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18e967c375c70d966a856c701571502ebab53636..a2307043063e723f536aa1e4b3b70150e03961cc 100644 GIT binary patch delta 78 zcmbQKc~+C9fpzMkjVxNcCOsl0MTvREIf*5i`FZgLMfvGPsl~-pYNu%Qu$JVPB<4)< bX6WI9sYy)%a!WVs@%~|C^xLe>pT`OSn7CB~NXaZNDFSjzGV}8kY$ogT8Zp}?#BWaG)feGKF>3NW#Zb0(pn(hw0HEX@ A7ytkO delta 99 zcmezE{L@)Hz?+#xgaHJUCwvKORoLuhuE@Y(zES)Y4`cLXMqW{g@YKwt(wrPFWrdW? o;*ugDrzA5!FIHo+KCcm@=H?V$eGy(1y^|LxhO)H+O<`aF0I;?kMgRZ+ diff --git a/_downloads/2932d6ac7842b6d781c27c5737ae52fa/plot_marginal_cumulative_incidence_estimation.ipynb b/_downloads/2932d6ac7842b6d781c27c5737ae52fa/plot_marginal_cumulative_incidence_estimation.ipynb index dcf7c13..0e6eb42 100644 --- a/_downloads/2932d6ac7842b6d781c27c5737ae52fa/plot_marginal_cumulative_incidence_estimation.ipynb +++ b/_downloads/2932d6ac7842b6d781c27c5737ae52fa/plot_marginal_cumulative_incidence_estimation.ipynb @@ -22,7 +22,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Since we know the true distribution of the data, we can compute the\ntheoretical cumulative incidence functions (CIFs) by integrating the hazard\nfunctions. The CIFs are the probability of experiencing the event of interest\nbefore time t, given that the subject has not experienced any other event\nbefore time t.\n\nThe following function computes the [hazard function of a Weibull\ndistribution](https://en.wikipedia.org/wiki/Weibull_distribution):\n\n" + "Since we know the true distribution of the data, we can compute the\ntheoretical cumulative incidence functions (CIFs) by integrating the hazard\nfunctions. The CIFs are the probability of experiencing the event of interest\nbefore time t, given that the subject has not experienced any other event\nbefore time t.\n\nThe following function computes the hazard function of a [Weibull\ndistribution](https://en.wikipedia.org/wiki/Weibull_distribution):\n\n" ] }, { @@ -136,7 +136,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip index d6fb0d78af28e7a0b7727b4a587573fe3fda8111..62c06628cf6becc9ec2865b6f5030fc48b70604e 100644 GIT binary patch delta 74 zcmX>ec05cdz?+#xgaHH;1N6c(+}}LgC=|*kmXTPMSd^lWR+^Vwl9`{Uke{ZIs1Uum VmQPlO(RlJ~Ek_QR^2txMk^xTX7;XRn delta 74 zcmX>ec05cdz?+#xgaHJMCVUAyE%2;kqfjWHSae2WRbo+!LRx8Fa!F=>o`_: def weibull_hazard(t, shape=1.0, scale=1.0, **ignored_kwargs): diff --git a/_images/sphx_glr_plot_marginal_cumulative_incidence_estimation_001.png b/_images/sphx_glr_plot_marginal_cumulative_incidence_estimation_001.png index 2015f3d4057fd4c35368f4e903083fd78da9005e..51e16b58503ec2c28e2722ca73fb482c31d88e88 100644 GIT binary patch delta 46 zcmbQ;%{sfAb%KYSv5rDUNl8JmmA-y%Vo5Thka#T>*%1 B5h?%x delta 46 zcmbQ;%{sfAb%KYSk&Z$}Nl8JmmA-y%Vo5H6m!oL@AiZB1i*8VLZX Cf)bzr delta 46 zcmX@Th4u6n)(IYRMmh=^B_##LR{Hw6i6sR&`6W4-NqYH3>H3`_: .. GENERATED FROM PYTHON SOURCE LINES 87-100 @@ -368,7 +368,7 @@ the large time horizons: .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 30.220 seconds) + **Total running time of the script:** (0 minutes 26.655 seconds) .. _sphx_glr_download_auto_examples_plot_marginal_cumulative_incidence_estimation.py: diff --git a/_sources/auto_examples/sg_execution_times.rst.txt b/_sources/auto_examples/sg_execution_times.rst.txt index 031fe0b..2f2148a 100644 --- a/_sources/auto_examples/sg_execution_times.rst.txt +++ b/_sources/auto_examples/sg_execution_times.rst.txt @@ -6,8 +6,8 @@ Computation times ================= -**00:30.220** total execution time for **auto_examples** files: +**00:26.655** total execution time for **auto_examples** files: +---------------------------------------------------------------------------------------------------------------------------------------+-----------+--------+ -| :ref:`sphx_glr_auto_examples_plot_marginal_cumulative_incidence_estimation.py` (``plot_marginal_cumulative_incidence_estimation.py``) | 00:30.220 | 0.0 MB | +| :ref:`sphx_glr_auto_examples_plot_marginal_cumulative_incidence_estimation.py` (``plot_marginal_cumulative_incidence_estimation.py``) | 00:26.655 | 0.0 MB | +---------------------------------------------------------------------------------------------------------------------------------------+-----------+--------+ diff --git a/_static/basic.css b/_static/basic.css index 6157296..e760386 100644 --- a/_static/basic.css +++ b/_static/basic.css @@ -237,6 +237,10 @@ a.headerlink { visibility: hidden; } +a:visited { + color: #551A8B; +} + h1:hover > a.headerlink, h2:hover > a.headerlink, h3:hover > a.headerlink, @@ -670,6 +674,16 @@ dd { margin-left: 30px; } +.sig dd { + margin-top: 0px; + margin-bottom: 0px; +} + +.sig dl { + margin-top: 0px; + margin-bottom: 0px; +} + dl > dd:last-child, dl > dd:last-child > :last-child { margin-bottom: 0; @@ -738,6 +752,14 @@ abbr, acronym { cursor: help; } +.translated { + background-color: rgba(207, 255, 207, 0.2) +} + +.untranslated { + background-color: rgba(255, 207, 207, 0.2) +} + /* -- code displays --------------------------------------------------------- */ pre { diff --git a/_static/documentation_options.js b/_static/documentation_options.js index ba05452..606c5b5 100644 --- a/_static/documentation_options.js +++ b/_static/documentation_options.js @@ -1,5 +1,4 @@ -var DOCUMENTATION_OPTIONS = { - URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), +const DOCUMENTATION_OPTIONS = { VERSION: '0.1.0', LANGUAGE: 'en', COLLAPSE_INDEX: false, diff --git a/_static/searchtools.js b/_static/searchtools.js index 97d56a7..7918c3f 100644 --- a/_static/searchtools.js +++ b/_static/searchtools.js @@ -57,12 +57,12 @@ const _removeChildren = (element) => { const _escapeRegExp = (string) => string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string -const _displayItem = (item, searchTerms) => { +const _displayItem = (item, searchTerms, highlightTerms) => { const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; - const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT; const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; const [docName, title, anchor, descr, score, _filename] = item; @@ -75,20 +75,24 @@ const _displayItem = (item, searchTerms) => { if (dirname.match(/\/index\/$/)) dirname = dirname.substring(0, dirname.length - 6); else if (dirname === "index/") dirname = ""; - requestUrl = docUrlRoot + dirname; + requestUrl = contentRoot + dirname; linkUrl = requestUrl; } else { // normal html builders - requestUrl = docUrlRoot + docName + docFileSuffix; + requestUrl = contentRoot + 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) + if (descr) { listItem.appendChild(document.createElement("span")).innerHTML = " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } else if (showSearchSummary) fetch(requestUrl) .then((responseData) => responseData.text()) @@ -97,6 +101,9 @@ const _displayItem = (item, searchTerms) => { listItem.appendChild( Search.makeSearchSummary(data, searchTerms) ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); }); Search.output.appendChild(listItem); }; @@ -115,14 +122,15 @@ const _finishSearch = (resultCount) => { const _displayNextItem = ( results, resultCount, - searchTerms + searchTerms, + highlightTerms, ) => { // 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); + _displayItem(results.pop(), searchTerms, highlightTerms); setTimeout( - () => _displayNextItem(results, resultCount, searchTerms), + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), 5 ); } @@ -360,7 +368,7 @@ const Search = { // console.info("search results:", Search.lastresults); // print the results - _displayNextItem(results, results.length, searchTerms); + _displayNextItem(results, results.length, searchTerms, highlightTerms); }, /** diff --git a/_static/sg_gallery-dataframe.css b/_static/sg_gallery-dataframe.css index 25be730..fac74c4 100644 --- a/_static/sg_gallery-dataframe.css +++ b/_static/sg_gallery-dataframe.css @@ -19,6 +19,7 @@ table.dataframe { color: var(--sg-text-color); font-size: 12px; table-layout: fixed; + width: auto; } table.dataframe thead { border-bottom: 1px solid var(--sg-text-color); diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js index aae669d..8a96c69 100644 --- a/_static/sphinx_highlight.js +++ b/_static/sphinx_highlight.js @@ -29,14 +29,19 @@ const _highlight = (node, addItems, text, className) => { } span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); parent.insertBefore( span, parent.insertBefore( - document.createTextNode(val.substr(pos + text.length)), + rest, node.nextSibling ) ); node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); if (isInSVG) { const rect = document.createElementNS( @@ -140,5 +145,10 @@ const SphinxHighlight = { }, }; -_ready(SphinxHighlight.highlightSearchWords); -_ready(SphinxHighlight.initEscapeListener); +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/api.html b/api.html index 9cba0c9..911328e 100644 --- a/api.html +++ b/api.html @@ -28,19 +28,19 @@ - - - - - + + + + + - - - + + + @@ -327,11 +327,11 @@

-

API#

+

API#

This page lists all the public functions and classes of the hazardous package:

-

Estimators#

+

Estimators#

@@ -341,7 +341,7 @@

Estimators -

Metrics#

+

Metrics#

GradientBoostingIncidence

@@ -360,7 +360,7 @@

Metrics -

Datasets#

+

Datasets#

metrics.brier_score_survival

@@ -370,7 +370,7 @@

Datasets -

Inverse Probability Censoring Weight#

+

Inverse Probability Censoring Weight#

data.make_synthetic_competing_weibull

@@ -475,7 +475,7 @@

Inverse Probability Censoring Weight

- Created using Sphinx 7.0.1. + Created using Sphinx 7.2.6.

diff --git a/auto_examples/index.html b/auto_examples/index.html index 8632112..c4be0dc 100644 --- a/auto_examples/index.html +++ b/auto_examples/index.html @@ -28,19 +28,19 @@ - - - - - + + + + + - - - + + + @@ -315,7 +315,7 @@

IPCWEstimator

- + @@ -377,7 +377,7 @@ diff --git a/generated/hazardous.GradientBoostingIncidence.html b/generated/hazardous.GradientBoostingIncidence.html index 3435b0c..8d2cffc 100644 --- a/generated/hazardous.GradientBoostingIncidence.html +++ b/generated/hazardous.GradientBoostingIncidence.html @@ -28,19 +28,19 @@ - - - - - + + + + + - - - + + + @@ -330,11 +330,11 @@
-

hazardous.GradientBoostingIncidence#

+

hazardous.GradientBoostingIncidence#

Usage examples at the bottom of this page.

-class hazardous.GradientBoostingIncidence(event_of_interest='any', loss='ibs', monotonic_incidence=False, hard_zero_fraction=0.1, n_iter=100, learning_rate=0.05, max_depth=None, max_leaf_nodes=31, min_samples_leaf=50, show_progressbar=True, n_time_grid_steps=100, time_horizon=None, random_state=None)#
+class hazardous.GradientBoostingIncidence(event_of_interest='any', loss='ibs', monotonic_incidence=False, hard_zero_fraction=0.1, n_iter=100, learning_rate=0.05, max_depth=None, max_leaf_nodes=31, min_samples_leaf=50, show_progressbar=True, n_time_grid_steps=100, time_horizon=None, random_state=None)#

Cause-specific Cumulative Incidence Function (CIF) with GBDT.

This internally relies on the histogram-based gradient boosting classifier or regressor implementation of scikit-learn.

@@ -344,7 +344,7 @@

Under the hood, this class uses randomly sampled reference time horizons concatenated as an extra input column to the underlying HGB binary classification model. At boosting iteration, a new tree is trained on a -copy of the original feature matrix X augmented with a new indepdent sample +copy of the original feature matrix X augmented with a new independent sample of time horizons.

One can obtain the survival probabilities for any event by summing all cause-specific CIF curves and computing 1 - “sum of CIF curves”.

@@ -406,7 +406,7 @@

-get_metadata_routing()#
+get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

@@ -423,7 +423,7 @@

-get_params(deep=True)#
+get_params(deep=True)#

Get parameters for this estimator.

Parameters:
@@ -444,7 +444,7 @@

-predict_proba(X, time_horizon=None)#
+predict_proba(X, time_horizon=None)#

Estimate the probability of incidence for a specific time horizon.

See the docstring for the time_horizon parameter for more details.

Returns a 2d array with shape (X.shape[0], 2). The second column holds @@ -460,7 +460,7 @@

-predict_quantile(X, quantile=0.5, times=None)#
+predict_quantile(X, quantile=0.5, times=None)#

Estimate the conditional median (or other quantile) time to event.

Note: this can return np.inf values when the estimated CIF does not reach the quantile value at the maximum time horizon observed on @@ -469,7 +469,7 @@

-predict_survival_function(X, times=None)#
+predict_survival_function(X, times=None)#

Compute the event specific survival function.

Warning: this metric only makes sense when y_train[“event”] is binary (single event) or when setting event_of_interest=’any’.

@@ -477,7 +477,7 @@

-score(X, y)#
+score(X, y)#

Return the negative time-integrated Brier score (IBS) or INLL.

This returns the negative of a proper scoring rule, so that the higher the value, the better the model to be consistent with the scoring @@ -509,7 +509,7 @@

-set_fit_request(*, times: bool | None | str = '$UNCHANGED$') GradientBoostingIncidence#
+set_fit_request(*, times: bool | None | str = '$UNCHANGED$') GradientBoostingIncidence#

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). @@ -552,7 +552,7 @@

-set_params(**params)#
+set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have @@ -576,7 +576,7 @@

-set_predict_proba_request(*, time_horizon: bool | None | str = '$UNCHANGED$') GradientBoostingIncidence#
+set_predict_proba_request(*, time_horizon: bool | None | str = '$UNCHANGED$') GradientBoostingIncidence#

Request metadata passed to the predict_proba method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). @@ -723,7 +723,7 @@

diff --git a/generated/hazardous.IPCWEstimator.html b/generated/hazardous.IPCWEstimator.html index 903c0ef..96d6cd8 100644 --- a/generated/hazardous.IPCWEstimator.html +++ b/generated/hazardous.IPCWEstimator.html @@ -28,19 +28,19 @@ - - - - - + + + + + - - - + + + @@ -329,11 +329,11 @@
-

hazardous.IPCWEstimator#

+

hazardous.IPCWEstimator#

Usage examples at the bottom of this page.

-class hazardous.IPCWEstimator#
+class hazardous.IPCWEstimator#

Estimate the Inverse Probability of Censoring Weight (IPCW).

This class estimates the inverse of the probability of “survival” to censoring using the Kaplan-Meier estimator on a binary indicator for @@ -353,7 +353,7 @@

IPCW value beyond the last observed time.

-compute_ipcw_at(times)#
+compute_ipcw_at(times)#

Estimate inverse censoring weight probability at times.

Linearly interpolate the censoring survival function and return the inverse values.

@@ -375,7 +375,7 @@

-fit(y)#
+fit(y)#

Compute the censoring survival function using Kaplan Meier and store it as an interpolation function.

@@ -396,7 +396,7 @@

-get_metadata_routing()#
+get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

@@ -413,7 +413,7 @@

-get_params(deep=True)#
+get_params(deep=True)#

Get parameters for this estimator.

Parameters:
@@ -434,7 +434,7 @@

-set_params(**params)#
+set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have @@ -549,7 +549,7 @@

diff --git a/generated/hazardous.data.make_synthetic_competing_weibull.html b/generated/hazardous.data.make_synthetic_competing_weibull.html index 34d28c6..5dace1b 100644 --- a/generated/hazardous.data.make_synthetic_competing_weibull.html +++ b/generated/hazardous.data.make_synthetic_competing_weibull.html @@ -28,19 +28,19 @@ - - - - - + + + + + - - - + + + @@ -330,10 +330,10 @@
-

hazardous.data.make_synthetic_competing_weibull#

+

hazardous.data.make_synthetic_competing_weibull#

-hazardous.data.make_synthetic_competing_weibull(n_events=3, n_samples=3000, return_X_y=False, base_scale=1000, feature_rounding=2, target_rounding=1, shape_ranges=((0.4, 0.9), (1.0, 1.0), (1.2, 3)), scale_ranges=((1, 20), (1, 10), (1.5, 5)), censoring_relative_scale=1.5, random_state=None)#
+hazardous.data.make_synthetic_competing_weibull(n_events=3, n_samples=3000, return_X_y=False, base_scale=1000, feature_rounding=2, target_rounding=1, shape_ranges=((0.4, 0.9), (1.0, 1.0), (1.2, 3)), scale_ranges=((1, 20), (1, 10), (1.5, 5)), censoring_relative_scale=1.5, random_state=None)#

Generate a synthetic dataset with competing Weibull-distributed events.

For each individual, we first sample one pair of shape and scale parameters for each event type uniformly from the given ranges. Each event type has a @@ -446,7 +446,7 @@

diff --git a/generated/hazardous.metrics.brier_score_incidence.html b/generated/hazardous.metrics.brier_score_incidence.html index 38f0abf..3c49400 100644 --- a/generated/hazardous.metrics.brier_score_incidence.html +++ b/generated/hazardous.metrics.brier_score_incidence.html @@ -28,19 +28,19 @@ - - - - - + + + + + - - - + + + @@ -331,10 +331,10 @@
-

hazardous.metrics.brier_score_incidence#

+

hazardous.metrics.brier_score_incidence#

-hazardous.metrics.brier_score_incidence(y_train, y_test, y_pred, times, event_of_interest='any')#
+hazardous.metrics.brier_score_incidence(y_train, y_test, y_pred, times, event_of_interest='any')#

Time-dependent Brier score for the kth cause of event.

\[\mathrm{BS}_k(t) = \frac{1}{n} \sum_{i=1}^n \hat{\omega}_i(t) @@ -485,7 +485,7 @@

diff --git a/generated/hazardous.metrics.brier_score_survival.html b/generated/hazardous.metrics.brier_score_survival.html index 22bf601..f977ede 100644 --- a/generated/hazardous.metrics.brier_score_survival.html +++ b/generated/hazardous.metrics.brier_score_survival.html @@ -28,19 +28,19 @@ - - - - - + + + + + - - - + + + @@ -331,10 +331,10 @@
-

hazardous.metrics.brier_score_survival#

+

hazardous.metrics.brier_score_survival#

-hazardous.metrics.brier_score_survival(y_train, y_test, y_pred, times)#
+hazardous.metrics.brier_score_survival(y_train, y_test, y_pred, times)#

Time-dependent Brier score of a survival function estimate.

\[\mathrm{BS}(t) = \frac{1}{n} \sum_{i=1}^n \mathbb{I} @@ -476,7 +476,7 @@

diff --git a/generated/hazardous.metrics.integrated_brier_score_incidence.html b/generated/hazardous.metrics.integrated_brier_score_incidence.html index ce784a1..a12457b 100644 --- a/generated/hazardous.metrics.integrated_brier_score_incidence.html +++ b/generated/hazardous.metrics.integrated_brier_score_incidence.html @@ -28,19 +28,19 @@ - - - - - + + + + + - - - + + + @@ -331,10 +331,10 @@
-

hazardous.metrics.integrated_brier_score_incidence#

+

hazardous.metrics.integrated_brier_score_incidence#

-hazardous.metrics.integrated_brier_score_incidence(y_train, y_test, y_pred, times, event_of_interest='any')#
+hazardous.metrics.integrated_brier_score_incidence(y_train, y_test, y_pred, times, event_of_interest='any')#

Time-integrated Brier score of a cause-specific cumulative incidence estimate.

\[\mathrm{IBS}_k = \frac{1}{t_{max} - t_{min}} \int^{t_{max}}_{t_{min}} @@ -477,7 +477,7 @@

diff --git a/generated/hazardous.metrics.integrated_brier_score_survival.html b/generated/hazardous.metrics.integrated_brier_score_survival.html index 19aa000..2bf01b7 100644 --- a/generated/hazardous.metrics.integrated_brier_score_survival.html +++ b/generated/hazardous.metrics.integrated_brier_score_survival.html @@ -28,19 +28,19 @@ - - - - - + + + + + - - - + + + @@ -331,10 +331,10 @@
-

hazardous.metrics.integrated_brier_score_survival#

+

hazardous.metrics.integrated_brier_score_survival#

-hazardous.metrics.integrated_brier_score_survival(y_train, y_test, y_pred, times)#
+hazardous.metrics.integrated_brier_score_survival(y_train, y_test, y_pred, times)#

Compute the Brier score integrated over the observed time range.

\[\mathrm{IBS} = \frac{1}{t_{max} - t_{min}} \int^{t_{max}}_{t_{min}} @@ -468,7 +468,7 @@

diff --git a/genindex.html b/genindex.html index 42bd20f..8a2f7c0 100644 --- a/genindex.html +++ b/genindex.html @@ -27,19 +27,19 @@ - - - - - + + + + + - - - + + + @@ -442,7 +442,7 @@

S

diff --git a/index.html b/index.html index 8ebca2a..ae8e989 100644 --- a/index.html +++ b/index.html @@ -28,19 +28,19 @@ - - - - - + + + + + - - - + + + @@ -277,7 +277,7 @@
-

HΛZΛRDOUS: predictive survival and competing risks analysis in Python#

+

HΛZΛRDOUS: predictive survival and competing risks analysis in Python#

    @@ -364,7 +364,7 @@

    HΛZΛRDOUS: predictive survival and competing risks analysis in Python

    - Created using Sphinx 7.0.1. + Created using Sphinx 7.2.6.

diff --git a/search.html b/search.html index 7f3fad9..374c668 100644 --- a/search.html +++ b/search.html @@ -26,19 +26,19 @@ - - - - - + + + + + - - - + + + @@ -352,7 +352,7 @@

Search

diff --git a/searchindex.js b/searchindex.js index 61a6d98..c5c1ca2 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["api", "auto_examples/index", "auto_examples/plot_marginal_cumulative_incidence_estimation", "auto_examples/sg_execution_times", "generated/hazardous.GradientBoostingIncidence", "generated/hazardous.IPCWEstimator", "generated/hazardous.data.make_synthetic_competing_weibull", "generated/hazardous.metrics.brier_score_incidence", "generated/hazardous.metrics.brier_score_survival", "generated/hazardous.metrics.integrated_brier_score_incidence", "generated/hazardous.metrics.integrated_brier_score_survival", "index"], "filenames": ["api.rst", "auto_examples/index.rst", "auto_examples/plot_marginal_cumulative_incidence_estimation.rst", "auto_examples/sg_execution_times.rst", "generated/hazardous.GradientBoostingIncidence.rst", "generated/hazardous.IPCWEstimator.rst", "generated/hazardous.data.make_synthetic_competing_weibull.rst", "generated/hazardous.metrics.brier_score_incidence.rst", "generated/hazardous.metrics.brier_score_survival.rst", "generated/hazardous.metrics.integrated_brier_score_incidence.rst", "generated/hazardous.metrics.integrated_brier_score_survival.rst", "index.rst"], "titles": ["API", "Example gallery", "Estimating marginal cumulative incidence functions", "Computation times", "hazardous.GradientBoostingIncidence", "hazardous.IPCWEstimator", "hazardous.data.make_synthetic_competing_weibull", "hazardous.metrics.brier_score_incidence", "hazardous.metrics.brier_score_survival", "hazardous.metrics.integrated_brier_score_incidence", "hazardous.metrics.integrated_brier_score_survival", "H\u039bZ\u039bRDOUS: predictive survival and competing risks analysis in Python"], "terms": {"thi": [0, 2, 4, 5, 7, 8, 9, 10], "page": [0, 4, 5], "list": 0, "all": [0, 1, 2, 4, 7, 9], "public": 0, "function": [0, 1, 3, 4, 5, 8, 10], "class": [0, 4, 5], "hazard": [0, 1, 2, 11], "packag": 0, "below": 1, "i": [1, 2, 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Estimating marginal cumulative incidence functions (plot_marginal_cumulative_incidence_estimation.py)

00:30.220

00:26.655

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