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chainladder (python)

Python versionsPyPI versionDownloadsConda-forge versionConda-forge downloadsLicenseBuild Statuscodecov ioDocumentation Status

chainladder: Property and Casualty Loss Reserving in Python

Welcome! The chainladder package was built to be able to handle all of your actuarial needs in python. It consists of popular actuarial tools, such as triangle data manipulation, link ratios calculation, and IBNR estimates with both deterministic and stochastic models. We build this package so you no longer have to rely on outdated softwares and tools when performing actuarial pricing or reserving indications.

This package strives to be minimalistic in needing its own API. The syntax mimics popular packages pandas for data manipulation and scikit-learn for model construction. An actuary that is already familiar with these tools will be able to pick up this package with ease. You will be able to save your mental energy for actual actuarial work.

Chainladder is built by a group of volunteers, and we need *YOUR* help!

This package is written in Python, if you are looking for a similar package written in R, please visit chainladder.

Documentation Site

We have a documentation website, where you can find installation instructions, tutorials, example galleries, sample datasets, API references, change log history, and more.

Visit Chainladder-Python on Read the Docs.

Contributors Working Group

We also have a Contributors Working Group that meets for one hour approximately every two weeks. We are part of the CAS volunteer groups and are supported by the CAS. If you are interested, you are welcome to join us through the Open-Source Projects Working Group.

We currently meet on Fridays 11:00am - Noon ET, but we periodically adjust meeting times to accommodate contributors across time zones around the globe. Our meetings are relaxed and collaborative, with discussions around milestones, package design ideas, open issues, and other behind-the-scenes work on GitHub.

We welcome contributors of all skill levels, including CAS members, its affiliate members, industry researchers, educators, students, and CAS candidates. To join us, you may respond to the CAS annual Volunteer Interest and Participation (VIP) Survey, reach out to a CAS staff persons (Heather Davis <hdavis@casact.org>, Elizabeth Smith <esmith@casact.org>), or the working group chair (Kenneth Hsu <kennethshsu@gmail.com>), and we will be happy to welcome you into the group.

Licenses

This package is released under Mozilla Public License 2.0.

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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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chainladder (python)
Python versionsPyPI versionDownloadsConda-forge versionConda-forge downloadsLicenseBuild Statuscodecov ioDocumentation Status
chainladder: Property and Casualty Loss Reserving in Python
Welcome! The chainladder package was built to be able to handle all of your actuarial needs in python. It consists of popular actuarial tools, such as triangle data manipulation, link ratios calculation, and IBNR estimates with both deterministic and stochastic models. We build this package so you no longer have to rely on outdated softwares and tools when performing actuarial pricing or reserving indications.
This package strives to be minimalistic in needing its own API. The syntax mimics popular packages pandas for data manipulation and scikit-learn for model
construction. An actuary that is already familiar with these tools will be able to pick up this package with ease. You will be able to save your mental energy for actual actuarial work.
Chainladder is built by a group of volunteers, and we need *YOUR* help!
This package is written in Python, if you are looking for a similar package written in R, please visit chainladder.
Documentation Site
We have a documentation website, where you can find installation instructions, tutorials, example galleries, sample datasets, API references, change log history, and more.
Visit Chainladder-Python on Read the Docs.
Contributors Working Group
We also have a Contributors Working Group that meets for one hour approximately every two weeks. We are part of the CAS volunteer groups and are supported by the CAS. If you are interested, you are welcome to join us through the Open-Source Projects Working Group.
We currently meet on Fridays 11:00am - Noon ET, but we periodically adjust meeting times to accommodate contributors across time zones around the globe. Our meetings are relaxed and collaborative, with discussions around milestones, package design ideas, open issues, and other behind-the-scenes work on GitHub.
We welcome contributors of all skill levels, including CAS members, its affiliate members, industry researchers, educators, students, and CAS candidates. To join us, you may respond to the CAS annual Volunteer Interest and Participation (VIP) Survey, reach out to a CAS staff persons (Heather Davis <hdavis@casact.org>, Elizabeth Smith <esmith@casact.org>), or the working group chair (Kenneth Hsu <kennethshsu@gmail.com>), and we will be happy to welcome you into the group.
Licenses
This package is released under Mozilla Public License 2.0.
About
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hdlclp.readthedocs.io/en/latest/intro.html
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Readme
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
(function(){
try {
var __m = "*";
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chainladder (python)
Python versionsPyPI versionDownloadsConda-forge versionConda-forge downloadsLicenseBuild Statuscodecov ioDocumentation Status
chainladder: Property and Casualty Loss Reserving in Python
Welcome! The chainladder package was built to be able to handle all of your actuarial needs in python. It consists of popular actuarial tools, such as triangle data manipulation, link ratios calculation, and IBNR estimates with both deterministic and stochastic models. We build this package so you no longer have to rely on outdated softwares and tools when performing actuarial pricing or reserving indications.
This package strives to be minimalistic in needing its own API. The syntax mimics popular packages pandas for data manipulation and scikit-learn for model
construction. An actuary that is already familiar with these tools will be able to pick up this package with ease. You will be able to save your mental energy for actual actuarial work.
Chainladder is built by a group of volunteers, and we need *YOUR* help!
This package is written in Python, if you are looking for a similar package written in R, please visit chainladder.
Documentation Site
We have a documentation website, where you can find installation instructions, tutorials, example galleries, sample datasets, API references, change log history, and more.
Visit Chainladder-Python on Read the Docs.
Contributors Working Group
We also have a Contributors Working Group that meets for one hour approximately every two weeks. We are part of the CAS volunteer groups and are supported by the CAS. If you are interested, you are welcome to join us through the Open-Source Projects Working Group.
We currently meet on Fridays 11:00am - Noon ET, but we periodically adjust meeting times to accommodate contributors across time zones around the globe. Our meetings are relaxed and collaborative, with discussions around milestones, package design ideas, open issues, and other behind-the-scenes work on GitHub.
We welcome contributors of all skill levels, including CAS members, its affiliate members, industry researchers, educators, students, and CAS candidates. To join us, you may respond to the CAS annual Volunteer Interest and Participation (VIP) Survey, reach out to a CAS staff persons (Heather Davis <hdavis@casact.org>, Elizabeth Smith <esmith@casact.org>), or the working group chair (Kenneth Hsu <kennethshsu@gmail.com>), and we will be happy to welcome you into the group.
Licenses
This package is released under Mozilla Public License 2.0.
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, '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('^' + ".*" + '

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chainladder (python)
Python versionsPyPI versionDownloadsConda-forge versionConda-forge downloadsLicenseBuild Statuscodecov ioDocumentation Status
chainladder: Property and Casualty Loss Reserving in Python
Welcome! The chainladder package was built to be able to handle all of your actuarial needs in python. It consists of popular actuarial tools, such as triangle data manipulation, link ratios calculation, and IBNR estimates with both deterministic and stochastic models. We build this package so you no longer have to rely on outdated softwares and tools when performing actuarial pricing or reserving indications.
This package strives to be minimalistic in needing its own API. The syntax mimics popular packages pandas for data manipulation and scikit-learn for model
construction. An actuary that is already familiar with these tools will be able to pick up this package with ease. You will be able to save your mental energy for actual actuarial work.
Chainladder is built by a group of volunteers, and we need *YOUR* help!
This package is written in Python, if you are looking for a similar package written in R, please visit chainladder.
Documentation Site
We have a documentation website, where you can find installation instructions, tutorials, example galleries, sample datasets, API references, change log history, and more.
Visit Chainladder-Python on Read the Docs.
Contributors Working Group
We also have a Contributors Working Group that meets for one hour approximately every two weeks. We are part of the CAS volunteer groups and are supported by the CAS. If you are interested, you are welcome to join us through the Open-Source Projects Working Group.
We currently meet on Fridays 11:00am - Noon ET, but we periodically adjust meeting times to accommodate contributors across time zones around the globe. Our meetings are relaxed and collaborative, with discussions around milestones, package design ideas, open issues, and other behind-the-scenes work on GitHub.
We welcome contributors of all skill levels, including CAS members, its affiliate members, industry researchers, educators, students, and CAS candidates. To join us, you may respond to the CAS annual Volunteer Interest and Participation (VIP) Survey, reach out to a CAS staff persons (Heather Davis <hdavis@casact.org>, Elizabeth Smith <esmith@casact.org>), or the working group chair (Kenneth Hsu <kennethshsu@gmail.com>), and we will be happy to welcome you into the group.
Licenses
This package is released under Mozilla Public License 2.0.
About
Work on cl
hdlclp.readthedocs.io/en/latest/intro.html
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, '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" + '

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chainladder (python)
Python versionsPyPI versionDownloadsConda-forge versionConda-forge downloadsLicenseBuild Statuscodecov ioDocumentation Status
chainladder: Property and Casualty Loss Reserving in Python
Welcome! The chainladder package was built to be able to handle all of your actuarial needs in python. It consists of popular actuarial tools, such as triangle data manipulation, link ratios calculation, and IBNR estimates with both deterministic and stochastic models. We build this package so you no longer have to rely on outdated softwares and tools when performing actuarial pricing or reserving indications.
This package strives to be minimalistic in needing its own API. The syntax mimics popular packages pandas for data manipulation and scikit-learn for model
construction. An actuary that is already familiar with these tools will be able to pick up this package with ease. You will be able to save your mental energy for actual actuarial work.
Chainladder is built by a group of volunteers, and we need *YOUR* help!
This package is written in Python, if you are looking for a similar package written in R, please visit chainladder.
Documentation Site
We have a documentation website, where you can find installation instructions, tutorials, example galleries, sample datasets, API references, change log history, and more.
Visit Chainladder-Python on Read the Docs.
Contributors Working Group
We also have a Contributors Working Group that meets for one hour approximately every two weeks. We are part of the CAS volunteer groups and are supported by the CAS. If you are interested, you are welcome to join us through the Open-Source Projects Working Group.
We currently meet on Fridays 11:00am - Noon ET, but we periodically adjust meeting times to accommodate contributors across time zones around the globe. Our meetings are relaxed and collaborative, with discussions around milestones, package design ideas, open issues, and other behind-the-scenes work on GitHub.
We welcome contributors of all skill levels, including CAS members, its affiliate members, industry researchers, educators, students, and CAS candidates. To join us, you may respond to the CAS annual Volunteer Interest and Participation (VIP) Survey, reach out to a CAS staff persons (Heather Davis <hdavis@casact.org>, Elizabeth Smith <esmith@casact.org>), or the working group chair (Kenneth Hsu <kennethshsu@gmail.com>), and we will be happy to welcome you into the group.
Licenses
This package is released under Mozilla Public License 2.0.
About
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hdlclp.readthedocs.io/en/latest/intro.html
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, '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); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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chainladder (python)
Python versionsPyPI versionDownloadsConda-forge versionConda-forge downloadsLicenseBuild Statuscodecov ioDocumentation Status
chainladder: Property and Casualty Loss Reserving in Python
Welcome! The chainladder package was built to be able to handle all of your actuarial needs in python. It consists of popular actuarial tools, such as triangle data manipulation, link ratios calculation, and IBNR estimates with both deterministic and stochastic models. We build this package so you no longer have to rely on outdated softwares and tools when performing actuarial pricing or reserving indications.
This package strives to be minimalistic in needing its own API. The syntax mimics popular packages pandas for data manipulation and scikit-learn for model
construction. An actuary that is already familiar with these tools will be able to pick up this package with ease. You will be able to save your mental energy for actual actuarial work.
Chainladder is built by a group of volunteers, and we need *YOUR* help!
This package is written in Python, if you are looking for a similar package written in R, please visit chainladder.
Documentation Site
We have a documentation website, where you can find installation instructions, tutorials, example galleries, sample datasets, API references, change log history, and more.
Visit Chainladder-Python on Read the Docs.
Contributors Working Group
We also have a Contributors Working Group that meets for one hour approximately every two weeks. We are part of the CAS volunteer groups and are supported by the CAS. If you are interested, you are welcome to join us through the Open-Source Projects Working Group.
We currently meet on Fridays 11:00am - Noon ET, but we periodically adjust meeting times to accommodate contributors across time zones around the globe. Our meetings are relaxed and collaborative, with discussions around milestones, package design ideas, open issues, and other behind-the-scenes work on GitHub.
We welcome contributors of all skill levels, including CAS members, its affiliate members, industry researchers, educators, students, and CAS candidates. To join us, you may respond to the CAS annual Volunteer Interest and Participation (VIP) Survey, reach out to a CAS staff persons (Heather Davis <hdavis@casact.org>, Elizabeth Smith <esmith@casact.org>), or the working group chair (Kenneth Hsu <kennethshsu@gmail.com>), and we will be happy to welcome you into the group.
Licenses
This package is released under Mozilla Public License 2.0.
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hdlclp.readthedocs.io/en/latest/intro.html
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, '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); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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chainladder (python)
Python versionsPyPI versionDownloadsConda-forge versionConda-forge downloadsLicenseBuild Statuscodecov ioDocumentation Status
chainladder: Property and Casualty Loss Reserving in Python
Welcome! The chainladder package was built to be able to handle all of your actuarial needs in python. It consists of popular actuarial tools, such as triangle data manipulation, link ratios calculation, and IBNR estimates with both deterministic and stochastic models. We build this package so you no longer have to rely on outdated softwares and tools when performing actuarial pricing or reserving indications.
This package strives to be minimalistic in needing its own API. The syntax mimics popular packages pandas for data manipulation and scikit-learn for model
construction. An actuary that is already familiar with these tools will be able to pick up this package with ease. You will be able to save your mental energy for actual actuarial work.
Chainladder is built by a group of volunteers, and we need *YOUR* help!
This package is written in Python, if you are looking for a similar package written in R, please visit chainladder.
Documentation Site
We have a documentation website, where you can find installation instructions, tutorials, example galleries, sample datasets, API references, change log history, and more.
Visit Chainladder-Python on Read the Docs.
Contributors Working Group
We also have a Contributors Working Group that meets for one hour approximately every two weeks. We are part of the CAS volunteer groups and are supported by the CAS. If you are interested, you are welcome to join us through the Open-Source Projects Working Group.
We currently meet on Fridays 11:00am - Noon ET, but we periodically adjust meeting times to accommodate contributors across time zones around the globe. Our meetings are relaxed and collaborative, with discussions around milestones, package design ideas, open issues, and other behind-the-scenes work on GitHub.
We welcome contributors of all skill levels, including CAS members, its affiliate members, industry researchers, educators, students, and CAS candidates. To join us, you may respond to the CAS annual Volunteer Interest and Participation (VIP) Survey, reach out to a CAS staff persons (Heather Davis <hdavis@casact.org>, Elizabeth Smith <esmith@casact.org>), or the working group chair (Kenneth Hsu <kennethshsu@gmail.com>), and we will be happy to welcome you into the group.
Licenses
This package is released under Mozilla Public License 2.0.
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hdlclp.readthedocs.io/en/latest/intro.html
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Readme
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, '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); }
})();
})();

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chainladder (python)
Python versionsPyPI versionDownloadsConda-forge versionConda-forge downloadsLicenseBuild Statuscodecov ioDocumentation Status
chainladder: Property and Casualty Loss Reserving in Python
Welcome! The chainladder package was built to be able to handle all of your actuarial needs in python. It consists of popular actuarial tools, such as triangle data manipulation, link ratios calculation, and IBNR estimates with both deterministic and stochastic models. We build this package so you no longer have to rely on outdated softwares and tools when performing actuarial pricing or reserving indications.
This package strives to be minimalistic in needing its own API. The syntax mimics popular packages pandas for data manipulation and scikit-learn for model
construction. An actuary that is already familiar with these tools will be able to pick up this package with ease. You will be able to save your mental energy for actual actuarial work.
Chainladder is built by a group of volunteers, and we need *YOUR* help!
This package is written in Python, if you are looking for a similar package written in R, please visit chainladder.
Documentation Site
We have a documentation website, where you can find installation instructions, tutorials, example galleries, sample datasets, API references, change log history, and more.
Visit Chainladder-Python on Read the Docs.
Contributors Working Group
We also have a Contributors Working Group that meets for one hour approximately every two weeks. We are part of the CAS volunteer groups and are supported by the CAS. If you are interested, you are welcome to join us through the Open-Source Projects Working Group.
We currently meet on Fridays 11:00am - Noon ET, but we periodically adjust meeting times to accommodate contributors across time zones around the globe. Our meetings are relaxed and collaborative, with discussions around milestones, package design ideas, open issues, and other behind-the-scenes work on GitHub.
We welcome contributors of all skill levels, including CAS members, its affiliate members, industry researchers, educators, students, and CAS candidates. To join us, you may respond to the CAS annual Volunteer Interest and Participation (VIP) Survey, reach out to a CAS staff persons (Heather Davis <hdavis@casact.org>, Elizabeth Smith <esmith@casact.org>), or the working group chair (Kenneth Hsu <kennethshsu@gmail.com>), and we will be happy to welcome you into the group.
Licenses
This package is released under Mozilla Public License 2.0.
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