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GFM

=========================================================================

GFM: Generalized factor model for ultra-high dimensional variables with mixed types.

GFM is a package for analyzing the (ultra)high dimensional data with mixed-type variables, developed by the Huazhen Lin's lab. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the number of factors. In our JASA paper, a two-step method is proposed to estimate the factor and loading matrix, in which the first step used the alternate maximization (AM) algorithm to obtain initial estimator. In the paper, the information criterion was provided to determine the number of factors. Recently, we proposed an overdispersed generalized factor model (OverGFM) and designed a variational EM algorithm to implement OverGFM. A singular value ratio based method was provided to determine the number of factors. In addition, the estimate from OverGFM can be also used as the initial estimates in the first step for GFMs in our previous JASA paper.

Check out our JASA paper for alternate maximization and information criterion, SIM paper for the variational EM and singular value ratio based method, and our Package vignette for a more complete description of the usage of GFM and OverGFM.

GFM and OverGFM can be used to analyze experimental dataset from different areas, for instance:

  • Social and behavioral sciences
  • Economy and finance
  • Genomics...

Please see our new paper for model details:

Installation

To install the the packages 'GFM' from 'Github', firstly, install the 'remotes' package.

install.packages("remotes")
remotes::install_github("feiyoung/GFM")

Or install the the packages "GFM" from 'CRAN'

install.packages("GFM")

Usage

For usage examples and guided walkthroughs, check the vignettes directory of the repo.

Demonstration

For an example of typical GFM usage, please see our Package vignette for a demonstration and overview of the functions included in GFM.

NEWs

GFM version 1.2.2 (2026-01-17) Update the add_identifiability() function to achieve a faster implementation for large datasets.

GFM version 1.2.1 (2023-08-10)

The function overdispersedGFM() that implements the overdispersed generalized factor model is added. In addition, the function OverGFMchooseFacNumber() is added, which implements singular value ratio (SVR) based method to select the number of factors.

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Generalized factor model for ultrahigh dimensional mixed-type data

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approximate-factor-modelfeature-extractionnonlinear-dimension-reductionnumber-of-factors

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GFM
=========================================================================
GFM: Generalized factor model for ultra-high dimensional variables with mixed types.
GFM is a package for analyzing the (ultra)high dimensional data with mixed-type variables, developed by the Huazhen Lin's lab. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the number of factors. In our JASA paper, a two-step method is proposed to estimate the factor and loading matrix, in which the first step used the alternate maximization (AM) algorithm to obtain initial estimator. In the paper, the information criterion was provided to determine the number of factors. Recently, we proposed an overdispersed generalized factor model (OverGFM) and designed a variational EM algorithm to implement OverGFM. A singular value ratio based method was provided to determine the number of factors. In addition, the estimate from OverGFM can be also used as the initial estimates in the first step for GFMs in our previous JASA paper.
Check out our JASA paper for alternate maximization and information criterion, SIM paper for the variational EM and singular value ratio based method, and our Package vignette for a more complete description of the usage of GFM and OverGFM.
GFM and OverGFM can be used to analyze experimental dataset from different areas, for instance:
  • Social and behavioral sciences
  • Economy and finance
  • Genomics...
Please see our new paper for model details:
Installation
To install the the packages 'GFM' from 'Github', firstly, install the 'remotes' package.
install.packages("remotes")
remotes::install_github("feiyoung/GFM")

Or install the the packages "GFM" from 'CRAN'
install.packages("GFM")

Usage
For usage examples and guided walkthroughs, check the vignettes directory of the repo.
Demonstration
For an example of typical GFM usage, please see our Package vignette for a demonstration and overview of the functions included in GFM.
NEWs
GFM version 1.2.2 (2026-01-17) Update the add_identifiability() function to achieve a faster implementation for large datasets.
GFM version 1.2.1 (2023-08-10)
The function overdispersedGFM() that implements the overdispersed generalized factor model is added.
In addition, the function OverGFMchooseFacNumber() is added, which implements singular value ratio (SVR) based method to select the number of factors.
About
Generalized factor model for ultrahigh dimensional mixed-type data
Topics
approximate-factor-modelfeature-extractionnonlinear-dimension-reductionnumber-of-factors
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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GFM
=========================================================================
GFM: Generalized factor model for ultra-high dimensional variables with mixed types.
GFM is a package for analyzing the (ultra)high dimensional data with mixed-type variables, developed by the Huazhen Lin's lab. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the number of factors. In our JASA paper, a two-step method is proposed to estimate the factor and loading matrix, in which the first step used the alternate maximization (AM) algorithm to obtain initial estimator. In the paper, the information criterion was provided to determine the number of factors. Recently, we proposed an overdispersed generalized factor model (OverGFM) and designed a variational EM algorithm to implement OverGFM. A singular value ratio based method was provided to determine the number of factors. In addition, the estimate from OverGFM can be also used as the initial estimates in the first step for GFMs in our previous JASA paper.
Check out our JASA paper for alternate maximization and information criterion, SIM paper for the variational EM and singular value ratio based method, and our Package vignette for a more complete description of the usage of GFM and OverGFM.
GFM and OverGFM can be used to analyze experimental dataset from different areas, for instance:
  • Social and behavioral sciences
  • Economy and finance
  • Genomics...
Please see our new paper for model details:
Installation
To install the the packages 'GFM' from 'Github', firstly, install the 'remotes' package.
install.packages("remotes")
remotes::install_github("feiyoung/GFM")

Or install the the packages "GFM" from 'CRAN'
install.packages("GFM")

Usage
For usage examples and guided walkthroughs, check the vignettes directory of the repo.
Demonstration
For an example of typical GFM usage, please see our Package vignette for a demonstration and overview of the functions included in GFM.
NEWs
GFM version 1.2.2 (2026-01-17) Update the add_identifiability() function to achieve a faster implementation for large datasets.
GFM version 1.2.1 (2023-08-10)
The function overdispersedGFM() that implements the overdispersed generalized factor model is added.
In addition, the function OverGFMchooseFacNumber() is added, which implements singular value ratio (SVR) based method to select the number of factors.
About
Generalized factor model for ultrahigh dimensional mixed-type data
Topics
approximate-factor-modelfeature-extractionnonlinear-dimension-reductionnumber-of-factors
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
3 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");
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})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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GFM
=========================================================================
GFM: Generalized factor model for ultra-high dimensional variables with mixed types.
GFM is a package for analyzing the (ultra)high dimensional data with mixed-type variables, developed by the Huazhen Lin's lab. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the number of factors. In our JASA paper, a two-step method is proposed to estimate the factor and loading matrix, in which the first step used the alternate maximization (AM) algorithm to obtain initial estimator. In the paper, the information criterion was provided to determine the number of factors. Recently, we proposed an overdispersed generalized factor model (OverGFM) and designed a variational EM algorithm to implement OverGFM. A singular value ratio based method was provided to determine the number of factors. In addition, the estimate from OverGFM can be also used as the initial estimates in the first step for GFMs in our previous JASA paper.
Check out our JASA paper for alternate maximization and information criterion, SIM paper for the variational EM and singular value ratio based method, and our Package vignette for a more complete description of the usage of GFM and OverGFM.
GFM and OverGFM can be used to analyze experimental dataset from different areas, for instance:
  • Social and behavioral sciences
  • Economy and finance
  • Genomics...
Please see our new paper for model details:
Installation
To install the the packages 'GFM' from 'Github', firstly, install the 'remotes' package.
install.packages("remotes")
remotes::install_github("feiyoung/GFM")

Or install the the packages "GFM" from 'CRAN'
install.packages("GFM")

Usage
For usage examples and guided walkthroughs, check the vignettes directory of the repo.
Demonstration
For an example of typical GFM usage, please see our Package vignette for a demonstration and overview of the functions included in GFM.
NEWs
GFM version 1.2.2 (2026-01-17) Update the add_identifiability() function to achieve a faster implementation for large datasets.
GFM version 1.2.1 (2023-08-10)
The function overdispersedGFM() that implements the overdispersed generalized factor model is added.
In addition, the function OverGFMchooseFacNumber() is added, which implements singular value ratio (SVR) based method to select the number of factors.
About
Generalized factor model for ultrahigh dimensional mixed-type data
Topics
approximate-factor-modelfeature-extractionnonlinear-dimension-reductionnumber-of-factors
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
3 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");
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})();
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var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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GFM
=========================================================================
GFM: Generalized factor model for ultra-high dimensional variables with mixed types.
GFM is a package for analyzing the (ultra)high dimensional data with mixed-type variables, developed by the Huazhen Lin's lab. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the number of factors. In our JASA paper, a two-step method is proposed to estimate the factor and loading matrix, in which the first step used the alternate maximization (AM) algorithm to obtain initial estimator. In the paper, the information criterion was provided to determine the number of factors. Recently, we proposed an overdispersed generalized factor model (OverGFM) and designed a variational EM algorithm to implement OverGFM. A singular value ratio based method was provided to determine the number of factors. In addition, the estimate from OverGFM can be also used as the initial estimates in the first step for GFMs in our previous JASA paper.
Check out our JASA paper for alternate maximization and information criterion, SIM paper for the variational EM and singular value ratio based method, and our Package vignette for a more complete description of the usage of GFM and OverGFM.
GFM and OverGFM can be used to analyze experimental dataset from different areas, for instance:
  • Social and behavioral sciences
  • Economy and finance
  • Genomics...
Please see our new paper for model details:
Installation
To install the the packages 'GFM' from 'Github', firstly, install the 'remotes' package.
install.packages("remotes")
remotes::install_github("feiyoung/GFM")

Or install the the packages "GFM" from 'CRAN'
install.packages("GFM")

Usage
For usage examples and guided walkthroughs, check the vignettes directory of the repo.
Demonstration
For an example of typical GFM usage, please see our Package vignette for a demonstration and overview of the functions included in GFM.
NEWs
GFM version 1.2.2 (2026-01-17) Update the add_identifiability() function to achieve a faster implementation for large datasets.
GFM version 1.2.1 (2023-08-10)
The function overdispersedGFM() that implements the overdispersed generalized factor model is added.
In addition, the function OverGFMchooseFacNumber() is added, which implements singular value ratio (SVR) based method to select the number of factors.
About
Generalized factor model for ultrahigh dimensional mixed-type data
Topics
approximate-factor-modelfeature-extractionnonlinear-dimension-reductionnumber-of-factors
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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GFM
=========================================================================
GFM: Generalized factor model for ultra-high dimensional variables with mixed types.
GFM is a package for analyzing the (ultra)high dimensional data with mixed-type variables, developed by the Huazhen Lin's lab. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the number of factors. In our JASA paper, a two-step method is proposed to estimate the factor and loading matrix, in which the first step used the alternate maximization (AM) algorithm to obtain initial estimator. In the paper, the information criterion was provided to determine the number of factors. Recently, we proposed an overdispersed generalized factor model (OverGFM) and designed a variational EM algorithm to implement OverGFM. A singular value ratio based method was provided to determine the number of factors. In addition, the estimate from OverGFM can be also used as the initial estimates in the first step for GFMs in our previous JASA paper.
Check out our JASA paper for alternate maximization and information criterion, SIM paper for the variational EM and singular value ratio based method, and our Package vignette for a more complete description of the usage of GFM and OverGFM.
GFM and OverGFM can be used to analyze experimental dataset from different areas, for instance:
  • Social and behavioral sciences
  • Economy and finance
  • Genomics...
Please see our new paper for model details:
Installation
To install the the packages 'GFM' from 'Github', firstly, install the 'remotes' package.
install.packages("remotes")
remotes::install_github("feiyoung/GFM")

Or install the the packages "GFM" from 'CRAN'
install.packages("GFM")

Usage
For usage examples and guided walkthroughs, check the vignettes directory of the repo.
Demonstration
For an example of typical GFM usage, please see our Package vignette for a demonstration and overview of the functions included in GFM.
NEWs
GFM version 1.2.2 (2026-01-17) Update the add_identifiability() function to achieve a faster implementation for large datasets.
GFM version 1.2.1 (2023-08-10)
The function overdispersedGFM() that implements the overdispersed generalized factor model is added.
In addition, the function OverGFMchooseFacNumber() is added, which implements singular value ratio (SVR) based method to select the number of factors.
About
Generalized factor model for ultrahigh dimensional mixed-type data
Topics
approximate-factor-modelfeature-extractionnonlinear-dimension-reductionnumber-of-factors
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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GFM
=========================================================================
GFM: Generalized factor model for ultra-high dimensional variables with mixed types.
GFM is a package for analyzing the (ultra)high dimensional data with mixed-type variables, developed by the Huazhen Lin's lab. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the number of factors. In our JASA paper, a two-step method is proposed to estimate the factor and loading matrix, in which the first step used the alternate maximization (AM) algorithm to obtain initial estimator. In the paper, the information criterion was provided to determine the number of factors. Recently, we proposed an overdispersed generalized factor model (OverGFM) and designed a variational EM algorithm to implement OverGFM. A singular value ratio based method was provided to determine the number of factors. In addition, the estimate from OverGFM can be also used as the initial estimates in the first step for GFMs in our previous JASA paper.
Check out our JASA paper for alternate maximization and information criterion, SIM paper for the variational EM and singular value ratio based method, and our Package vignette for a more complete description of the usage of GFM and OverGFM.
GFM and OverGFM can be used to analyze experimental dataset from different areas, for instance:
  • Social and behavioral sciences
  • Economy and finance
  • Genomics...
Please see our new paper for model details:
Installation
To install the the packages 'GFM' from 'Github', firstly, install the 'remotes' package.
install.packages("remotes")
remotes::install_github("feiyoung/GFM")

Or install the the packages "GFM" from 'CRAN'
install.packages("GFM")

Usage
For usage examples and guided walkthroughs, check the vignettes directory of the repo.
Demonstration
For an example of typical GFM usage, please see our Package vignette for a demonstration and overview of the functions included in GFM.
NEWs
GFM version 1.2.2 (2026-01-17) Update the add_identifiability() function to achieve a faster implementation for large datasets.
GFM version 1.2.1 (2023-08-10)
The function overdispersedGFM() that implements the overdispersed generalized factor model is added.
In addition, the function OverGFMchooseFacNumber() is added, which implements singular value ratio (SVR) based method to select the number of factors.
About
Generalized factor model for ultrahigh dimensional mixed-type data
Topics
approximate-factor-modelfeature-extractionnonlinear-dimension-reductionnumber-of-factors
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
3 forks
Report repository
Releases
Packages
Contributors
Languages

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GFM
=========================================================================
GFM: Generalized factor model for ultra-high dimensional variables with mixed types.
GFM is a package for analyzing the (ultra)high dimensional data with mixed-type variables, developed by the Huazhen Lin's lab. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the number of factors. In our JASA paper, a two-step method is proposed to estimate the factor and loading matrix, in which the first step used the alternate maximization (AM) algorithm to obtain initial estimator. In the paper, the information criterion was provided to determine the number of factors. Recently, we proposed an overdispersed generalized factor model (OverGFM) and designed a variational EM algorithm to implement OverGFM. A singular value ratio based method was provided to determine the number of factors. In addition, the estimate from OverGFM can be also used as the initial estimates in the first step for GFMs in our previous JASA paper.
Check out our JASA paper for alternate maximization and information criterion, SIM paper for the variational EM and singular value ratio based method, and our Package vignette for a more complete description of the usage of GFM and OverGFM.
GFM and OverGFM can be used to analyze experimental dataset from different areas, for instance:
  • Social and behavioral sciences
  • Economy and finance
  • Genomics...
Please see our new paper for model details:
Installation
To install the the packages 'GFM' from 'Github', firstly, install the 'remotes' package.
install.packages("remotes")
remotes::install_github("feiyoung/GFM")

Or install the the packages "GFM" from 'CRAN'
install.packages("GFM")

Usage
For usage examples and guided walkthroughs, check the vignettes directory of the repo.
Demonstration
For an example of typical GFM usage, please see our Package vignette for a demonstration and overview of the functions included in GFM.
NEWs
GFM version 1.2.2 (2026-01-17) Update the add_identifiability() function to achieve a faster implementation for large datasets.
GFM version 1.2.1 (2023-08-10)
The function overdispersedGFM() that implements the overdispersed generalized factor model is added.
In addition, the function OverGFMchooseFacNumber() is added, which implements singular value ratio (SVR) based method to select the number of factors.
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Generalized factor model for ultrahigh dimensional mixed-type data
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