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Larch: the logit architect

This is a tool for the estimation and application of logit-based discrete choice models. It is designed to integrate with NumPy and facilitate fast processing of linear models. If you want to estimate non-linear models, try Biogeme, which is more flexible in form and can be used for almost any model structure. If you don't know what the difference is, you probably want to start with linear models.

Larch is undergoing a transformation, with a new computational architecture that can significantly improve performance when working with large datasets. The new code relies on [numba](https://numba.pydata.org/), [xarray](https://xarray.pydata.org/en/stable/), and [sharrow](https://activitysim.github.io/sharrow) to enable super-fast estimation of choice models. Many (but not yet all) of the core features of Larch have been moved over to this new platform.

You can still use the old version of Larch as normal, but to try out the new version just import larch.numba instead of larch itself.

This project is very much under development. There are plenty of undocumented functions and features; use them at your own risk. Undocumented features may be non-functional, not rigorously tested, deprecated or removed without notice in a future version.

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Larch: a Python tool for choice modeling

larch.newman.me

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Larch: the logit architect
This is a tool for the estimation and application of logit-based discrete choice models.
It is designed to integrate with NumPy and facilitate fast processing of linear models.
If you want to estimate non-linear models, try Biogeme,
which is more flexible in form and can be used for almost any model structure.
If you don't know what the difference is, you probably want to start with linear models.
Larch is undergoing a transformation, with a new computational architecture
that can significantly improve performance when working with large datasets.
The new code relies on [numba](https://numba.pydata.org/),
[xarray](https://xarray.pydata.org/en/stable/), and
[sharrow](https://activitysim.github.io/sharrow) to enable super-fast estimation
of choice models. Many (but not yet all) of the core features of Larch have been moved
over to this new platform.
You can still use the old version of Larch as normal, but to try out the new version
just import larch.numba instead of larch itself.
This project is very much under development. There are plenty of undocumented functions
and features; use them at your own risk. Undocumented features may be non-functional,
not rigorously tested, deprecated or removed without notice in a future version.
About
Larch: a Python tool for choice modeling
larch.newman.me
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, '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");
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})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Larch: the logit architect
This is a tool for the estimation and application of logit-based discrete choice models.
It is designed to integrate with NumPy and facilitate fast processing of linear models.
If you want to estimate non-linear models, try Biogeme,
which is more flexible in form and can be used for almost any model structure.
If you don't know what the difference is, you probably want to start with linear models.
Larch is undergoing a transformation, with a new computational architecture
that can significantly improve performance when working with large datasets.
The new code relies on [numba](https://numba.pydata.org/),
[xarray](https://xarray.pydata.org/en/stable/), and
[sharrow](https://activitysim.github.io/sharrow) to enable super-fast estimation
of choice models. Many (but not yet all) of the core features of Larch have been moved
over to this new platform.
You can still use the old version of Larch as normal, but to try out the new version
just import larch.numba instead of larch itself.
This project is very much under development. There are plenty of undocumented functions
and features; use them at your own risk. Undocumented features may be non-functional,
not rigorously tested, deprecated or removed without notice in a future version.
About
Larch: a Python tool for choice modeling
larch.newman.me
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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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Larch: the logit architect
This is a tool for the estimation and application of logit-based discrete choice models.
It is designed to integrate with NumPy and facilitate fast processing of linear models.
If you want to estimate non-linear models, try Biogeme,
which is more flexible in form and can be used for almost any model structure.
If you don't know what the difference is, you probably want to start with linear models.
Larch is undergoing a transformation, with a new computational architecture
that can significantly improve performance when working with large datasets.
The new code relies on [numba](https://numba.pydata.org/),
[xarray](https://xarray.pydata.org/en/stable/), and
[sharrow](https://activitysim.github.io/sharrow) to enable super-fast estimation
of choice models. Many (but not yet all) of the core features of Larch have been moved
over to this new platform.
You can still use the old version of Larch as normal, but to try out the new version
just import larch.numba instead of larch itself.
This project is very much under development. There are plenty of undocumented functions
and features; use them at your own risk. Undocumented features may be non-functional,
not rigorously tested, deprecated or removed without notice in a future version.
About
Larch: a Python tool for choice modeling
larch.newman.me
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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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larch
https://img.shields.io/conda/v/conda-forge/larchhttps://img.shields.io/conda/dn/conda-forge/larchhttps://img.shields.io/conda/l/conda-forge/larch
Larch: the logit architect
This is a tool for the estimation and application of logit-based discrete choice models.
It is designed to integrate with NumPy and facilitate fast processing of linear models.
If you want to estimate non-linear models, try Biogeme,
which is more flexible in form and can be used for almost any model structure.
If you don't know what the difference is, you probably want to start with linear models.
Larch is undergoing a transformation, with a new computational architecture
that can significantly improve performance when working with large datasets.
The new code relies on [numba](https://numba.pydata.org/),
[xarray](https://xarray.pydata.org/en/stable/), and
[sharrow](https://activitysim.github.io/sharrow) to enable super-fast estimation
of choice models. Many (but not yet all) of the core features of Larch have been moved
over to this new platform.
You can still use the old version of Larch as normal, but to try out the new version
just import larch.numba instead of larch itself.
This project is very much under development. There are plenty of undocumented functions
and features; use them at your own risk. Undocumented features may be non-functional,
not rigorously tested, deprecated or removed without notice in a future version.
About
Larch: a Python tool for choice modeling
larch.newman.me
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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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https://img.shields.io/conda/v/conda-forge/larchhttps://img.shields.io/conda/dn/conda-forge/larchhttps://img.shields.io/conda/l/conda-forge/larch
Larch: the logit architect
This is a tool for the estimation and application of logit-based discrete choice models.
It is designed to integrate with NumPy and facilitate fast processing of linear models.
If you want to estimate non-linear models, try Biogeme,
which is more flexible in form and can be used for almost any model structure.
If you don't know what the difference is, you probably want to start with linear models.
Larch is undergoing a transformation, with a new computational architecture
that can significantly improve performance when working with large datasets.
The new code relies on [numba](https://numba.pydata.org/),
[xarray](https://xarray.pydata.org/en/stable/), and
[sharrow](https://activitysim.github.io/sharrow) to enable super-fast estimation
of choice models. Many (but not yet all) of the core features of Larch have been moved
over to this new platform.
You can still use the old version of Larch as normal, but to try out the new version
just import larch.numba instead of larch itself.
This project is very much under development. There are plenty of undocumented functions
and features; use them at your own risk. Undocumented features may be non-functional,
not rigorously tested, deprecated or removed without notice in a future version.
About
Larch: a Python tool for choice modeling
larch.newman.me
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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");
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} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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https://img.shields.io/conda/v/conda-forge/larchhttps://img.shields.io/conda/dn/conda-forge/larchhttps://img.shields.io/conda/l/conda-forge/larch
Larch: the logit architect
This is a tool for the estimation and application of logit-based discrete choice models.
It is designed to integrate with NumPy and facilitate fast processing of linear models.
If you want to estimate non-linear models, try Biogeme,
which is more flexible in form and can be used for almost any model structure.
If you don't know what the difference is, you probably want to start with linear models.
Larch is undergoing a transformation, with a new computational architecture
that can significantly improve performance when working with large datasets.
The new code relies on [numba](https://numba.pydata.org/),
[xarray](https://xarray.pydata.org/en/stable/), and
[sharrow](https://activitysim.github.io/sharrow) to enable super-fast estimation
of choice models. Many (but not yet all) of the core features of Larch have been moved
over to this new platform.
You can still use the old version of Larch as normal, but to try out the new version
just import larch.numba instead of larch itself.
This project is very much under development. There are plenty of undocumented functions
and features; use them at your own risk. Undocumented features may be non-functional,
not rigorously tested, deprecated or removed without notice in a future version.
About
Larch: a Python tool for choice modeling
larch.newman.me
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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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Larch: the logit architect
This is a tool for the estimation and application of logit-based discrete choice models.
It is designed to integrate with NumPy and facilitate fast processing of linear models.
If you want to estimate non-linear models, try Biogeme,
which is more flexible in form and can be used for almost any model structure.
If you don't know what the difference is, you probably want to start with linear models.
Larch is undergoing a transformation, with a new computational architecture
that can significantly improve performance when working with large datasets.
The new code relies on [numba](https://numba.pydata.org/),
[xarray](https://xarray.pydata.org/en/stable/), and
[sharrow](https://activitysim.github.io/sharrow) to enable super-fast estimation
of choice models. Many (but not yet all) of the core features of Larch have been moved
over to this new platform.
You can still use the old version of Larch as normal, but to try out the new version
just import larch.numba instead of larch itself.
This project is very much under development. There are plenty of undocumented functions
and features; use them at your own risk. Undocumented features may be non-functional,
not rigorously tested, deprecated or removed without notice in a future version.
About
Larch: a Python tool for choice modeling
larch.newman.me
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