</p>
+<p>Compute the Standardized Mean Differences between
+treated and control patients.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>confounders</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">pd.DataFrame</span></code>) – confounders array.</p></li>
+<li><p><strong>treated</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">np.ndarray</span></code>) – mask of booleans giving information about treated patients.</p></li>
+<li><p><strong>weights</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">np.ndarray</span></code>) – weights for the aggregation</p></li>
+<li><p><strong>use_unweighted_variance</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a>) – if True, the variance is computed without weights. To follow
+<a class="reference external" href="https://stats.stackexchange.com/questions/618643/formula-for-standardized-mean-difference-in-cobalt-package-for-categorical-varia">https://stats.stackexchange.com/questions/618643/formula-for-standardized-mean-difference-in-cobalt-package-for-categorical-varia</a>  # noqa: E501
+If False use recalibrated variance as in <a class="reference external" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4626409/">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4626409/</a>.  # noqa: E501</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>smd</strong> – standardized mean differences of the confounders.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">np.ndarray</span></code></p>
+</dd>
+</dl>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../quickstart.html">Quickstart</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quickstart.html#pooled-iptw-analysis">Pooled IPTW analysis</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quickstart.html#distributed-analysis">Distributed Analysis</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="#fedeca.scripts.substra_assets.csv_opener.CSVOpener"><code class="docutils literal notranslate"><span class="pre">CSVOpener</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.scripts.substra_assets.csv_opener.CSVOpener.fake_data"><code class="docutils literal notranslate"><span class="pre">CSVOpener.fake_data()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.scripts.substra_assets.csv_opener.CSVOpener.get_data"><code class="docutils literal notranslate"><span class="pre">CSVOpener.get_data()</span></code></a></li>
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+            
+  <section id="fedeca-scripts">
+<h1>fedeca.scripts<a class="headerlink" href="#fedeca-scripts" title="Permalink to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="fedeca.scripts.substra_assets.csv_opener.CSVOpener">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">CSVOpener</span></span><a class="headerlink" href="#fedeca.scripts.substra_assets.csv_opener.CSVOpener" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Opener</span></code></p>
+<p>CSV opener class.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.scripts.substra_assets.csv_opener.CSVOpener.fake_data">
+<span class="sig-name descname"><span class="pre">fake_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.scripts.substra_assets.csv_opener.CSVOpener.fake_data" title="Permalink to this definition">¶</a></dt>
+<dd><p>Generate simulated survival data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>n_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a> or <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a>, <em>optional</em>) – Number of samples, by default None.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Fake survival data.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">pd.DataFrame</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.scripts.substra_assets.csv_opener.CSVOpener.get_data">
+<span class="sig-name descname"><span class="pre">get_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">folders</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.scripts.substra_assets.csv_opener.CSVOpener.get_data" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get data from CSV files.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>folders</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a>) – List of folder paths.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Loaded data from CSV files.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">pd.DataFrame</span></code></p>
+</dd>
+</dl>
+</dd></dl>
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+<li class="toctree-l1 current"><a class="current reference internal" href="#">fedeca.strategies</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.strategies.WebDisco"><code class="docutils literal notranslate"><span class="pre">WebDisco</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.strategies.WebDisco.aggregate_moments"><code class="docutils literal notranslate"><span class="pre">WebDisco.aggregate_moments()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.strategies.WebDisco.build_compute_plan"><code class="docutils literal notranslate"><span class="pre">WebDisco.build_compute_plan()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.strategies.WebDisco.perform_evaluation"><code class="docutils literal notranslate"><span class="pre">WebDisco.perform_evaluation()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.strategies.WebDisco.perform_round"><code class="docutils literal notranslate"><span class="pre">WebDisco.perform_round()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.strategies.WebDisco.name"><code class="docutils literal notranslate"><span class="pre">WebDisco.name</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.strategies.bootstraper.make_bootstrap_metric_function"><code class="docutils literal notranslate"><span class="pre">make_bootstrap_metric_function()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.strategies.bootstraper.make_bootstrap_strategy"><code class="docutils literal notranslate"><span class="pre">make_bootstrap_strategy()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.strategies.webdisco_utils.compute_summary_function"><code class="docutils literal notranslate"><span class="pre">compute_summary_function()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.strategies.webdisco_utils.get_final_cox_model_function"><code class="docutils literal notranslate"><span class="pre">get_final_cox_model_function()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.strategies.webdisco_utils.get_last_algo_from_round_count"><code class="docutils literal notranslate"><span class="pre">get_last_algo_from_round_count()</span></code></a></li>
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+            
+  <section id="fedeca-strategies">
+<h1>fedeca.strategies<a class="headerlink" href="#fedeca-strategies" title="Permalink to this heading">¶</a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="fedeca.strategies.WebDisco">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">WebDisco</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">algo</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_functions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standardize_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-16</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.strategies.WebDisco" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Strategy</span></code></p>
+<p>WebDisco strategy class.</p>
+<p>It can only be used with traditional Cox models on pandas.DataFrames.
+This strategy is one of its kind because it can only be used with
+Linear CoxPH models defined in fedeca.utils.survival_utils. Therefore all models are
+initialized with zeroed weights (as in lifelines), tested and we cover all possible
+use cases with the dtype and ndim arguments. This strategy splits the computations
+of gradient and Hessian between workers to compute a centralized batch Newton-
+Raphson update on Breslow’s partial log-likelihod (to handle tied events it uses
+Breslow’s approximation unlike lifelines which uses Efron’s by default but Efron is
+not separable). This strategy uses lifeline’s adaptive step-size to converge faster
+starting from initial_ste_size and use lifelines safe way of inverting the hessian.
+As lifelines standardizes the data by default we allow the user to do it optionally.</p>
+<section id="reference">
+<h2>Reference<a class="headerlink" href="#reference" title="Permalink to this heading">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference external" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5009917/">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5009917/</a></p></li>
+</ul>
+<dl class="field-list simple">
+<dt class="field-odd">param statistics_computed<span class="colon">:</span></dt>
+<dd class="field-odd"><p>If the statistics that we can find in each gradient, hessian are already
+computed and given as attribute to the server or not.</p>
+</dd>
+<dt class="field-even">type statistics_computed<span class="colon">:</span></dt>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool,</span></code></p>
+</dd>
+<dt class="field-odd">param initial_step_size<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The initial step size of the Newton-Raphson algorithm at the server side.
+The following steps will use lifelines heuristics to adapt the step-size.
+Defaults to 0.9.</p>
+</dd>
+<dt class="field-even">type initial_step_size<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">otional</span></code></p>
+</dd>
+<dt class="field-odd">type tol<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a></p>
+</dd>
+<dt class="field-even">param tol<span class="colon">:</span></dt>
+<dd class="field-even"><p>Capping every division to avoid dividing by 0. Defaults to 1e-16.</p>
+</dd>
+<dt class="field-odd">type tol<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, <em>optional</em></p>
+</dd>
+<dt class="field-even">type standardize_data<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a></p>
+</dd>
+<dt class="field-odd">param standardize_data<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Whether or not to standardize the data before comuting updates.
+Defaults to False.</p>
+</dd>
+<dt class="field-even">type standardize_data<span class="colon">:</span></dt>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">bool,</span></code></p>
+</dd>
+<dt class="field-odd">param penalizer<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Add a regularizer in case of ill-conditioned hessians, which happen quite
+often with large covariates.
+Defaults to 0.</p>
+</dd>
+<dt class="field-even">type penalizer<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, <em>optional</em></p>
+</dd>
+<dt class="field-odd">param l1_ratio<span class="colon">:</span></dt>
+<dd class="field-odd"><p>When using a penalizer the ratio between L1 and L2 regularization as in
+sklearn.
+Defaults to 0.</p>
+</dd>
+<dt class="field-even">type l1_ratio<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, <em>optional</em></p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.strategies.WebDisco.aggregate_moments">
+<span class="sig-name descname"><span class="pre">aggregate_moments</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shared_states</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.strategies.WebDisco.aggregate_moments" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the global centered moments given the local results.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>shared_states</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">List</span></code>) – List of results (local_m1, local_m2, n_samples) from training nodes.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Global results to be shared with train nodes via shared_state.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.strategies.WebDisco.build_compute_plan">
+<span class="sig-name descname"><span class="pre">build_compute_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_data_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggregation_node</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">evaluation_strategy</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_rounds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clean_models</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.strategies.WebDisco.build_compute_plan" title="Permalink to this definition">¶</a></dt>
+<dd><p>Build the computation graph of the strategy.</p>
+<p>It removes initialization round,
+which is useless in this case as all models start at 0.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>train_data_nodes</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">typing.List[TrainDataNode],</span></code>) – list of the train organizations</p></li>
+<li><p><strong>aggregation_node</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">typing.Optional[AggregationNode],</span></code>) – aggregation node, necessary for centralized strategy, unused otherwise</p></li>
+<li><p><strong>evaluation_strategy</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Optional[EvaluationStrategy],</span></code>) – When and how to compute performance.</p></li>
+<li><p><strong>num_rounds</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int,</span></code>) – The number of rounds to perform.</p></li>
+<li><p><strong>clean_models</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span> <span class="pre">(default=True),</span></code>) – Clean the intermediary models on the Substra platform.
+Set it to False if you want to download or re-use
+intermediary models. This causes the disk space to
+fill quickly so should be set to True unless needed.
+Defaults to True.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.strategies.WebDisco.perform_evaluation">
+<span class="sig-name descname"><span class="pre">perform_evaluation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">test_data_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_data_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">round_idx</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.strategies.WebDisco.perform_evaluation" title="Permalink to this definition">¶</a></dt>
+<dd><p>Evaluate model on the given test_data_nodes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>test_data_nodes</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">List[TestDataNode]),</span></code>) – test data nodes to intersect with train data nodes to evaluate the
+model on.</p></li>
+<li><p><strong>train_data_nodes</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">List[TrainDataNode],</span></code>) – train data nodes the model has been trained on.</p></li>
+<li><p><strong>round_idx</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int,</span></code>) – round index.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#NotImplementedError" title="(in Python v3.13)"><strong>NotImplementedError</strong></a> – </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.strategies.WebDisco.perform_round">
+<span class="sig-name descname"><span class="pre">perform_round</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_data_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggregation_node</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">round_idx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clean_models</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">additional_orgs_permissions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.strategies.WebDisco.perform_round" title="Permalink to this definition">¶</a></dt>
+<dd><p>Perform one round of webdisco.</p>
+<dl class="simple">
+<dt>One round of the WebDisco strategy consists in:</dt><dd><ul class="simple">
+<li><p>optionally compute global means and stds for all features if
+standardize_data is True</p></li>
+<li><p>compute global survival statistics that will be reused at each round</p></li>
+<li><p>build building blocks of the gradient and hessian based on global risk
+sets</p></li>
+<li><p>perform a Newton-Raphson update on each train data nodes</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>train_data_nodes</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">typing.List[TrainDataNode],</span></code>) – List of the nodes on which to train</p></li>
+<li><p><strong>aggregation_node</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">AggregationNode</span></code>) – node without data, used to perform operations on the
+shared states of the models</p></li>
+<li><p><strong>round_idx</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int,</span></code>) – Round number, it starts at 0.</p></li>
+<li><p><strong>clean_models</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">bool,</span></code>) – Clean the intermediary models of this round on the
+Substra platform. Set it to False if you want to
+download or re-use intermediary models. This causes the
+disk space to fill quickly so should be set to True unless needed.</p></li>
+<li><p><strong>additional_orgs_permissions</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">typing.Optional[set],</span></code>) – Additional permissions to give to the model outputs after training,
+in order to test the model on an other organization.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="fedeca.strategies.WebDisco.name">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">name</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">StrategyName</span></em><a class="headerlink" href="#fedeca.strategies.WebDisco.name" title="Permalink to this definition">¶</a></dt>
+<dd><p>The name of the strategy.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>StrategyName</strong></p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">Name</span></code> of <code class="xref py py-class docutils literal notranslate"><span class="pre">the</span> <span class="pre">strategy</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>algo</strong> (<em>Algo</em>) – </p></li>
+<li><p><strong>metric_functions</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Dict" title="(in Python v3.13)"><em>Dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><em>Callable</em></a><em>] </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.13)"><em>List</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><em>Callable</em></a><em>] </em><em>| </em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><em>Callable</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>standardize_data</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – </p></li>
+<li><p><strong>tol</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><em>float</em></a>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<span class="target" id="module-fedeca.strategies.bootstraper"></span><p>Bootstrap substra strategy in an efficient fashion.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.strategies.bootstraper.make_bootstrap_metric_function">
+<span class="sig-name descname"><span class="pre">make_bootstrap_metric_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metric_functions</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.strategies.bootstraper.make_bootstrap_metric_function" title="Permalink to this definition">¶</a></dt>
+<dd><p>Take the metric_functions dict, and bootstrap each metric.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>metric_functions</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>) – The metric functions to hook.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.strategies.bootstraper.make_bootstrap_strategy">
+<span class="sig-name descname"><span class="pre">make_bootstrap_strategy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">strategy</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bootstrap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap_seeds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap_function</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">client_specific_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.strategies.bootstraper.make_bootstrap_strategy" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bootstrap a substrafl strategy wo impacting the number of compute tasks.</p>
+<p>In order to reduce the bottleneck of substra when bootstraping a strategy
+we need to go over the strategy compute plan and modifies each local atomic
+task to execute n_bootstrap times on bootstraped data. Each modified task
+returns a list of n_bootstraps original outputs obtained on each bootstrap.
+Each aggregation task is then modified to aggregate the n_bootstrap outputs
+independently.
+This code heavily uses some code patterns invented by Arthur Pignet.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>strategy</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Strategy</span></code>) – The strategy to bootstrap.</p></li>
+<li><p><strong>n_bootstrap</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[int</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>) – Number of bootstrap to be performed. If None will use
+len(bootstrap_seeds) instead. If bootstrap_seeds is given
+seeds those seeds will be used for the generation
+otherwise seeds are generated randomly.</p></li>
+<li><p><strong>bootstrap_seeds</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[list[int]</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>) – The list of seeds used for bootstrapping random states.
+If None will generate n_bootstrap randomly, in the presence
+of both allways use bootstrap_seeds.</p></li>
+<li><p><strong>bootstrap_function</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[Callable</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>) – A function with signature f(data, seed) that returns a bootstrapped
+version of the data.
+If None, use the BootstrapMixin function.
+Note that this can be used to provide splits/cross-validation capabilities
+as well where seed would be the fold number in a flattened list of folds.</p></li>
+<li><p><strong>client_specific_kwargs</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[None</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">list[dict]]</span></code>) – A list of dictionaries containing the kwargs to be passed to the algos
+if they are different for each bootstrap. It is useful to chain bootstrapped
+compute plans for instance.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The resulting efficiently bootstrapped strategy</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">Strategy</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
+<span class="target" id="module-fedeca.strategies.webdisco_utils"></span><p>Webdisco utils.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.strategies.webdisco_utils.compute_summary_function">
+<span class="sig-name descname"><span class="pre">compute_summary_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">final_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">variance_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.05</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.strategies.webdisco_utils.compute_summary_function" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute summary function.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>final_params</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">np.ndarray</span></code>) – The estimated vallues of Cox model coefficients.</p></li>
+<li><p><strong>variance_matrix</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">np.ndarray</span></code>) – Computed variance matrix whether using robust estimation or not.</p></li>
+<li><p><strong>alpha</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, <em>optional</em>) – The quantile level to test, by default 0.05</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Summary of IPTW analysis as in lifelines.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">pd.DataFrame</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.strategies.webdisco_utils.get_final_cox_model_function">
+<span class="sig-name descname"><span class="pre">get_final_cox_model_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">client</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_plan_key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_rounds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standardize_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">duration_col</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">event_col</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">simu_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">robust</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.strategies.webdisco_utils.get_final_cox_model_function" title="Permalink to this definition">¶</a></dt>
+<dd><p>Retrieve first converged Cox model and corresponding hessian.</p>
+<p>In case of bootstrapping retrieves the first converged Cox models for each
+seed.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>Client</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Client</span></code>) – The susbtrafl Client that registered the CP.</p></li>
+<li><p><strong>compute_plan_key</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[str</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">Algo]</span></code>) – The key of the CP.</p></li>
+<li><p><strong>num_rounds</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>) – The number of rounds of the CP.</p></li>
+<li><p><strong>standardize_data</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, <em>optional</em>) – Whether or not the data was standadized, by default 0.05</p></li>
+<li><p><strong>duration_col</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>) – The name of the duration column.</p></li>
+<li><p><strong>event_col</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>) – The name of the event column.</p></li>
+<li><p><strong>simu_mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a>) – Whether or not we are using simu mode. Note this could be inferred from
+the Client.</p></li>
+<li><p><strong>robust</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a>, <em>optional</em>) – Retreive global statistics for robust variance estimation.</p></li>
+<li><p><strong>client</strong> (<em>Client</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Returns hessian, log-likelihood, Cox model’s weights, global moments</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">tuple</span></code></a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.strategies.webdisco_utils.get_last_algo_from_round_count">
+<span class="sig-name descname"><span class="pre">get_last_algo_from_round_count</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_rounds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">standardize_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">simu_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.strategies.webdisco_utils.get_last_algo_from_round_count" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get true number of rounds.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>num_rounds</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">list[int]</span></code>) – _description_</p></li>
+<li><p><strong>standardize_data</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a>, <em>optional</em>) – _description_, by default True</p></li>
+<li><p><strong>simu_mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a>) – Whether or not we are in simu mode.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>_description_</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">_type_</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
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+<li class="toctree-l1"><a class="reference internal" href="../quickstart.html">Quickstart</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../quickstart.html#pooled-iptw-analysis">Pooled IPTW analysis</a></li>
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+<li class="toctree-l1 current"><a class="current reference internal" href="#">fedeca.utils</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.utils.data_utils.generate_cox_data_and_substra_clients"><code class="docutils literal notranslate"><span class="pre">generate_cox_data_and_substra_clients()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.utils.data_utils.split_control_over_centers"><code class="docutils literal notranslate"><span class="pre">split_control_over_centers()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.utils.data_utils.split_dataframe_across_clients"><code class="docutils literal notranslate"><span class="pre">split_dataframe_across_clients()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.utils.data_utils.uniform_split"><code class="docutils literal notranslate"><span class="pre">uniform_split()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.utils.moments_utils.aggregation_mean"><code class="docutils literal notranslate"><span class="pre">aggregation_mean()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.utils.moments_utils.compute_centered_moment"><code class="docutils literal notranslate"><span class="pre">compute_centered_moment()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.utils.moments_utils.compute_global_moments"><code class="docutils literal notranslate"><span class="pre">compute_global_moments()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.utils.moments_utils.compute_uncentered_moment"><code class="docutils literal notranslate"><span class="pre">compute_uncentered_moment()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.utils.substrafl_utils.Experiment"><code class="docutils literal notranslate"><span class="pre">Experiment</span></code></a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.utils.substrafl_utils.Experiment.fit"><code class="docutils literal notranslate"><span class="pre">Experiment.fit()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.utils.substrafl_utils.Experiment.get_outmodel"><code class="docutils literal notranslate"><span class="pre">Experiment.get_outmodel()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.utils.substrafl_utils.Experiment.reset_experiment"><code class="docutils literal notranslate"><span class="pre">Experiment.reset_experiment()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fedeca.utils.substrafl_utils.Experiment.run"><code class="docutils literal notranslate"><span class="pre">Experiment.run()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.utils.substrafl_utils.SubstraflTorchDataset"><code class="docutils literal notranslate"><span class="pre">SubstraflTorchDataset</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.utils.substrafl_utils.download_train_task_models_by_round"><code class="docutils literal notranslate"><span class="pre">download_train_task_models_by_round()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.utils.substrafl_utils.get_outmodel_function"><code class="docutils literal notranslate"><span class="pre">get_outmodel_function()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.utils.substrafl_utils.get_simu_state_from_round"><code class="docutils literal notranslate"><span class="pre">get_simu_state_from_round()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.utils.substrafl_utils.make_accuracy_function"><code class="docutils literal notranslate"><span class="pre">make_accuracy_function()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.utils.substrafl_utils.make_c_index_function"><code class="docutils literal notranslate"><span class="pre">make_c_index_function()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.utils.substrafl_utils.make_substrafl_torch_dataset_class"><code class="docutils literal notranslate"><span class="pre">make_substrafl_torch_dataset_class()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#fedeca.utils.tensor_utils.compare_tensors_lists"><code class="docutils literal notranslate"><span class="pre">compare_tensors_lists()</span></code></a></li>
+</ul>
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+  <section id="module-fedeca.utils.data_utils">
+<span id="fedeca-utils"></span><h1>fedeca.utils<a class="headerlink" href="#module-fedeca.utils.data_utils" title="Permalink to this heading">¶</a></h1>
+<p>Utility functions of data generation.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.utils.data_utils.generate_cox_data_and_substra_clients">
+<span class="sig-name descname"><span class="pre">generate_cox_data_and_substra_clients</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_clients</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ndim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_method_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'subprocess'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">urls</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokens</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_per_client</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_treated</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ncategorical</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.data_utils.generate_cox_data_and_substra_clients" title="Permalink to this definition">¶</a></dt>
+<dd><p>Generate Cox data on disk for several clients.</p>
+<p>Generate Cox data and register them with different
+fake clients.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_clients</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">(optional)</span></code>) – Number of clients. Defaults to 2.</p></li>
+<li><p><strong>ndim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">(optional)</span></code>) – Number of covariates. Defaults to 10.</p></li>
+<li><p><strong>Union</strong><strong>[</strong><strong>dict</strong> (<em>split_method_kwargs =</em>) – The argument to the split_method uniform.</p></li>
+<li><p><strong>None</strong><strong>]</strong> – The argument to the split_method uniform.</p></li>
+<li><p><strong>backend_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">(optional)</span></code>) – Type of backend. Defaults to “subprocess”.</p></li>
+<li><p><strong>data_path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">(optional)</span></code>) – Path to save the data. Defaults to None.</p></li>
+<li><p><strong>seed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">(optional)</span></code>) – Random seed. Defaults to 42.</p></li>
+<li><p><strong>n_per_client</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">(optional)</span></code>) – Number of samples per client. Defaults to 200.</p></li>
+<li><p><strong>add_treated</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">(optional)</span></code>) – Whether or not to keep treated column.</p></li>
+<li><p><strong>ncategorical</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">(optional)</span></code>) – Number of features to make categorical a posteriori (moving away from Cox
+assumptions).</p></li>
+<li><p><strong>split_method_kwargs</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><em>dict</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>urls</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a>) – </p></li>
+<li><p><strong>tokens</strong> (<em>None</em><em> | </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.utils.data_utils.split_control_over_centers">
+<span class="sig-name descname"><span class="pre">split_control_over_centers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clients</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">treatment_info</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'treatment_allocation'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_random</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.data_utils.split_control_over_centers" title="Permalink to this definition">¶</a></dt>
+<dd><p>Split patients in the control group over the centers.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>df</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame,</span></code>) – Dataframe containing features of the patients.</p></li>
+<li><p><strong>n_clients</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int,</span></code>) – Number of clients.</p></li>
+<li><p><strong>treatment_info</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">(optional)</span></code>) – Column name for the treatment allocation covariate.
+Defaults to “treatment_allocation”.</p></li>
+<li><p><strong>use_random</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a>) – Whether or not to shuffle the control group indices before splitting.</p></li>
+<li><p><strong>seed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>) – The seed of the shuffling.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.utils.data_utils.split_dataframe_across_clients">
+<span class="sig-name descname"><span class="pre">split_dataframe_across_clients</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clients</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_method_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'subprocess'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">urls</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokens</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.data_utils.split_dataframe_across_clients" title="Permalink to this definition">¶</a></dt>
+<dd><p>Split patients over the centers.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>df</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame,</span></code>) – Dataframe containing features of the patients.</p></li>
+<li><p><strong>n_clients</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int,</span></code>) – Number of clients.</p></li>
+<li><p><strong>split_method</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[Callable</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">str]</span></code>) – How to split the dataset across all clients, if callable should have the
+signature: df, n_clients, kwargs -&gt; list[list[int]]
+if str should be an existing key, which will invoke the corresponding
+callable. Possible values are <cite>uniform</cite> which splits the patients
+uniformly across centers or <cite>split_control_over_centers</cite> where one
+center has all the treated patients and the control is split over the
+remaining ones.</p></li>
+<li><p><strong>split_method_kwargs</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[dict</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>) – Optional kwargs for the split_method method.</p></li>
+<li><p><strong>backend_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">(optional)</span></code>) – Backend type. Default is “subprocess”.</p></li>
+<li><p><strong>data_path</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[str</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None],</span></code>) – Path on where to save the data on disk.</p></li>
+<li><p><strong>urls</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">List,</span></code>) – List of urls.</p></li>
+<li><p><strong>tokens</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">List,</span></code>) – List of tokens.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.utils.data_utils.uniform_split">
+<span class="sig-name descname"><span class="pre">uniform_split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_clients</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_random</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.data_utils.uniform_split" title="Permalink to this definition">¶</a></dt>
+<dd><p>Split patients uniformly over n_clients.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>df</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame,</span></code>) – Dataframe containing features of the patients.</p></li>
+<li><p><strong>n_clients</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">int,</span></code>) – Number of clients.</p></li>
+<li><p><strong>use_random</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a>) – Whether or not to shuffle data before splitting. Defaults to True.</p></li>
+<li><p><strong>seed</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>, <code class="xref py py-class docutils literal notranslate"><span class="pre">(optional)</span></code>) – Seeding for shuffling</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<span class="target" id="module-fedeca.utils.moments_utils"></span><p>A module containing utils to compute high-order moments using Newton’s formeanla.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.utils.moments_utils.aggregation_mean">
+<span class="sig-name descname"><span class="pre">aggregation_mean</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">local_means</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_local_samples</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.moments_utils.aggregation_mean" title="Permalink to this definition">¶</a></dt>
+<dd><p>Aggregate local means.</p>
+<p>Aggregate the local means into a global mean by using the local number of samples.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>local_means</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">List[Any]</span></code>) – List of local means. Could be array, float, Series.</p></li>
+<li><p><strong>n_local_samples</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">List[int]</span></code>) – List of number of samples used for each local mean.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Aggregated mean. Same type of the local means</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">Any</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.utils.moments_utils.compute_centered_moment">
+<span class="sig-name descname"><span class="pre">compute_centered_moment</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">uncentered_moments</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.moments_utils.compute_centered_moment" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the centered moment of order k.</p>
+<p>Given a list of the k first unnormalized moments,
+compute the centered moment of order k.
+For high values of the moments the results can
+differ from scipy.special.moment.
+We are interested in computing
+.. math:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>\<span class="n">hat</span><span class="p">{</span>\<span class="n">mu</span><span class="p">}</span><span class="n">_k</span>  <span class="o">=</span> \<span class="n">frac</span><span class="p">{</span><span class="mi">1</span><span class="p">}{</span>\<span class="n">hat</span><span class="p">{</span>\<span class="n">sigma</span><span class="p">}</span><span class="o">^</span><span class="n">k</span><span class="p">}</span>
+    \<span class="n">mathbb</span> <span class="n">E_Z</span> \<span class="n">left</span><span class="p">[</span> <span class="p">(</span><span class="n">Z</span> <span class="o">-</span> \<span class="n">hat</span><span class="p">{</span>\<span class="n">mu</span><span class="p">})</span><span class="o">^</span><span class="n">k</span>\<span class="n">right</span><span class="p">]</span>
+\<span class="n">hat</span><span class="p">{</span>\<span class="n">mu</span><span class="p">}</span><span class="n">_k</span>  <span class="o">=</span> \<span class="n">frac</span><span class="p">{</span><span class="mi">1</span><span class="p">}{</span>\<span class="n">hat</span><span class="p">{</span>\<span class="n">sigma</span><span class="p">}</span><span class="o">^</span><span class="n">k</span><span class="p">}</span>
+    \<span class="n">mathbb</span> <span class="n">E_Z</span> \<span class="n">left</span><span class="p">[</span> \<span class="n">sum_</span><span class="p">{</span><span class="n">l</span><span class="o">=</span><span class="mi">0</span><span class="p">}</span><span class="o">^</span><span class="n">k</span>\<span class="n">binom</span><span class="p">{</span><span class="n">k</span><span class="p">}{</span><span class="n">l</span><span class="p">}</span> <span class="n">Z</span><span class="o">^</span><span class="p">{</span><span class="n">k</span><span class="o">-</span><span class="n">l</span><span class="p">}</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">^</span><span class="n">l</span>\<span class="n">hat</span>\<span class="n">mu</span><span class="o">^</span><span class="n">l</span><span class="p">)</span>\<span class="n">right</span><span class="p">]</span>
+\<span class="n">hat</span><span class="p">{</span>\<span class="n">mu</span><span class="p">}</span><span class="n">_k</span>  <span class="o">=</span> \<span class="n">frac</span><span class="p">{</span><span class="mi">1</span><span class="p">}{</span>\<span class="n">hat</span><span class="p">{</span>\<span class="n">sigma</span><span class="p">}</span><span class="o">^</span><span class="n">k</span><span class="p">}</span>
+  \<span class="n">sum_</span><span class="p">{</span><span class="n">l</span><span class="o">=</span><span class="mi">0</span><span class="p">}</span><span class="o">^</span><span class="n">k</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">^</span><span class="n">l</span>\<span class="n">binom</span><span class="p">{</span><span class="n">k</span><span class="p">}{</span><span class="n">l</span><span class="p">}</span> \<span class="n">mathbb</span> <span class="n">E_Z</span> \<span class="n">left</span><span class="p">[</span> <span class="n">Z</span><span class="o">^</span><span class="p">{</span><span class="n">k</span><span class="o">-</span><span class="n">l</span><span class="p">}</span>
+  \<span class="n">right</span><span class="p">]</span>\<span class="n">mathbb</span> <span class="n">E_Z</span> \<span class="n">left</span><span class="p">[</span> <span class="n">Z</span> \<span class="n">right</span><span class="p">]</span><span class="o">^</span><span class="n">l</span>
+</pre></div>
+</div>
+<p>thus we only need the list uncentered moments up to order k.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>uncentered_moments</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">List[Any]</span></code>) – List of the k first non-centered moment.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The centered k-th moment.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">Any</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.utils.moments_utils.compute_global_moments">
+<span class="sig-name descname"><span class="pre">compute_global_moments</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shared_states</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.moments_utils.compute_global_moments" title="Permalink to this definition">¶</a></dt>
+<dd><p>Aggregate local moments.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>shared_states</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a>) – list of outputs from compute_uncentered_moment.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The results of the aggregation with both centered and uncentered moments.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.utils.moments_utils.compute_uncentered_moment">
+<span class="sig-name descname"><span class="pre">compute_uncentered_moment</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.moments_utils.compute_uncentered_moment" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compute the uncentered moment.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">pd.DataFrame</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">np.array</span></code>) – dataframe.</p></li>
+<li><p><strong>order</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>) – order of the moment.</p></li>
+<li><p><strong>weights</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">np.array</span></code>) – weight for the aggregation.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Moment of order k.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">pd.DataFrame</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">np.array</span></code></p>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference external" href="https://docs.python.org/3/library/exceptions.html#NotImplementedError" title="(in Python v3.13)"><strong>NotImplementedError</strong></a> – Raised if the data type is not Dataframe nor np.ndarray.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<span class="target" id="module-fedeca.utils.substrafl_utils"></span><p>Utils functions for Substra.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="fedeca.utils.substrafl_utils.Experiment">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Experiment</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">strategies</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_rounds_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ds_client</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_data_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_data_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggregation_node</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">evaluation_frequency</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experiment_folder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'./experiments'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clean_models</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fedeca_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">algo_dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">partner_client</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.substrafl_utils.Experiment" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
+<p>Experiment class.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>strategies</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a>) – </p></li>
+<li><p><strong>num_rounds_list</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em>]</em>) – </p></li>
+<li><p><strong>ds_client</strong> (<em>Client</em><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>train_data_nodes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em>[</em><em>substrafl.nodes.train_data_node.TrainDataNode</em><em>] </em><em>| </em><em>None</em>) – </p></li>
+<li><p><strong>test_data_nodes</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em>[</em><em>substrafl.nodes.test_data_node.TestDataNode</em><em>] </em><em>| </em><em>None</em>) – </p></li>
+<li><p><strong>aggregation_node</strong> (<em>AggregationNode</em><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>evaluation_frequency</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><em>int</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>experiment_folder</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a>) – </p></li>
+<li><p><strong>clean_models</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – </p></li>
+<li><p><strong>fedeca_path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>algo_dependencies</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>partner_client</strong> (<em>Client</em><em> | </em><em>None</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.utils.substrafl_utils.Experiment.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nb_clients</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'uniform'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_method_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'subprocess'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">urls</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tokens</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.substrafl_utils.Experiment.fit" title="Permalink to this definition">¶</a></dt>
+<dd><p>Fit strategies on global data split across clients.</p>
+<p>For test if provided we use test_data_nodes from int or the
+train_data_nodes in the latter train=test.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">pd.DataFrame</span></code>) – The global data to be split has to be a dataframe as we only support
+one opener type.</p></li>
+<li><p><strong>nb_clients</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[int</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>, <em>optional</em>) – The number of clients used to split data across, by default None</p></li>
+<li><p><strong>split_method</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[Callable</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>, <em>optional</em>) – How to split data across the nb_clients, by default None.</p></li>
+<li><p><strong>split_method_kwargs</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[Callable</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>, <em>optional</em>) – Argument of the function used to split data, by default None.</p></li>
+<li><p><strong>data_path</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[str</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>) – Where to store the data on disk when backend is not remote.</p></li>
+<li><p><strong>backend_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>) – The backend to use for substra. Can be either:
+[“subprocess”, “docker”, “remote”]. Defaults to “subprocess”.</p></li>
+<li><p><strong>urls</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[list[str]</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>) – Urls corresponding to clients API if using remote backend_type.
+Defaults to None.</p></li>
+<li><p><strong>tokens</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[list[str]</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>) – Tokens necessary to authenticate each client API if backend_type
+is remote. Defauts to None.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.utils.substrafl_utils.Experiment.get_outmodel">
+<span class="sig-name descname"><span class="pre">get_outmodel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx_task</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.substrafl_utils.Experiment.get_outmodel" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get the output model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>task_name</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>) – Name of the task.</p></li>
+<li><p><strong>strategy_idx</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>, <em>optional</em>) – Index of the strategy, by default 0.</p></li>
+<li><p><strong>idx_task</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>, <em>optional</em>) – Index of the task, by default 0.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.utils.substrafl_utils.Experiment.reset_experiment">
+<span class="sig-name descname"><span class="pre">reset_experiment</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.substrafl_utils.Experiment.reset_experiment" title="Permalink to this definition">¶</a></dt>
+<dd><p>Reset the state of the object.</p>
+<p>So it can be fit with a new dataset.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="fedeca.utils.substrafl_utils.Experiment.run">
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_strategies_to_run</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.substrafl_utils.Experiment.run" title="Permalink to this definition">¶</a></dt>
+<dd><p>Run the experiment.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>num_strategies_to_run</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">int</span></code></a>, <em>optional</em>) – Number of strategies to run, by default None.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="fedeca.utils.substrafl_utils.SubstraflTorchDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">SubstraflTorchDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_from_opener</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_inference</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_columns</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">columns_to_drop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'float64'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_torch_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.substrafl_utils.SubstraflTorchDataset" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></p>
+<p>Substra torch dataset class.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>is_inference</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – </p></li>
+<li><p><strong>target_columns</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>columns_to_drop</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>fit_cols</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><em>list</em></a><em> | </em><em>None</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.utils.substrafl_utils.download_train_task_models_by_round">
+<span class="sig-name descname"><span class="pre">download_train_task_models_by_round</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">client</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dest_folder</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_plan_key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">round_idx</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.substrafl_utils.download_train_task_models_by_round" title="Permalink to this definition">¶</a></dt>
+<dd><p>Download models associated with a specific round of a train task.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.utils.substrafl_utils.get_outmodel_function">
+<span class="sig-name descname"><span class="pre">get_outmodel_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">client</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_plan_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx_task</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tasks_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.substrafl_utils.get_outmodel_function" title="Permalink to this definition">¶</a></dt>
+<dd><p>Retrieve an output model from a task or tasks_dict.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.utils.substrafl_utils.get_simu_state_from_round">
+<span class="sig-name descname"><span class="pre">get_simu_state_from_round</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">simu_memory</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">client_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">round_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.substrafl_utils.get_simu_state_from_round" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get the simulation state from a specific round and client.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>simu_memory</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">SimuStatesMemory</span></code>) – Simulation memory.</p></li>
+<li><p><strong>client_id</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>) – Client ID.</p></li>
+<li><p><strong>round_idx</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Optional[int]</span></code>, <em>optional</em>) – Round index, by default None.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.utils.substrafl_utils.make_accuracy_function">
+<span class="sig-name descname"><span class="pre">make_accuracy_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">treatment_col</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.substrafl_utils.make_accuracy_function" title="Permalink to this definition">¶</a></dt>
+<dd><p>Build accuracy function.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>treatment_col</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">str,</span></code>) – Column name for the treatment allocation.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.utils.substrafl_utils.make_c_index_function">
+<span class="sig-name descname"><span class="pre">make_c_index_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">duration_col</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">event_col</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.substrafl_utils.make_c_index_function" title="Permalink to this definition">¶</a></dt>
+<dd><p>Build C-index function.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>duration_col</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">str,</span></code>) – Column name for the duration.</p></li>
+<li><p><strong>event_col</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">str,</span></code>) – Column name for the event.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.utils.substrafl_utils.make_substrafl_torch_dataset_class">
+<span class="sig-name descname"><span class="pre">make_substrafl_torch_dataset_class</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target_cols</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">event_col</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">duration_col</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'float64'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_torch_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">client_identifier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.substrafl_utils.make_substrafl_torch_dataset_class" title="Permalink to this definition">¶</a></dt>
+<dd><p>Create a custom SubstraflTorchDataset class for survival analysis.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>target_cols</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a>) – List of target columns.</p></li>
+<li><p><strong>event_col</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>) – Name of the event column.</p></li>
+<li><p><strong>duration_col</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>) – Name of the duration column.</p></li>
+<li><p><strong>fit_cols</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[list</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">None]</span></code>, <em>optional</em>) – List of columns to fit on, by default None will use all
+columns that can be cast to numeric except target_columns.</p></li>
+<li><p><strong>dtype</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">str</span></code></a>, <em>optional</em>) – Data type, by default “float64”.</p></li>
+<li><p><strong>return_torch_tensors</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">bool</span></code></a>, <em>optional</em>) – Returns torch.Tensor. Defaults to False.</p></li>
+<li><p><strong>client_identifier</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Union[None</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">str]</span></code>, <em>optional</em>) – Name of the column that identifies the client and that is to be dropped.
+By default assumes there is no client identifier.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Custom SubstraflTorchDataset class.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#type" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">type</span></code></a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<span class="target" id="module-fedeca.utils.tensor_utils"></span><p>Functions for tensors comparison.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="fedeca.utils.tensor_utils.compare_tensors_lists">
+<span class="sig-name descname"><span class="pre">compare_tensors_lists</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor_list_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_list_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#fedeca.utils.tensor_utils.compare_tensors_lists" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compare list of tensors up to a certain precision.</p>
+<p>The criteria that is checked is the following: <a href="#id1"><span class="problematic" id="id2">|x - y|</span></a> &lt;= <a href="#id3"><span class="problematic" id="id4">|y|</span></a> * rtol + atol.
+So there are two terms to consider. The first one is relative (rtol) and the second
+is absolute (atol). The default for atol is a bit low for float32 tensors. We keep
+the defaults everywhereto be safe except in the tests computed gradients wrt theory
+where we raise atol to 1e-6. It makes sens in this case because it matches the
+expected precision for slightly different float32 ops that should theoretically
+give the exact same result.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>tensor_list_a</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a>) – a list of tensors</p></li>
+<li><p><strong>tensor_list_b</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a>) – a list of tensors</p></li>
+<li><p><strong>atol</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, <em>optional</em>) – absolute difference tolerance for tensor-to-tensor comparison. Default to 1e-5.</p></li>
+<li><p><strong>rtol</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">float</span></code></a>, <em>optional</em>) – relative difference tolerance for tensor-to-tensor comparison. Default to 1e-8.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<span class="target" id="module-fedeca.utils.typing"></span><p>Module defining type alias.</p>
+</section>
+
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+<li class="toctree-l1"><a class="reference internal" href="quickstart.html#distributed-analysis">Distributed Analysis</a></li>
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+<h1 id="index">Index</h1>
+
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+ <a href="#A"><strong>A</strong></a>
+ | <a href="#B"><strong>B</strong></a>
+ | <a href="#C"><strong>C</strong></a>
+ | <a href="#D"><strong>D</strong></a>
+ | <a href="#E"><strong>E</strong></a>
+ | <a href="#F"><strong>F</strong></a>
+ | <a href="#G"><strong>G</strong></a>
+ | <a href="#L"><strong>L</strong></a>
+ | <a href="#M"><strong>M</strong></a>
+ | <a href="#N"><strong>N</strong></a>
+ | <a href="#P"><strong>P</strong></a>
+ | <a href="#R"><strong>R</strong></a>
+ | <a href="#S"><strong>S</strong></a>
+ | <a href="#T"><strong>T</strong></a>
+ | <a href="#U"><strong>U</strong></a>
+ | <a href="#W"><strong>W</strong></a>
+ 
+</div>
+<h2 id="A">A</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/strategies.html#fedeca.strategies.WebDisco.aggregate_moments">aggregate_moments() (WebDisco method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/utils.html#fedeca.utils.moments_utils.aggregation_mean">aggregation_mean() (in module fedeca.utils.moments_utils)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="B">B</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/competitors.html#fedeca.MatchingAjudsted.bootstrap_sample">bootstrap_sample() (MatchingAjudsted method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/strategies.html#fedeca.strategies.WebDisco.build_compute_plan">build_compute_plan() (WebDisco method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/iptw.html#fedeca.FedECA.check_cp_status">check_cp_status() (FedECA method)</a>
+</li>
+      <li><a href="api/iptw.html#fedeca.FedECA.clients_names">clients_names (FedECA attribute)</a>
+</li>
+      <li><a href="api/utils.html#fedeca.utils.tensor_utils.compare_tensors_lists">compare_tensors_lists() (in module fedeca.utils.tensor_utils)</a>
+</li>
+      <li><a href="api/utils.html#fedeca.utils.moments_utils.compute_centered_moment">compute_centered_moment() (in module fedeca.utils.moments_utils)</a>
+</li>
+      <li><a href="api/utils.html#fedeca.utils.moments_utils.compute_global_moments">compute_global_moments() (in module fedeca.utils.moments_utils)</a>
+</li>
+      <li><a href="api/algorithms.html#fedeca.algorithms.TorchWebDiscoAlgo.compute_local_phi_stats">compute_local_phi_stats() (TorchWebDiscoAlgo method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/iptw.html#fedeca.FedECA.compute_propensity_scores">compute_propensity_scores() (FedECA method)</a>
+</li>
+      <li><a href="api/iptw.html#fedeca.FedECA.compute_summary">compute_summary() (FedECA method)</a>
+</li>
+      <li><a href="api/strategies.html#fedeca.strategies.webdisco_utils.compute_summary_function">compute_summary_function() (in module fedeca.strategies.webdisco_utils)</a>
+</li>
+      <li><a href="api/utils.html#fedeca.utils.moments_utils.compute_uncentered_moment">compute_uncentered_moment() (in module fedeca.utils.moments_utils)</a>
+</li>
+      <li><a href="api/algorithms.html#fedeca.algorithms.TorchWebDiscoAlgo.compute_X_y_and_propensity_weights">compute_X_y_and_propensity_weights() (TorchWebDiscoAlgo method)</a>
+</li>
+      <li><a href="api/scripts.html#fedeca.scripts.substra_assets.csv_opener.CSVOpener">CSVOpener (class in fedeca.scripts.substra_assets.csv_opener)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="D">D</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/utils.html#fedeca.utils.substrafl_utils.download_train_task_models_by_round">download_train_task_models_by_round() (in module fedeca.utils.substrafl_utils)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="E">E</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/utils.html#fedeca.utils.substrafl_utils.Experiment">Experiment (class in fedeca.utils.substrafl_utils)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/scripts.html#fedeca.scripts.substra_assets.csv_opener.CSVOpener.fake_data">fake_data() (CSVOpener method)</a>
+</li>
+      <li><a href="api/iptw.html#fedeca.FedECA">FedECA (class in fedeca)</a>
+</li>
+      <li>
+    fedeca.metrics.metrics
+
+      <ul>
+        <li><a href="api/metrics.html#module-fedeca.metrics.metrics">module</a>
+</li>
+      </ul></li>
+      <li>
+    fedeca.strategies.bootstraper
+
+      <ul>
+        <li><a href="api/strategies.html#module-fedeca.strategies.bootstraper">module</a>
+</li>
+      </ul></li>
+      <li>
+    fedeca.strategies.webdisco_utils
+
+      <ul>
+        <li><a href="api/strategies.html#module-fedeca.strategies.webdisco_utils">module</a>
+</li>
+      </ul></li>
+      <li>
+    fedeca.utils.data_utils
+
+      <ul>
+        <li><a href="api/utils.html#module-fedeca.utils.data_utils">module</a>
+</li>
+      </ul></li>
+      <li>
+    fedeca.utils.moments_utils
+
+      <ul>
+        <li><a href="api/utils.html#module-fedeca.utils.moments_utils">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    fedeca.utils.substrafl_utils
+
+      <ul>
+        <li><a href="api/utils.html#module-fedeca.utils.substrafl_utils">module</a>
+</li>
+      </ul></li>
+      <li>
+    fedeca.utils.tensor_utils
+
+      <ul>
+        <li><a href="api/utils.html#module-fedeca.utils.tensor_utils">module</a>
+</li>
+      </ul></li>
+      <li>
+    fedeca.utils.typing
+
+      <ul>
+        <li><a href="api/utils.html#module-fedeca.utils.typing">module</a>
+</li>
+      </ul></li>
+      <li><a href="api/utils.html#fedeca.utils.substrafl_utils.Experiment.fit">fit() (Experiment method)</a>
+
+      <ul>
+        <li><a href="api/iptw.html#fedeca.FedECA.fit">(FedECA method)</a>
+</li>
+        <li><a href="api/competitors.html#fedeca.MatchingAjudsted.fit">(MatchingAjudsted method)</a>
+</li>
+        <li><a href="api/competitors.html#fedeca.NaiveComparison.fit">(NaiveComparison method)</a>
+</li>
+        <li><a href="api/competitors.html#fedeca.PooledIPTW.fit">(PooledIPTW method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="G">G</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/utils.html#fedeca.utils.data_utils.generate_cox_data_and_substra_clients">generate_cox_data_and_substra_clients() (in module fedeca.utils.data_utils)</a>
+</li>
+      <li><a href="api/scripts.html#fedeca.scripts.substra_assets.csv_opener.CSVOpener.get_data">get_data() (CSVOpener method)</a>
+</li>
+      <li><a href="api/iptw.html#fedeca.FedECA.get_final_cox_model">get_final_cox_model() (FedECA method)</a>
+</li>
+      <li><a href="api/strategies.html#fedeca.strategies.webdisco_utils.get_final_cox_model_function">get_final_cox_model_function() (in module fedeca.strategies.webdisco_utils)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/strategies.html#fedeca.strategies.webdisco_utils.get_last_algo_from_round_count">get_last_algo_from_round_count() (in module fedeca.strategies.webdisco_utils)</a>
+</li>
+      <li><a href="api/utils.html#fedeca.utils.substrafl_utils.Experiment.get_outmodel">get_outmodel() (Experiment method)</a>
+</li>
+      <li><a href="api/utils.html#fedeca.utils.substrafl_utils.get_outmodel_function">get_outmodel_function() (in module fedeca.utils.substrafl_utils)</a>
+</li>
+      <li><a href="api/utils.html#fedeca.utils.substrafl_utils.get_simu_state_from_round">get_simu_state_from_round() (in module fedeca.utils.substrafl_utils)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/algorithms.html#fedeca.algorithms.TorchWebDiscoAlgo.local_uncentered_moments">local_uncentered_moments() (TorchWebDiscoAlgo method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/utils.html#fedeca.utils.substrafl_utils.make_accuracy_function">make_accuracy_function() (in module fedeca.utils.substrafl_utils)</a>
+</li>
+      <li><a href="api/strategies.html#fedeca.strategies.bootstraper.make_bootstrap_metric_function">make_bootstrap_metric_function() (in module fedeca.strategies.bootstraper)</a>
+</li>
+      <li><a href="api/strategies.html#fedeca.strategies.bootstraper.make_bootstrap_strategy">make_bootstrap_strategy() (in module fedeca.strategies.bootstraper)</a>
+</li>
+      <li><a href="api/utils.html#fedeca.utils.substrafl_utils.make_c_index_function">make_c_index_function() (in module fedeca.utils.substrafl_utils)</a>
+</li>
+      <li><a href="api/utils.html#fedeca.utils.substrafl_utils.make_substrafl_torch_dataset_class">make_substrafl_torch_dataset_class() (in module fedeca.utils.substrafl_utils)</a>
+</li>
+      <li><a href="api/competitors.html#fedeca.MatchingAjudsted">MatchingAjudsted (class in fedeca)</a>
+</li>
+      <li>
+    module
+
+      <ul>
+        <li><a href="api/metrics.html#module-fedeca.metrics.metrics">fedeca.metrics.metrics</a>
+</li>
+        <li><a href="api/strategies.html#module-fedeca.strategies.bootstraper">fedeca.strategies.bootstraper</a>
+</li>
+        <li><a href="api/strategies.html#module-fedeca.strategies.webdisco_utils">fedeca.strategies.webdisco_utils</a>
+</li>
+        <li><a href="api/utils.html#module-fedeca.utils.data_utils">fedeca.utils.data_utils</a>
+</li>
+        <li><a href="api/utils.html#module-fedeca.utils.moments_utils">fedeca.utils.moments_utils</a>
+</li>
+        <li><a href="api/utils.html#module-fedeca.utils.substrafl_utils">fedeca.utils.substrafl_utils</a>
+</li>
+        <li><a href="api/utils.html#module-fedeca.utils.tensor_utils">fedeca.utils.tensor_utils</a>
+</li>
+        <li><a href="api/utils.html#module-fedeca.utils.typing">fedeca.utils.typing</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="N">N</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/competitors.html#fedeca.NaiveComparison">NaiveComparison (class in fedeca)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/strategies.html#fedeca.strategies.WebDisco.name">name (WebDisco property)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/strategies.html#fedeca.strategies.WebDisco.perform_evaluation">perform_evaluation() (WebDisco method)</a>
+</li>
+      <li><a href="api/strategies.html#fedeca.strategies.WebDisco.perform_round">perform_round() (WebDisco method)</a>
+</li>
+      <li><a href="api/competitors.html#fedeca.MatchingAjudsted.point_estimate">point_estimate() (MatchingAjudsted method)</a>
+
+      <ul>
+        <li><a href="api/competitors.html#fedeca.NaiveComparison.point_estimate">(NaiveComparison method)</a>
+</li>
+        <li><a href="api/competitors.html#fedeca.PooledIPTW.point_estimate">(PooledIPTW method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/competitors.html#fedeca.PooledIPTW">PooledIPTW (class in fedeca)</a>
+</li>
+      <li><a href="api/algorithms.html#fedeca.algorithms.TorchWebDiscoAlgo.predict">predict() (TorchWebDiscoAlgo method)</a>
+</li>
+      <li><a href="api/iptw.html#fedeca.FedECA.print_summary">print_summary() (FedECA method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="R">R</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/utils.html#fedeca.utils.substrafl_utils.Experiment.reset_experiment">reset_experiment() (Experiment method)</a>
+
+      <ul>
+        <li><a href="api/iptw.html#fedeca.FedECA.reset_experiment">(FedECA method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/utils.html#fedeca.utils.substrafl_utils.Experiment.run">run() (Experiment method)</a>
+
+      <ul>
+        <li><a href="api/iptw.html#fedeca.FedECA.run">(FedECA method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/iptw.html#fedeca.FedECA.set_propensity_model_strategy">set_propensity_model_strategy() (FedECA method)</a>
+</li>
+      <li><a href="api/utils.html#fedeca.utils.data_utils.split_control_over_centers">split_control_over_centers() (in module fedeca.utils.data_utils)</a>
+</li>
+      <li><a href="api/utils.html#fedeca.utils.data_utils.split_dataframe_across_clients">split_dataframe_across_clients() (in module fedeca.utils.data_utils)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/metrics.html#fedeca.metrics.metrics.standardized_mean_diff">standardized_mean_diff() (in module fedeca.metrics.metrics)</a>
+</li>
+      <li><a href="api/algorithms.html#fedeca.algorithms.TorchWebDiscoAlgo.strategies">strategies (TorchWebDiscoAlgo property)</a>
+</li>
+      <li><a href="api/utils.html#fedeca.utils.substrafl_utils.SubstraflTorchDataset">SubstraflTorchDataset (class in fedeca.utils.substrafl_utils)</a>
+</li>
+      <li><a href="api/algorithms.html#fedeca.algorithms.TorchWebDiscoAlgo.summary">summary() (TorchWebDiscoAlgo method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="T">T</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/algorithms.html#fedeca.algorithms.TorchWebDiscoAlgo">TorchWebDiscoAlgo (class in fedeca.algorithms)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/algorithms.html#fedeca.algorithms.TorchWebDiscoAlgo.train">train() (TorchWebDiscoAlgo method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="U">U</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/utils.html#fedeca.utils.data_utils.uniform_split">uniform_split() (in module fedeca.utils.data_utils)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="W">W</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api/strategies.html#fedeca.strategies.WebDisco">WebDisco (class in fedeca.strategies)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+
+
+           </div>
+           
+          </div>
+          <footer>
+  
+
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+<p class="caption" role="heading"><span class="caption-text">Getting Started Instructions</span></p>
+<ul>
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+<li class="toctree-l1"><a class="reference internal" href="quickstart.html#pooled-iptw-analysis">Pooled IPTW analysis</a></li>
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+  <section id="fedeca-documentation">
+<h1>FedECA documentation<a class="headerlink" href="#fedeca-documentation" title="Permalink to this heading">¶</a></h1>
+<p>This package allows to perform both simulations and deployments of federated
+external control arms (FedECA) analyses.</p>
+<p>Before using this code make sure to:</p>
+<ol class="arabic simple">
+<li><p>read and accept the terms of the license license.md that can be found at the root of the repository.</p></li>
+<li><p>read <a class="reference external" href="https://docs.substra.org/en/stable/additional/privacy-strategy.html">substra’s privacy strategy</a></p></li>
+<li><p>read our <a class="reference external" href="https://arxiv.org/abs/2311.16984">companion article</a></p></li>
+<li><p><a class="reference external" href="https://opacus.ai/docs/faq#:~:text=What%20is%20the%20secure_rng,the%20security%20this%20brings.">activate secure rng in Opacus</a> if you plan on using differential privacy.</p></li>
+</ol>
+<section id="citing-this-work">
+<h2>Citing this work<a class="headerlink" href="#citing-this-work" title="Permalink to this heading">¶</a></h2>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@ARTICLE</span><span class="p">{</span><span class="n">terrail2023fedeca</span><span class="p">,</span>
+     <span class="n">author</span> <span class="o">=</span> <span class="p">{{</span><span class="n">Ogier</span> <span class="n">du</span> <span class="n">Terrail</span><span class="p">},</span> <span class="n">Jean</span> <span class="ow">and</span> <span class="p">{</span><span class="n">Klopfenstein</span><span class="p">},</span> <span class="n">Quentin</span> <span class="ow">and</span> <span class="p">{</span><span class="n">Li</span><span class="p">},</span> <span class="n">Honghao</span> <span class="ow">and</span> <span class="p">{</span><span class="n">Mayer</span><span class="p">},</span> <span class="n">Imke</span> <span class="ow">and</span> <span class="p">{</span><span class="n">Loiseau</span><span class="p">},</span> <span class="n">Nicolas</span> <span class="ow">and</span> <span class="p">{</span><span class="n">Hallal</span><span class="p">},</span> <span class="n">Mohammad</span> <span class="ow">and</span> <span class="p">{</span><span class="n">Debouver</span><span class="p">},</span> <span class="n">Michael</span> <span class="ow">and</span> <span class="p">{</span><span class="n">Camalon</span><span class="p">},</span> <span class="n">Thibault</span> <span class="ow">and</span> <span class="p">{</span><span class="n">Fouqueray</span><span class="p">},</span> <span class="n">Thibault</span> <span class="ow">and</span> <span class="p">{</span><span class="n">Arellano</span> <span class="n">Castro</span><span class="p">},</span> <span class="n">Jorge</span> <span class="ow">and</span> <span class="p">{</span><span class="n">Yanes</span><span class="p">},</span> <span class="n">Zahia</span> <span class="ow">and</span> <span class="p">{</span><span class="n">Dahan</span><span class="p">},</span> <span class="n">Laetitia</span> <span class="ow">and</span> <span class="p">{</span><span class="n">Ta</span><span class="p">{</span>\<span class="s2">&quot;\i}eb}, Julien and {Laurent-Puig}, Pierre and </span><span class="si">{Bachet}</span><span class="s2">, Jean-Baptiste and </span><span class="si">{Zhao}</span><span class="s2">, Shulin and </span><span class="si">{Nicolle}</span><span class="s2">, Remy and </span><span class="si">{Cros}</span><span class="s2">, J{</span><span class="se">\&#39;</span><span class="s2">e}rome and </span><span class="si">{Gonzalez}</span><span class="s2">, Daniel and {Carreras-Torres}, Robert and {Garcia Velasco}, Adelaida and </span><span class="si">{Abdilleh}</span><span class="s2">, Kawther and </span><span class="si">{Doss}</span><span class="s2">, Sudheer and </span><span class="si">{Balazard}</span><span class="s2">, F{</span><span class="se">\&#39;</span><span class="s2">e}lix and </span><span class="si">{Andreux}</span><span class="s2">, Mathieu},</span>
+     <span class="n">title</span> <span class="o">=</span> <span class="s2">&quot;{FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings}&quot;</span><span class="p">,</span>
+     <span class="n">journal</span> <span class="o">=</span> <span class="p">{</span><span class="n">arXiv</span> <span class="n">e</span><span class="o">-</span><span class="n">prints</span><span class="p">},</span>
+     <span class="n">keywords</span> <span class="o">=</span> <span class="p">{</span><span class="n">Statistics</span> <span class="o">-</span> <span class="n">Methodology</span><span class="p">,</span> <span class="n">Computer</span> <span class="n">Science</span> <span class="o">-</span> <span class="n">Distributed</span><span class="p">,</span> <span class="n">Parallel</span><span class="p">,</span> <span class="ow">and</span> <span class="n">Cluster</span> <span class="n">Computing</span><span class="p">,</span> <span class="n">Computer</span> <span class="n">Science</span> <span class="o">-</span> <span class="n">Machine</span> <span class="n">Learning</span><span class="p">},</span>
+     <span class="n">year</span> <span class="o">=</span> <span class="mi">2023</span><span class="p">,</span>
+     <span class="n">month</span> <span class="o">=</span> <span class="n">nov</span><span class="p">,</span>
+     <span class="n">eid</span> <span class="o">=</span> <span class="p">{</span><span class="n">arXiv</span><span class="p">:</span><span class="mf">2311.16984</span><span class="p">},</span>
+     <span class="n">pages</span> <span class="o">=</span> <span class="p">{</span><span class="n">arXiv</span><span class="p">:</span><span class="mf">2311.16984</span><span class="p">},</span>
+     <span class="n">doi</span> <span class="o">=</span> <span class="p">{</span><span class="mf">10.48550</span><span class="o">/</span><span class="n">arXiv</span><span class="mf">.2311.16984</span><span class="p">},</span>
+     <span class="n">archivePrefix</span> <span class="o">=</span> <span class="p">{</span><span class="n">arXiv</span><span class="p">},</span>
+     <span class="n">eprint</span> <span class="o">=</span> <span class="p">{</span><span class="mf">2311.16984</span><span class="p">},</span>
+     <span class="n">primaryClass</span> <span class="o">=</span> <span class="p">{</span><span class="n">stat</span><span class="o">.</span><span class="n">ME</span><span class="p">},</span>
+     <span class="n">adsurl</span> <span class="o">=</span> <span class="p">{</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">ui</span><span class="o">.</span><span class="n">adsabs</span><span class="o">.</span><span class="n">harvard</span><span class="o">.</span><span class="n">edu</span><span class="o">/</span><span class="nb">abs</span><span class="o">/</span><span class="mi">2023</span><span class="n">arXiv231116984O</span><span class="p">},</span>
+     <span class="n">adsnote</span> <span class="o">=</span> <span class="p">{</span><span class="n">Provided</span> <span class="n">by</span> <span class="n">the</span> <span class="n">SAO</span><span class="o">/</span><span class="n">NASA</span> <span class="n">Astrophysics</span> <span class="n">Data</span> <span class="n">System</span><span class="p">}</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</section>
+<section id="license">
+<h2>License<a class="headerlink" href="#license" title="Permalink to this heading">¶</a></h2>
+<p>FedECA is released under a custom license that can be found under license.md at the root of the repository.</p>
+<div class="toctree-wrapper compound">
+<p class="caption" role="heading"><span class="caption-text">Installation</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="quickstart.html#pooled-iptw-analysis">Pooled IPTW analysis</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quickstart.html#distributed-analysis">Distributed Analysis</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="quickstart.html">Quickstart</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quickstart.html#pooled-iptw-analysis">Pooled IPTW analysis</a></li>
+<li class="toctree-l1"><a class="reference internal" href="quickstart.html#distributed-analysis">Distributed Analysis</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="api/competitors.html">fedeca.competitors</a></li>
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+           <div itemprop="articleBody">
+            
+  <section id="installation">
+<h1>Installation<a class="headerlink" href="#installation" title="Permalink to this heading">¶</a></h1>
+<p>To install the package, create an env with python <code class="docutils literal notranslate"><span class="pre">3.9</span></code> with conda</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda<span class="w"> </span>create<span class="w"> </span>-n<span class="w"> </span>fedeca<span class="w"> </span><span class="nv">python</span><span class="o">=</span><span class="m">3</span>.9
+conda<span class="w"> </span>activate<span class="w"> </span>fedeca
+</pre></div>
+</div>
+<p>Within the environment, install the package by running:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">git</span> <span class="n">clone</span> <span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">owkin</span><span class="o">/</span><span class="n">fedeca</span><span class="o">.</span><span class="n">git</span>
+<span class="n">pip</span> <span class="n">install</span> <span class="o">-</span><span class="n">e</span> <span class="s2">&quot;.[all_extra]&quot;</span>
+</pre></div>
+</div>
+<p>If you plan developing, you should also install the pre-commit hooks</p>
+<p><a href="#id1"><span class="problematic" id="id2">``</span></a><a href="#id3"><span class="problematic" id="id4">`</span></a>bash
+pre-commit install</p>
+</section>
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+  <section id="quickstart">
+<h1>Quickstart<a class="headerlink" href="#quickstart" title="Permalink to this heading">¶</a></h1>
+<p>FedECA tries to mimic scikit-learn API as much as possible with the constraints
+of distributed learning.
+The first step in data science is always the data.
+We need to first use or generate some survival data in pandas.dataframe format.
+Note that fedeca should work on any data format, provided that the
+return type of the substra opener is indeed a pandas.dataframe but let’s keep
+it simple in this quickstart.</p>
+<p>Here we will use fedeca utils which will generate some synthetic survival data
+following CoxPH assumptions:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">fedeca.utils.survival_utils</span> <span class="kn">import</span> <span class="n">CoxData</span>
+<span class="c1"># Let&#39;s generate 1000 data samples with 10 covariates</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">CoxData</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">n_samples</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">ndim</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">generate_dataframe</span><span class="p">()</span>
+
+<span class="c1"># We remove the true propensity score</span>
+<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;propensity_scores&quot;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Let’s inspect the data that we have here.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">info</span><span class="p">())</span>
+<span class="c1"># &lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;</span>
+<span class="c1"># RangeIndex: 1000 entries, 0 to 999</span>
+<span class="c1"># Data columns (total 13 columns):</span>
+<span class="c1">#  #   Column     Non-Null Count  Dtype</span>
+<span class="c1"># ---  ------     --------------  -----</span>
+<span class="c1">#  0   X_0        1000 non-null   float64</span>
+<span class="c1">#  1   X_1        1000 non-null   float64</span>
+<span class="c1">#  2   X_2        1000 non-null   float64</span>
+<span class="c1">#  3   X_3        1000 non-null   float64</span>
+<span class="c1">#  4   X_4        1000 non-null   float64</span>
+<span class="c1">#  5   X_5        1000 non-null   float64</span>
+<span class="c1">#  6   X_6        1000 non-null   float64</span>
+<span class="c1">#  7   X_7        1000 non-null   float64</span>
+<span class="c1">#  8   X_8        1000 non-null   float64</span>
+<span class="c1">#  9   X_9        1000 non-null   float64</span>
+<span class="c1">#  10  time       1000 non-null   float64</span>
+<span class="c1">#  11  event      1000 non-null   uint8</span>
+<span class="c1">#  12  treatment  1000 non-null   uint8</span>
+<span class="c1"># dtypes: float64(11), uint8(2)</span>
+<span class="c1"># memory usage: 88.0 KB</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
+<span class="c1">#         X_0       X_1       X_2       X_3       X_4       X_5       X_6       X_7       X_8       X_9      time  event  treatment</span>
+<span class="c1"># 0 -0.918373 -0.814340 -0.148994  0.482720 -1.130384 -1.254769 -0.462002  1.451622  1.199705  0.133197  2.573516      1          1</span>
+<span class="c1"># 1  0.360051 -0.863619  0.198673  0.330630 -0.189184 -0.802424 -1.694990 -0.989009 -0.421245 -0.112665  0.519108      1          1</span>
+<span class="c1"># 2  0.442502  0.024682  0.069500 -0.398015 -0.521236 -0.824907  0.373018  1.016843  0.765661  0.858817  0.652803      1          1</span>
+<span class="c1"># 3 -0.783965 -1.116391 -1.482413 -2.039827 -1.639304 -0.500380 -0.298467 -1.801688 -0.743004 -0.724039  0.074925      1          1</span>
+<span class="c1"># 4 -0.199620 -0.652347 -0.018776  0.004630 -0.122242 -0.413490 -0.450718 -0.761894 -1.323135 -0.234899  0.006951      1          1</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;treatment&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">())</span>
+<span class="c1"># array([1, 0], dtype=uint8)</span>
+<span class="n">df</span><span class="p">[</span><span class="s2">&quot;treatment&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+<span class="c1"># 500</span>
+</pre></div>
+</div>
+<p>So we have survival data with covariates and a binary treatment variable.
+Let’s inspect it using proper survival plots using the great survival analysis
+package <a class="reference external" href="https://github.com/CamDavidsonPilon/lifelines">lifelines</a> that was a
+source of inspiration for fedeca:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">lifelines</span> <span class="kn">import</span> <span class="n">KaplanMeierFitter</span> <span class="k">as</span> <span class="n">KMF</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="n">treatments</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+<span class="n">kms</span> <span class="o">=</span> <span class="p">[</span><span class="n">KMF</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">durations</span><span class="o">=</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;treatment&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">t</span><span class="p">][</span><span class="s2">&quot;time&quot;</span><span class="p">],</span> <span class="n">event_observed</span><span class="o">=</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;treatment&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">t</span><span class="p">][</span><span class="s2">&quot;event&quot;</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">treatments</span><span class="p">]</span>
+
+<span class="n">axs</span> <span class="o">=</span> <span class="p">[</span><span class="n">km</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s2">&quot;treated&quot;</span> <span class="k">if</span> <span class="n">t</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="s2">&quot;untreated&quot;</span><span class="p">)</span> <span class="k">for</span> <span class="n">km</span><span class="p">,</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">kms</span><span class="p">,</span> <span class="n">treatments</span><span class="p">)]</span>
+<span class="n">axs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Survival Probability&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1500</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s2">&quot;treated_vs_untreated.pdf&quot;</span><span class="p">,</span> <span class="n">bbox_inches</span><span class="o">=</span><span class="s2">&quot;tight&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Open <code class="docutils literal notranslate"><span class="pre">treated_vs_untreated.pdf</span></code> in your favorite pdf viewer and see for yourself.</p>
+</section>
+<section id="pooled-iptw-analysis">
+<h1>Pooled IPTW analysis<a class="headerlink" href="#pooled-iptw-analysis" title="Permalink to this heading">¶</a></h1>
+<p>The treatment seems to improve survival but it’s hard to say for sure as it might
+simply be due to chance or sampling bias.
+Let’s perform an IPTW analysis to be sure:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">fedeca.competitors</span> <span class="kn">import</span> <span class="n">PooledIPTW</span>
+<span class="n">pooled_iptw</span> <span class="o">=</span> <span class="n">PooledIPTW</span><span class="p">(</span><span class="n">treated_col</span><span class="o">=</span><span class="s2">&quot;treatment&quot;</span><span class="p">,</span> <span class="n">event_col</span><span class="o">=</span><span class="s2">&quot;event&quot;</span><span class="p">,</span> <span class="n">duration_col</span><span class="o">=</span><span class="s2">&quot;time&quot;</span><span class="p">)</span>
+<span class="c1"># Targets is the propensity weights</span>
+<span class="n">pooled_iptw</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">df</span><span class="p">,</span> <span class="n">targets</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">pooled_iptw</span><span class="o">.</span><span class="n">results_</span><span class="p">)</span>
+<span class="c1">#                coef  exp(coef)  se(coef)  coef lower 95%  coef upper 95%  exp(coef) lower 95%  exp(coef) upper 95%  cmp to         z         p  -log2(p)</span>
+<span class="c1"># covariate</span>
+<span class="c1"># treatment  0.041727    1.04261  0.070581       -0.096609        0.180064             0.907911             1.197294     0.0  0.591196  0.554389   0.85103</span>
+</pre></div>
+</div>
+<p>When looking at the <code class="docutils literal notranslate"><span class="pre">p-value=0.554389</span> <span class="pre">&gt;</span> <span class="pre">0.05</span></code>, thus judging by what we observe we
+cannot say for sure that there is a treatment effect. We say the ATE is non significant.</p>
+</section>
+<section id="distributed-analysis">
+<h1>Distributed Analysis<a class="headerlink" href="#distributed-analysis" title="Permalink to this heading">¶</a></h1>
+<p>However in practice data is private and held by different institutions. Therefore
+in practice each client holds a subset of the rows of our dataframe.
+We will simulate this using a realistic scenario where a “pharma” node is developing
+a new drug and thus holds all treated and the rest of the data is split across
+3 other institutions where patients were treated with the old drug.
+We will use the split utils of FedECA.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">fedeca.utils.data_utils</span> <span class="kn">import</span> <span class="n">split_dataframe_across_clients</span>
+
+<span class="n">clients</span><span class="p">,</span> <span class="n">train_data_nodes</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">split_dataframe_across_clients</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">,</span>
+    <span class="n">n_clients</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
+    <span class="n">split_method</span><span class="o">=</span> <span class="s2">&quot;split_control_over_centers&quot;</span><span class="p">,</span>
+    <span class="n">split_method_kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;treatment_info&quot;</span><span class="p">:</span> <span class="s2">&quot;treatment&quot;</span><span class="p">},</span>
+    <span class="n">data_path</span><span class="o">=</span><span class="s2">&quot;./data&quot;</span><span class="p">,</span>
+    <span class="n">backend_type</span><span class="o">=</span><span class="s2">&quot;simu&quot;</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>Note that you can replace split_method by any callable with the signature
+<code class="docutils literal notranslate"><span class="pre">pd.DataFrame</span> <span class="pre">-&gt;</span> <span class="pre">list[int]</span></code> where the list of ints is the split of the indices
+of the df across the different institutions.
+To convince you that the split was effective you can inspect the folder “./data”.
+You will find different subfolders <code class="docutils literal notranslate"><span class="pre">center0</span></code> to <code class="docutils literal notranslate"><span class="pre">center3</span></code> each with different
+parts of the data.
+To unpack a bit what is going on in more depth, we have created a dict of client
+‘clients’,
+which is a dict with 4 keys containing substra API handles towards the different
+institutions and their data.
+<code class="docutils literal notranslate"><span class="pre">train_data_nodes</span></code> is a list of handles towards the datasets of the different institutions
+that were registered through the substra interface using the data in the different
+folders.
+You might have noticed that we did not talk about the <code class="docutils literal notranslate"><span class="pre">backend_type</span></code> argument.
+This argument is used to choose on which network will experiments be run.
+“simu” means in-RAM. If you finish this tutorial do try other values such as:
+“docker” or “subprocess” but expect a significant slow-down as experiments
+get closer and closer to a real distributed system.</p>
+<p>Now let’s try to see if we can reproduce the pooled anaysis in this much more
+complicated distributed setting:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">fedeca</span> <span class="kn">import</span> <span class="n">FedECA</span>
+<span class="c1"># We use the first client as the node, which launches order</span>
+<span class="n">ds_client</span> <span class="o">=</span> <span class="n">clients</span><span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="n">clients</span><span class="o">.</span><span class="n">keys</span><span class="p">())[</span><span class="mi">0</span><span class="p">]]</span>
+<span class="n">fed_iptw</span> <span class="o">=</span> <span class="n">FedECA</span><span class="p">(</span><span class="n">ndim</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">ds_client</span><span class="o">=</span><span class="n">ds_client</span><span class="p">,</span> <span class="n">train_data_nodes</span><span class="o">=</span><span class="n">train_data_nodes</span><span class="p">,</span> <span class="n">treated_col</span><span class="o">=</span><span class="s2">&quot;treatment&quot;</span><span class="p">,</span> <span class="n">duration_col</span><span class="o">=</span><span class="s2">&quot;time&quot;</span><span class="p">,</span> <span class="n">event_col</span><span class="o">=</span><span class="s2">&quot;event&quot;</span><span class="p">,</span> <span class="n">variance_method</span><span class="o">=</span><span class="s2">&quot;robust&quot;</span><span class="p">)</span>
+<span class="n">fed_iptw</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
+<span class="c1"># Final partial log-likelihood:</span>
+<span class="c1"># [-11499.19619422]</span>
+<span class="c1">#        coef  se(coef)  coef lower 95%  coef upper 95%         z         p  exp(coef)  exp(coef) lower 95%  exp(coef) upper 95%</span>
+<span class="c1"># 0  0.041718  0.070581       -0.096618        0.180054  0.591062  0.554479     1.0426             0.907902             1.197282</span>
+</pre></div>
+</div>
+<p>In fact what we did above is both quite verbose. For simulation purposes we
+advise to use directly the scikit-learn inspired syntax:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">fedeca</span> <span class="kn">import</span> <span class="n">FedECA</span>
+
+<span class="n">fed_iptw</span> <span class="o">=</span> <span class="n">FedECA</span><span class="p">(</span><span class="n">ndim</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">treated_col</span><span class="o">=</span><span class="s2">&quot;treatment&quot;</span><span class="p">,</span> <span class="n">event_col</span><span class="o">=</span><span class="s2">&quot;event&quot;</span><span class="p">,</span> <span class="n">duration_col</span><span class="o">=</span><span class="s2">&quot;time&quot;</span><span class="p">)</span>
+<span class="n">fed_iptw</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">n_clients</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">split_method</span><span class="o">=</span><span class="s2">&quot;split_control_over_centers&quot;</span><span class="p">,</span> <span class="n">split_method_kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;treatment_info&quot;</span><span class="p">:</span> <span class="s2">&quot;treatment&quot;</span><span class="p">},</span> <span class="n">data_path</span><span class="o">=</span><span class="s2">&quot;./data&quot;</span><span class="p">,</span> <span class="n">variance_method</span><span class="o">=</span><span class="s2">&quot;robust&quot;</span><span class="p">,</span> <span class="n">backend_type</span><span class="o">=</span><span class="s2">&quot;simu&quot;</span><span class="p">)</span>
+<span class="c1">#        coef  se(coef)  coef lower 95%  coef upper 95%         z         p  exp(coef)  exp(coef) lower 95%  exp(coef) upper 95%</span>
+<span class="c1"># 0  0.041718  0.070581       -0.096618        0.180054  0.591062  0.554479     1.0426             0.907902             1.197282</span>
+</pre></div>
+</div>
+<p>We find a similar p-value ! The distributed analysis is working as expected.
+We recommend to users that made it to here as a next step to use their own data
+and write custom split functions and to test this pipeline under various
+heterogeneity settings.
+Another interesting avenue is to try adding differential privacy to the training
+of the propensity model but that is outside the scope of this quickstart.</p>
+</section>
+
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