BranchesTags

Latest commit

History

55 Commits

Folders and files

NameName
Last commit message
Last commit date
ADLP-package
ADLP-package
paper_results_replication
paper_results_replication
.gitignore
.gitignore
README.md
README.md

Repository files navigation

ADLP

Repository for ADLP - ensemble reserving package

Introduction

We present ADLP (Accident and Development period adjusted Linear Pools), a tailored ensemble technique for general insurance loss reserving. ADLP seeks to combine various loss reserving models, leveraging their strengths, with combination weights optimised to enhance the ensemble's distributional forecasting performance.

This package originates from the paper "Ensemble distributional forecasting for insurance loss reserving," while also offering users ample flexibility to choose or create component models for the ensemble, and to employ data partitioning for calibrating either the component models or the combination weights, aligning with their experiences.

Reference

For a full description of ADLP's structure and modelling details, readers should refer to:

Avanzi, B., Li, Y., Wong, B., & Xian, A. (2022). Ensemble distributional forecasting for insurance loss reserving. arXiv preprint arXiv:2206.08541.

To cite this package in publications, please use:

citation("ADLP")

Install Package

To install the development version of the package from this GitHub repository, do

if (!require(remotes)) install.packages("remotes")
remotes::install_github("agi-lab/ADLP/ADLP-package", build_vignettes = TRUE)

After the installation, run:

library(ADLP)

as you would normally do will load the package. View a full demonstration of the package by running

vignette("ADLP-demo", package = "ADLP")

About

ADLP: Accident and Development period adjusted Linear Pools for Actuarial Stochastic Reserving

Resources

Readme
Activity
Custom properties

Stars

1 star

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
Latest commit
History
55 Commits
Folders and files
NameName
Last commit message
Last commit date
ADLP-package
ADLP-package
paper_results_replication
paper_results_replication
.gitignore
.gitignore
README.md
README.md
Repository files navigation
ADLP
Repository for ADLP - ensemble reserving package
Introduction
We present ADLP (Accident and Development period adjusted Linear Pools), a tailored ensemble technique for general insurance loss reserving. ADLP seeks to combine various loss reserving models, leveraging their strengths, with combination weights optimised to enhance the ensemble's distributional forecasting performance.
This package originates from the paper "Ensemble distributional forecasting for insurance loss reserving," while also offering users ample flexibility to choose or create component models for the ensemble, and to employ data partitioning for calibrating either the component models or the combination weights, aligning with their experiences.
Reference
For a full description of ADLP's structure and modelling details, readers should refer to:
Avanzi, B., Li, Y., Wong, B., & Xian, A. (2022). Ensemble distributional forecasting for insurance loss reserving. arXiv preprint arXiv:2206.08541.
To cite this package in publications, please use:
citation("ADLP")
Install Package
To install the development version of the package from this GitHub repository, do
if (!require(remotes)) install.packages("remotes")
remotes::install_github("agi-lab/ADLP/ADLP-package", build_vignettes = TRUE)

After the installation, run:
library(ADLP)
as you would normally do will load the package. View a full demonstration of the package by running
vignette("ADLP-demo", package = "ADLP")
About
ADLP: Accident and Development period adjusted Linear Pools for Actuarial Stochastic Reserving
Resources
Readme
Activity
Custom properties
Stars
1 star
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
Latest commit
History
55 Commits
Folders and files
NameName
Last commit message
Last commit date
ADLP-package
ADLP-package
paper_results_replication
paper_results_replication
.gitignore
.gitignore
README.md
README.md
Repository files navigation
ADLP
Repository for ADLP - ensemble reserving package
Introduction
We present ADLP (Accident and Development period adjusted Linear Pools), a tailored ensemble technique for general insurance loss reserving. ADLP seeks to combine various loss reserving models, leveraging their strengths, with combination weights optimised to enhance the ensemble's distributional forecasting performance.
This package originates from the paper "Ensemble distributional forecasting for insurance loss reserving," while also offering users ample flexibility to choose or create component models for the ensemble, and to employ data partitioning for calibrating either the component models or the combination weights, aligning with their experiences.
Reference
For a full description of ADLP's structure and modelling details, readers should refer to:
Avanzi, B., Li, Y., Wong, B., & Xian, A. (2022). Ensemble distributional forecasting for insurance loss reserving. arXiv preprint arXiv:2206.08541.
To cite this package in publications, please use:
citation("ADLP")
Install Package
To install the development version of the package from this GitHub repository, do
if (!require(remotes)) install.packages("remotes")
remotes::install_github("agi-lab/ADLP/ADLP-package", build_vignettes = TRUE)

After the installation, run:
library(ADLP)
as you would normally do will load the package. View a full demonstration of the package by running
vignette("ADLP-demo", package = "ADLP")
About
ADLP: Accident and Development period adjusted Linear Pools for Actuarial Stochastic Reserving
Resources
Readme
Activity
Custom properties
Stars
1 star
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
Latest commit
History
55 Commits
Folders and files
NameName
Last commit message
Last commit date
ADLP-package
ADLP-package
paper_results_replication
paper_results_replication
.gitignore
.gitignore
README.md
README.md
Repository files navigation
ADLP
Repository for ADLP - ensemble reserving package
Introduction
We present ADLP (Accident and Development period adjusted Linear Pools), a tailored ensemble technique for general insurance loss reserving. ADLP seeks to combine various loss reserving models, leveraging their strengths, with combination weights optimised to enhance the ensemble's distributional forecasting performance.
This package originates from the paper "Ensemble distributional forecasting for insurance loss reserving," while also offering users ample flexibility to choose or create component models for the ensemble, and to employ data partitioning for calibrating either the component models or the combination weights, aligning with their experiences.
Reference
For a full description of ADLP's structure and modelling details, readers should refer to:
Avanzi, B., Li, Y., Wong, B., & Xian, A. (2022). Ensemble distributional forecasting for insurance loss reserving. arXiv preprint arXiv:2206.08541.
To cite this package in publications, please use:
citation("ADLP")
Install Package
To install the development version of the package from this GitHub repository, do
if (!require(remotes)) install.packages("remotes")
remotes::install_github("agi-lab/ADLP/ADLP-package", build_vignettes = TRUE)

After the installation, run:
library(ADLP)
as you would normally do will load the package. View a full demonstration of the package by running
vignette("ADLP-demo", package = "ADLP")
About
ADLP: Accident and Development period adjusted Linear Pools for Actuarial Stochastic Reserving
Resources
Readme
Activity
Custom properties
Stars
1 star
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
Latest commit
History
55 Commits
Folders and files
NameName
Last commit message
Last commit date
ADLP-package
ADLP-package
paper_results_replication
paper_results_replication
.gitignore
.gitignore
README.md
README.md
Repository files navigation
ADLP
Repository for ADLP - ensemble reserving package
Introduction
We present ADLP (Accident and Development period adjusted Linear Pools), a tailored ensemble technique for general insurance loss reserving. ADLP seeks to combine various loss reserving models, leveraging their strengths, with combination weights optimised to enhance the ensemble's distributional forecasting performance.
This package originates from the paper "Ensemble distributional forecasting for insurance loss reserving," while also offering users ample flexibility to choose or create component models for the ensemble, and to employ data partitioning for calibrating either the component models or the combination weights, aligning with their experiences.
Reference
For a full description of ADLP's structure and modelling details, readers should refer to:
Avanzi, B., Li, Y., Wong, B., & Xian, A. (2022). Ensemble distributional forecasting for insurance loss reserving. arXiv preprint arXiv:2206.08541.
To cite this package in publications, please use:
citation("ADLP")
Install Package
To install the development version of the package from this GitHub repository, do
if (!require(remotes)) install.packages("remotes")
remotes::install_github("agi-lab/ADLP/ADLP-package", build_vignettes = TRUE)

After the installation, run:
library(ADLP)
as you would normally do will load the package. View a full demonstration of the package by running
vignette("ADLP-demo", package = "ADLP")
About
ADLP: Accident and Development period adjusted Linear Pools for Actuarial Stochastic Reserving
Resources
Readme
Activity
Custom properties
Stars
1 star
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
Latest commit
History
55 Commits
Folders and files
NameName
Last commit message
Last commit date
ADLP-package
ADLP-package
paper_results_replication
paper_results_replication
.gitignore
.gitignore
README.md
README.md
Repository files navigation
ADLP
Repository for ADLP - ensemble reserving package
Introduction
We present ADLP (Accident and Development period adjusted Linear Pools), a tailored ensemble technique for general insurance loss reserving. ADLP seeks to combine various loss reserving models, leveraging their strengths, with combination weights optimised to enhance the ensemble's distributional forecasting performance.
This package originates from the paper "Ensemble distributional forecasting for insurance loss reserving," while also offering users ample flexibility to choose or create component models for the ensemble, and to employ data partitioning for calibrating either the component models or the combination weights, aligning with their experiences.
Reference
For a full description of ADLP's structure and modelling details, readers should refer to:
Avanzi, B., Li, Y., Wong, B., & Xian, A. (2022). Ensemble distributional forecasting for insurance loss reserving. arXiv preprint arXiv:2206.08541.
To cite this package in publications, please use:
citation("ADLP")
Install Package
To install the development version of the package from this GitHub repository, do
if (!require(remotes)) install.packages("remotes")
remotes::install_github("agi-lab/ADLP/ADLP-package", build_vignettes = TRUE)

After the installation, run:
library(ADLP)
as you would normally do will load the package. View a full demonstration of the package by running
vignette("ADLP-demo", package = "ADLP")
About
ADLP: Accident and Development period adjusted Linear Pools for Actuarial Stochastic Reserving
Resources
Readme
Activity
Custom properties
Stars
1 star
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
Latest commit
History
55 Commits
Folders and files
NameName
Last commit message
Last commit date
ADLP-package
ADLP-package
paper_results_replication
paper_results_replication
.gitignore
.gitignore
README.md
README.md
Repository files navigation
ADLP
Repository for ADLP - ensemble reserving package
Introduction
We present ADLP (Accident and Development period adjusted Linear Pools), a tailored ensemble technique for general insurance loss reserving. ADLP seeks to combine various loss reserving models, leveraging their strengths, with combination weights optimised to enhance the ensemble's distributional forecasting performance.
This package originates from the paper "Ensemble distributional forecasting for insurance loss reserving," while also offering users ample flexibility to choose or create component models for the ensemble, and to employ data partitioning for calibrating either the component models or the combination weights, aligning with their experiences.
Reference
For a full description of ADLP's structure and modelling details, readers should refer to:
Avanzi, B., Li, Y., Wong, B., & Xian, A. (2022). Ensemble distributional forecasting for insurance loss reserving. arXiv preprint arXiv:2206.08541.
To cite this package in publications, please use:
citation("ADLP")
Install Package
To install the development version of the package from this GitHub repository, do
if (!require(remotes)) install.packages("remotes")
remotes::install_github("agi-lab/ADLP/ADLP-package", build_vignettes = TRUE)

After the installation, run:
library(ADLP)
as you would normally do will load the package. View a full demonstration of the package by running
vignette("ADLP-demo", package = "ADLP")
About
ADLP: Accident and Development period adjusted Linear Pools for Actuarial Stochastic Reserving
Resources
Readme
Activity
Custom properties
Stars
1 star
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
Latest commit
History
55 Commits
Folders and files
NameName
Last commit message
Last commit date
ADLP-package
ADLP-package
paper_results_replication
paper_results_replication
.gitignore
.gitignore
README.md
README.md
Repository files navigation
ADLP
Repository for ADLP - ensemble reserving package
Introduction
We present ADLP (Accident and Development period adjusted Linear Pools), a tailored ensemble technique for general insurance loss reserving. ADLP seeks to combine various loss reserving models, leveraging their strengths, with combination weights optimised to enhance the ensemble's distributional forecasting performance.
This package originates from the paper "Ensemble distributional forecasting for insurance loss reserving," while also offering users ample flexibility to choose or create component models for the ensemble, and to employ data partitioning for calibrating either the component models or the combination weights, aligning with their experiences.
Reference
For a full description of ADLP's structure and modelling details, readers should refer to:
Avanzi, B., Li, Y., Wong, B., & Xian, A. (2022). Ensemble distributional forecasting for insurance loss reserving. arXiv preprint arXiv:2206.08541.
To cite this package in publications, please use:
citation("ADLP")
Install Package
To install the development version of the package from this GitHub repository, do
if (!require(remotes)) install.packages("remotes")
remotes::install_github("agi-lab/ADLP/ADLP-package", build_vignettes = TRUE)

After the installation, run:
library(ADLP)
as you would normally do will load the package. View a full demonstration of the package by running
vignette("ADLP-demo", package = "ADLP")
About
ADLP: Accident and Development period adjusted Linear Pools for Actuarial Stochastic Reserving
Resources
Readme
Activity
Custom properties
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages