BranchesTags

Repository files navigation

R-hubDOI

ReSurv

ReSurv is an R software for predicting IBNR claims. The software includes tools for synthetic data generation, data pre-processing, hyperparameters tuning, model estimation and prediction.

The package is based on the approach illustrated in Hiabu M., Hofman E., and Pittarello G. (2023) and estimates feature dependent development factors using individual reserving data.

Available Machine Learning (ML) models

There is a one-to-one relationship between development factors and hazard rates (Hiabu et al. (2023)). The package implements extends the following machine learning algorithms for proportional hazard models:

  • Cox model with splines (COX, Gray (1992)).

  • Neural Networks (NN, Katzman et al. (2018)).

  • eXtreme Gradient Boosting (XGB, Chen et al. (2016)).

ReSurv extends COX, NN, and XGB to account for ties in left-truncated and right-censored observations.

Installation

Developer Version

The developers version of the package can be installed from GitHub.

devtools::install_github('https://github.com/edhofman/ReSurv')

Python Dependencies

For using the NN models we suggest to install a virtual environment using

install_pyresurv()

The default name of the virtual environment is "pyresurv".

We then suggest to refresh the R session and to import the ReSurv package in R using

library(ReSurv)
reticulate::use_virtualenv("pyresurv")

Managing Multiple Package Dependencies

This section is taken from the guidelines of the R package reticulate for handling the case of multiple packages in your session that used isolated-package-environments. The most straightforward solution would be installing a dedicated environment for both.

envname <- "./venv"
ReSurv::install_pyresurv(envname = envname)
pysparklyr::install_pyspark(envname = envname)

References

  • Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).

  • Gray, R. J. (1992). Flexible methods for analyzing survival data using splines, with applications to breast cancer prognosis. Journal of the American Statistical Association, 87(420), 942-951.

  • Hiabu, M., Hofman, E., & Pittarello, G. (2023). A machine learning approach based on survival analysis for IBNR frequencies in non-life reserving. arXiv preprint arXiv:2312.14549.

  • Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems, 25.

  • Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology, 18, 1-12.

About

Repository of the R package ReSurv

Resources

Readme
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
R-hubDOI
ReSurv
ReSurv is an R software for predicting IBNR claims. The software includes tools for synthetic data generation, data pre-processing, hyperparameters tuning, model estimation and prediction.
The package is based on the approach illustrated in Hiabu M., Hofman E., and Pittarello G. (2023) and estimates feature dependent development factors using individual reserving data.
Available Machine Learning (ML) models
There is a one-to-one relationship between development factors and hazard rates (Hiabu et al. (2023)). The package implements extends the following machine learning algorithms for proportional hazard models:
  • Cox model with splines (COX, Gray (1992)).
  • Neural Networks (NN, Katzman et al. (2018)).
  • eXtreme Gradient Boosting (XGB, Chen et al. (2016)).
ReSurv extends COX, NN, and XGB to account for ties in left-truncated and right-censored observations.
Installation
Developer Version
The developers version of the package can be installed from GitHub.
devtools::install_github('https://github.com/edhofman/ReSurv')

Python Dependencies
For using the NN models we suggest to install a virtual environment using
install_pyresurv()

The default name of the virtual environment is "pyresurv".
We then suggest to refresh the R session and to import the ReSurv package in R using
library(ReSurv)
reticulate::use_virtualenv("pyresurv")

Managing Multiple Package Dependencies
This section is taken from the guidelines of the R package reticulate for handling the case of multiple packages in your session that used isolated-package-environments.
The most straightforward solution would be installing a dedicated environment for both.
envname <- "./venv"
ReSurv::install_pyresurv(envname = envname)
pysparklyr::install_pyspark(envname = envname)

References
  • Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
  • Gray, R. J. (1992). Flexible methods for analyzing survival data using splines, with applications to breast cancer prognosis. Journal of the American Statistical Association, 87(420), 942-951.
  • Hiabu, M., Hofman, E., & Pittarello, G. (2023). A machine learning approach based on survival analysis for IBNR frequencies in non-life reserving. arXiv preprint arXiv:2312.14549.
  • Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems, 25.
  • Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology, 18, 1-12.
About
Repository of the R package ReSurv
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
R-hubDOI
ReSurv
ReSurv is an R software for predicting IBNR claims. The software includes tools for synthetic data generation, data pre-processing, hyperparameters tuning, model estimation and prediction.
The package is based on the approach illustrated in Hiabu M., Hofman E., and Pittarello G. (2023) and estimates feature dependent development factors using individual reserving data.
Available Machine Learning (ML) models
There is a one-to-one relationship between development factors and hazard rates (Hiabu et al. (2023)). The package implements extends the following machine learning algorithms for proportional hazard models:
  • Cox model with splines (COX, Gray (1992)).
  • Neural Networks (NN, Katzman et al. (2018)).
  • eXtreme Gradient Boosting (XGB, Chen et al. (2016)).
ReSurv extends COX, NN, and XGB to account for ties in left-truncated and right-censored observations.
Installation
Developer Version
The developers version of the package can be installed from GitHub.
devtools::install_github('https://github.com/edhofman/ReSurv')

Python Dependencies
For using the NN models we suggest to install a virtual environment using
install_pyresurv()

The default name of the virtual environment is "pyresurv".
We then suggest to refresh the R session and to import the ReSurv package in R using
library(ReSurv)
reticulate::use_virtualenv("pyresurv")

Managing Multiple Package Dependencies
This section is taken from the guidelines of the R package reticulate for handling the case of multiple packages in your session that used isolated-package-environments.
The most straightforward solution would be installing a dedicated environment for both.
envname <- "./venv"
ReSurv::install_pyresurv(envname = envname)
pysparklyr::install_pyspark(envname = envname)

References
  • Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
  • Gray, R. J. (1992). Flexible methods for analyzing survival data using splines, with applications to breast cancer prognosis. Journal of the American Statistical Association, 87(420), 942-951.
  • Hiabu, M., Hofman, E., & Pittarello, G. (2023). A machine learning approach based on survival analysis for IBNR frequencies in non-life reserving. arXiv preprint arXiv:2312.14549.
  • Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems, 25.
  • Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology, 18, 1-12.
About
Repository of the R package ReSurv
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
R-hubDOI
ReSurv
ReSurv is an R software for predicting IBNR claims. The software includes tools for synthetic data generation, data pre-processing, hyperparameters tuning, model estimation and prediction.
The package is based on the approach illustrated in Hiabu M., Hofman E., and Pittarello G. (2023) and estimates feature dependent development factors using individual reserving data.
Available Machine Learning (ML) models
There is a one-to-one relationship between development factors and hazard rates (Hiabu et al. (2023)). The package implements extends the following machine learning algorithms for proportional hazard models:
  • Cox model with splines (COX, Gray (1992)).
  • Neural Networks (NN, Katzman et al. (2018)).
  • eXtreme Gradient Boosting (XGB, Chen et al. (2016)).
ReSurv extends COX, NN, and XGB to account for ties in left-truncated and right-censored observations.
Installation
Developer Version
The developers version of the package can be installed from GitHub.
devtools::install_github('https://github.com/edhofman/ReSurv')

Python Dependencies
For using the NN models we suggest to install a virtual environment using
install_pyresurv()

The default name of the virtual environment is "pyresurv".
We then suggest to refresh the R session and to import the ReSurv package in R using
library(ReSurv)
reticulate::use_virtualenv("pyresurv")

Managing Multiple Package Dependencies
This section is taken from the guidelines of the R package reticulate for handling the case of multiple packages in your session that used isolated-package-environments.
The most straightforward solution would be installing a dedicated environment for both.
envname <- "./venv"
ReSurv::install_pyresurv(envname = envname)
pysparklyr::install_pyspark(envname = envname)

References
  • Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
  • Gray, R. J. (1992). Flexible methods for analyzing survival data using splines, with applications to breast cancer prognosis. Journal of the American Statistical Association, 87(420), 942-951.
  • Hiabu, M., Hofman, E., & Pittarello, G. (2023). A machine learning approach based on survival analysis for IBNR frequencies in non-life reserving. arXiv preprint arXiv:2312.14549.
  • Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems, 25.
  • Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology, 18, 1-12.
About
Repository of the R package ReSurv
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
R-hubDOI
ReSurv
ReSurv is an R software for predicting IBNR claims. The software includes tools for synthetic data generation, data pre-processing, hyperparameters tuning, model estimation and prediction.
The package is based on the approach illustrated in Hiabu M., Hofman E., and Pittarello G. (2023) and estimates feature dependent development factors using individual reserving data.
Available Machine Learning (ML) models
There is a one-to-one relationship between development factors and hazard rates (Hiabu et al. (2023)). The package implements extends the following machine learning algorithms for proportional hazard models:
  • Cox model with splines (COX, Gray (1992)).
  • Neural Networks (NN, Katzman et al. (2018)).
  • eXtreme Gradient Boosting (XGB, Chen et al. (2016)).
ReSurv extends COX, NN, and XGB to account for ties in left-truncated and right-censored observations.
Installation
Developer Version
The developers version of the package can be installed from GitHub.
devtools::install_github('https://github.com/edhofman/ReSurv')

Python Dependencies
For using the NN models we suggest to install a virtual environment using
install_pyresurv()

The default name of the virtual environment is "pyresurv".
We then suggest to refresh the R session and to import the ReSurv package in R using
library(ReSurv)
reticulate::use_virtualenv("pyresurv")

Managing Multiple Package Dependencies
This section is taken from the guidelines of the R package reticulate for handling the case of multiple packages in your session that used isolated-package-environments.
The most straightforward solution would be installing a dedicated environment for both.
envname <- "./venv"
ReSurv::install_pyresurv(envname = envname)
pysparklyr::install_pyspark(envname = envname)

References
  • Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
  • Gray, R. J. (1992). Flexible methods for analyzing survival data using splines, with applications to breast cancer prognosis. Journal of the American Statistical Association, 87(420), 942-951.
  • Hiabu, M., Hofman, E., & Pittarello, G. (2023). A machine learning approach based on survival analysis for IBNR frequencies in non-life reserving. arXiv preprint arXiv:2312.14549.
  • Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems, 25.
  • Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology, 18, 1-12.
About
Repository of the R package ReSurv
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
R-hubDOI
ReSurv
ReSurv is an R software for predicting IBNR claims. The software includes tools for synthetic data generation, data pre-processing, hyperparameters tuning, model estimation and prediction.
The package is based on the approach illustrated in Hiabu M., Hofman E., and Pittarello G. (2023) and estimates feature dependent development factors using individual reserving data.
Available Machine Learning (ML) models
There is a one-to-one relationship between development factors and hazard rates (Hiabu et al. (2023)). The package implements extends the following machine learning algorithms for proportional hazard models:
  • Cox model with splines (COX, Gray (1992)).
  • Neural Networks (NN, Katzman et al. (2018)).
  • eXtreme Gradient Boosting (XGB, Chen et al. (2016)).
ReSurv extends COX, NN, and XGB to account for ties in left-truncated and right-censored observations.
Installation
Developer Version
The developers version of the package can be installed from GitHub.
devtools::install_github('https://github.com/edhofman/ReSurv')

Python Dependencies
For using the NN models we suggest to install a virtual environment using
install_pyresurv()

The default name of the virtual environment is "pyresurv".
We then suggest to refresh the R session and to import the ReSurv package in R using
library(ReSurv)
reticulate::use_virtualenv("pyresurv")

Managing Multiple Package Dependencies
This section is taken from the guidelines of the R package reticulate for handling the case of multiple packages in your session that used isolated-package-environments.
The most straightforward solution would be installing a dedicated environment for both.
envname <- "./venv"
ReSurv::install_pyresurv(envname = envname)
pysparklyr::install_pyspark(envname = envname)

References
  • Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
  • Gray, R. J. (1992). Flexible methods for analyzing survival data using splines, with applications to breast cancer prognosis. Journal of the American Statistical Association, 87(420), 942-951.
  • Hiabu, M., Hofman, E., & Pittarello, G. (2023). A machine learning approach based on survival analysis for IBNR frequencies in non-life reserving. arXiv preprint arXiv:2312.14549.
  • Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems, 25.
  • Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology, 18, 1-12.
About
Repository of the R package ReSurv
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
R-hubDOI
ReSurv
ReSurv is an R software for predicting IBNR claims. The software includes tools for synthetic data generation, data pre-processing, hyperparameters tuning, model estimation and prediction.
The package is based on the approach illustrated in Hiabu M., Hofman E., and Pittarello G. (2023) and estimates feature dependent development factors using individual reserving data.
Available Machine Learning (ML) models
There is a one-to-one relationship between development factors and hazard rates (Hiabu et al. (2023)). The package implements extends the following machine learning algorithms for proportional hazard models:
  • Cox model with splines (COX, Gray (1992)).
  • Neural Networks (NN, Katzman et al. (2018)).
  • eXtreme Gradient Boosting (XGB, Chen et al. (2016)).
ReSurv extends COX, NN, and XGB to account for ties in left-truncated and right-censored observations.
Installation
Developer Version
The developers version of the package can be installed from GitHub.
devtools::install_github('https://github.com/edhofman/ReSurv')

Python Dependencies
For using the NN models we suggest to install a virtual environment using
install_pyresurv()

The default name of the virtual environment is "pyresurv".
We then suggest to refresh the R session and to import the ReSurv package in R using
library(ReSurv)
reticulate::use_virtualenv("pyresurv")

Managing Multiple Package Dependencies
This section is taken from the guidelines of the R package reticulate for handling the case of multiple packages in your session that used isolated-package-environments.
The most straightforward solution would be installing a dedicated environment for both.
envname <- "./venv"
ReSurv::install_pyresurv(envname = envname)
pysparklyr::install_pyspark(envname = envname)

References
  • Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
  • Gray, R. J. (1992). Flexible methods for analyzing survival data using splines, with applications to breast cancer prognosis. Journal of the American Statistical Association, 87(420), 942-951.
  • Hiabu, M., Hofman, E., & Pittarello, G. (2023). A machine learning approach based on survival analysis for IBNR frequencies in non-life reserving. arXiv preprint arXiv:2312.14549.
  • Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems, 25.
  • Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology, 18, 1-12.
About
Repository of the R package ReSurv
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
R-hubDOI
ReSurv
ReSurv is an R software for predicting IBNR claims. The software includes tools for synthetic data generation, data pre-processing, hyperparameters tuning, model estimation and prediction.
The package is based on the approach illustrated in Hiabu M., Hofman E., and Pittarello G. (2023) and estimates feature dependent development factors using individual reserving data.
Available Machine Learning (ML) models
There is a one-to-one relationship between development factors and hazard rates (Hiabu et al. (2023)). The package implements extends the following machine learning algorithms for proportional hazard models:
  • Cox model with splines (COX, Gray (1992)).
  • Neural Networks (NN, Katzman et al. (2018)).
  • eXtreme Gradient Boosting (XGB, Chen et al. (2016)).
ReSurv extends COX, NN, and XGB to account for ties in left-truncated and right-censored observations.
Installation
Developer Version
The developers version of the package can be installed from GitHub.
devtools::install_github('https://github.com/edhofman/ReSurv')

Python Dependencies
For using the NN models we suggest to install a virtual environment using
install_pyresurv()

The default name of the virtual environment is "pyresurv".
We then suggest to refresh the R session and to import the ReSurv package in R using
library(ReSurv)
reticulate::use_virtualenv("pyresurv")

Managing Multiple Package Dependencies
This section is taken from the guidelines of the R package reticulate for handling the case of multiple packages in your session that used isolated-package-environments.
The most straightforward solution would be installing a dedicated environment for both.
envname <- "./venv"
ReSurv::install_pyresurv(envname = envname)
pysparklyr::install_pyspark(envname = envname)

References
  • Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
  • Gray, R. J. (1992). Flexible methods for analyzing survival data using splines, with applications to breast cancer prognosis. Journal of the American Statistical Association, 87(420), 942-951.
  • Hiabu, M., Hofman, E., & Pittarello, G. (2023). A machine learning approach based on survival analysis for IBNR frequencies in non-life reserving. arXiv preprint arXiv:2312.14549.
  • Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems, 25.
  • Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC medical research methodology, 18, 1-12.
About
Repository of the R package ReSurv
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages