Pub Date : 2025-03-18DOI: 10.1016/j.softx.2025.102126
Yexiang Yan , Yazhou Xie
This paper presents opstool, a Python package designed to enhance the pre- and post-processing capabilities of OpenSees and OpenSeesPy. It simplifies structural analysis workflows by automating tasks such as mesh generation, data management, and data visualization. The package efficiently manages large-scale simulation results, enabling the structured extraction of system, nodal, and element responses. In addition, it integrates adaptive iteration algorithms to improve convergence issues in nonlinear static and dynamic response analyses. By reducing manual modeling effort and enhancing model accuracy, opstool improves workflow efficiency and enables researchers and practitioners to conduct more effective computational simulations using OpenSees and OpenSeesPy, which further supports various task forces in earthquake engineering, such as performance-based design of new structures and regional seismic risk assessment of existing infrastructure systems.
{"title":"opstool: A Python library for OpenSeesPy analysis automation, streamlined pre- and post-processing, and enhanced data visualization","authors":"Yexiang Yan , Yazhou Xie","doi":"10.1016/j.softx.2025.102126","DOIUrl":"10.1016/j.softx.2025.102126","url":null,"abstract":"<div><div>This paper presents opstool, a Python package designed to enhance the pre- and post-processing capabilities of OpenSees and OpenSeesPy. It simplifies structural analysis workflows by automating tasks such as mesh generation, data management, and data visualization. The package efficiently manages large-scale simulation results, enabling the structured extraction of system, nodal, and element responses. In addition, it integrates adaptive iteration algorithms to improve convergence issues in nonlinear static and dynamic response analyses. By reducing manual modeling effort and enhancing model accuracy, opstool improves workflow efficiency and enables researchers and practitioners to conduct more effective computational simulations using OpenSees and OpenSeesPy, which further supports various task forces in earthquake engineering, such as performance-based design of new structures and regional seismic risk assessment of existing infrastructure systems.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102126"},"PeriodicalIF":2.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-18DOI: 10.1016/j.softx.2025.102062
Indigo Brownhall , Miles Lifson , Stephen Hall, Charles Constant , Giovanni Lavezzi , Marek Ziebart , Richard Linares , Santosh Bhattarai
The rapid increase in the number of Low-Earth Orbit (LEO) satellites and reducing launch costs is likely to threaten the orbital environment. Understanding how this growth will affect the orbital debris population is paramount to designing effective policy, regulation and mitigation to protect the long term space sustainability of LEO. This will require interdisciplinary research of potential impacts, demanding contributions from social scientists, economists, astronomers, and alike. However, the complexity of astrodynamics and technical ability to build evolutionary space environment models often poses a significant barrier to interdisciplinary engagement, impeding critical research in this area. Previous models and tools have been developed, but are often not open-source nor accessible. MIT Orbital Capacity Assessment Tools (MOCAT) was developed to provide an open-source evolutionary space environment modeling capability to the broader space and policy communities, featuring both a computationally intensive but higher fidelity full-scale Monte Carlo model (MOCAT-MC) and a lower fidelity but significantly faster source sink evolutionary modeling framework, (MOCAT-SSEM). Here we continue this journey by presenting a Python version of the source sink tool, MOCAT-pySSEM with an accompanying web application (featuring cloud-hosted computation) to support future interdisciplinary research.
{"title":"MOCAT-pySSEM: An open-source Python library and user interface for orbital debris and source sink environmental modeling","authors":"Indigo Brownhall , Miles Lifson , Stephen Hall, Charles Constant , Giovanni Lavezzi , Marek Ziebart , Richard Linares , Santosh Bhattarai","doi":"10.1016/j.softx.2025.102062","DOIUrl":"10.1016/j.softx.2025.102062","url":null,"abstract":"<div><div>The rapid increase in the number of Low-Earth Orbit (LEO) satellites and reducing launch costs is likely to threaten the orbital environment. Understanding how this growth will affect the orbital debris population is paramount to designing effective policy, regulation and mitigation to protect the long term space sustainability of LEO. This will require interdisciplinary research of potential impacts, demanding contributions from social scientists, economists, astronomers, and alike. However, the complexity of astrodynamics and technical ability to build evolutionary space environment models often poses a significant barrier to interdisciplinary engagement, impeding critical research in this area. Previous models and tools have been developed, but are often not open-source nor accessible. MIT Orbital Capacity Assessment Tools (MOCAT) was developed to provide an open-source evolutionary space environment modeling capability to the broader space and policy communities, featuring both a computationally intensive but higher fidelity full-scale Monte Carlo model (MOCAT-MC) and a lower fidelity but significantly faster source sink evolutionary modeling framework, (MOCAT-SSEM). Here we continue this journey by presenting a Python version of the source sink tool, MOCAT-pySSEM with an accompanying web application (featuring cloud-hosted computation) to support future interdisciplinary research.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102062"},"PeriodicalIF":2.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-17DOI: 10.1016/j.softx.2025.102121
Marco Perini , Daniele Antonucci , Rocco Giudice , Marco Savino Piscitelli , Alfonso Capozzoli
One of the key challenges of Energy Management and Information Systems in buildings is related to the lack of interoperability, due to the absence of standardization of the underlying data models. In recent years, there has been a growing interest in using ontology-based metadata models to address this issue, as they offer a structured approach to organize and share information across diverse systems (e.g. Brick ontology). However, the creation of ontology-based metadata models is often a labor-intensive task that requires specific domain expertise, hindering the practical use of such data models. For this reason, in this work the BrickLLM Python library is introduced, which addresses this issue by generating Brick-compliant Resource Description Framework graphs through Large Language Models, automating the process of converting natural language building descriptions into machine-readable metadata. The library supports both cloud-based APIs (e.g., OpenAI, Anthropic, Fireworks AI), local models (e.g. LLaMa3.2, etc.) and evenfine-tuned ones. This paper explores the architecture, key functionalities, and practical applications of BrickLLM, showcasing its potential impact on the future of building systems monitoring and automation.
{"title":"BrickLLM: A Python library for generating Brick-compliant RDF graphs using LLMs","authors":"Marco Perini , Daniele Antonucci , Rocco Giudice , Marco Savino Piscitelli , Alfonso Capozzoli","doi":"10.1016/j.softx.2025.102121","DOIUrl":"10.1016/j.softx.2025.102121","url":null,"abstract":"<div><div>One of the key challenges of Energy Management and Information Systems in buildings is related to the lack of interoperability, due to the absence of standardization of the underlying data models. In recent years, there has been a growing interest in using ontology-based metadata models to address this issue, as they offer a structured approach to organize and share information across diverse systems (e.g. Brick ontology). However, the creation of ontology-based metadata models is often a labor-intensive task that requires specific domain expertise, hindering the practical use of such data models. For this reason, in this work the BrickLLM Python library is introduced, which addresses this issue by generating Brick-compliant Resource Description Framework graphs through Large Language Models, automating the process of converting natural language building descriptions into machine-readable metadata. The library supports both cloud-based APIs (e.g., OpenAI, Anthropic, Fireworks AI), local models (e.g. LLaMa3.2, etc.) and evenfine-tuned ones. This paper explores the architecture, key functionalities, and practical applications of BrickLLM, showcasing its potential impact on the future of building systems monitoring and automation.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102121"},"PeriodicalIF":2.4,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-15DOI: 10.1016/j.softx.2025.102130
Vartul Shrivastava , Shekhar Shukla
In today's technological landscape, where data processing forms the backbone of modeling and predictive analytics, data imputation is crucial in filling missing values within datasets using statistical techniques. However, a notable gap exists in the literature for a toolkit that intuitively offers data imputation. This research aims to bridge the gap by introducing Impute-VSS. This web-based imputation suite offers comparative simulations along with a novel imputation management pipeline for in-depth interpretation of imputation techniques. This manuscript showcases the technical premise of Impute-VSS and emphasises its potential as an inclusive data imputation suite for practitioners and researchers.
{"title":"Impute-VSS: A comprehensive web-based visualization and simulation suite for comparative data imputation and statistical evaluation","authors":"Vartul Shrivastava , Shekhar Shukla","doi":"10.1016/j.softx.2025.102130","DOIUrl":"10.1016/j.softx.2025.102130","url":null,"abstract":"<div><div>In today's technological landscape, where data processing forms the backbone of modeling and predictive analytics, data imputation is crucial in filling missing values within datasets using statistical techniques. However, a notable gap exists in the literature for a toolkit that intuitively offers data imputation. This research aims to bridge the gap by introducing Impute-VSS. This web-based imputation suite offers comparative simulations along with a novel imputation management pipeline for in-depth interpretation of imputation techniques. This manuscript showcases the technical premise of Impute-VSS and emphasises its potential as an inclusive data imputation suite for practitioners and researchers.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102130"},"PeriodicalIF":2.4,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-14DOI: 10.1016/j.softx.2025.102129
Yu Wang , Renfu Li , Kun Wang
This paper presents an open-source framework based on unstructured meshes for three-dimensional large-scale parallel topology optimization using PETSc and Eigen, which is easy to use and expand. The framework supports both eight-node hexahedral and four-node tetrahedral meshes and solves the compliance and stress topology optimization problem. The method of moving asymptotes (MMA) is chosen as the optimization solver. The validity of the framework is demonstrated by a classical cantilever beam problem and two more examples of wheel rim and rotator illustrate the expansibility of the framework.
{"title":"An open-source parallel topology optimization framework based on unstructured 3D FEA using PETSc and Eigen","authors":"Yu Wang , Renfu Li , Kun Wang","doi":"10.1016/j.softx.2025.102129","DOIUrl":"10.1016/j.softx.2025.102129","url":null,"abstract":"<div><div>This paper presents an open-source framework based on unstructured meshes for three-dimensional large-scale parallel topology optimization using PETSc and Eigen, which is easy to use and expand. The framework supports both eight-node hexahedral and four-node tetrahedral meshes and solves the compliance and stress topology optimization problem. The method of moving asymptotes (MMA) is chosen as the optimization solver. The validity of the framework is demonstrated by a classical cantilever beam problem and two more examples of wheel rim and rotator illustrate the expansibility of the framework.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102129"},"PeriodicalIF":2.4,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-14DOI: 10.1016/j.softx.2025.102123
Xing Zheng Wu
The PileBetaGR package is a web application designed to enable accessible and reproducible computation of the geometric reliability index for piles using site-specific load-displacement curves. The application compiles a series of functions for analyzing load-displacement data: (i) a power law regression is used to fit each load-displacement curve, yielding a set of regression parameters for the site; (ii) a normal copula model is established to fit the joint distribution of these regression variables, allowing a geometric reliability index to be computed; (iii) the critical environmental contour is determined based on the joint probability density function and the limit state function. The PileBetaGR enables users to construct three- and four-dimensional environmental contours by treating the dead and live load as random variables and to understand the roles various correlation coefficients, marginal distributions, and loading ratios play in the reliability index evaluation. A web application that facilitates the use of the package even for those with no background in R programming is offered via Shiny apps.
PileBetaGR 软件包是一个网络应用程序，旨在利用特定场地的荷载-位移曲线，以可访问和可重复的方式计算桩的几何可靠性指数。该应用程序编译了一系列分析荷载-位移数据的功能：(i) 使用幂律回归法拟合每条荷载-位移曲线，从而得出场地的一组回归参数；(ii) 建立正态共线模型以拟合这些回归变量的联合分布，从而计算出几何可靠性指数；(iii) 根据联合概率密度函数和极限状态函数确定临界环境等值线。PileBetaGR 使用户能够通过将死荷载和活荷载视为随机变量来构建三维和四维环境等值线，并了解各种相关系数、边际分布和荷载比在可靠性指数评估中的作用。通过 Shiny 应用程序，即使没有 R 编程背景的人也能方便地使用该软件包。
{"title":"PileBetaGR: An R-based integrative tool for predicting the geometric reliability index of piles using load-displacement curves","authors":"Xing Zheng Wu","doi":"10.1016/j.softx.2025.102123","DOIUrl":"10.1016/j.softx.2025.102123","url":null,"abstract":"<div><div>The PileBetaGR package is a web application designed to enable accessible and reproducible computation of the geometric reliability index for piles using site-specific load-displacement curves. The application compiles a series of functions for analyzing load-displacement data: (i) a power law regression is used to fit each load-displacement curve, yielding a set of regression parameters for the site; (ii) a normal copula model is established to fit the joint distribution of these regression variables, allowing a geometric reliability index to be computed; (iii) the critical environmental contour is determined based on the joint probability density function and the limit state function. The PileBetaGR enables users to construct three- and four-dimensional environmental contours by treating the dead and live load as random variables and to understand the roles various correlation coefficients, marginal distributions, and loading ratios play in the reliability index evaluation. A web application that facilitates the use of the package even for those with no background in R programming is offered via Shiny apps.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102123"},"PeriodicalIF":2.4,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-12DOI: 10.1016/j.softx.2025.102118
Miłosz Wieczór , Jacek Czub , Modesto Orozco
Despite the increasing automation of workflows for the preparation of systems for molecular dynamics simulations, the custom editing of molecular topologies to accommodate non-standard modifications remains a daunting task even for experienced users. To alleviate this issue, we created Gromologist, a utility library that provides the simulation community with a toolbox of primitive operations, as well as useful repetitive procedures identified during years of research. The library has been developed in response to users’ feedback, and will continue to grow to include more use cases, thorough automatic testing and support for a broader spectrum of rare features. The program is available at gitlab.com/KomBioMol/gromologist and via Python’s pip.
{"title":"Gromologist: A GROMACS-oriented utility library for structure and topology manipulation","authors":"Miłosz Wieczór , Jacek Czub , Modesto Orozco","doi":"10.1016/j.softx.2025.102118","DOIUrl":"10.1016/j.softx.2025.102118","url":null,"abstract":"<div><div>Despite the increasing automation of workflows for the preparation of systems for molecular dynamics simulations, the custom editing of molecular topologies to accommodate non-standard modifications remains a daunting task even for experienced users. To alleviate this issue, we created Gromologist, a utility library that provides the simulation community with a toolbox of primitive operations, as well as useful repetitive procedures identified during years of research. The library has been developed in response to users’ feedback, and will continue to grow to include more use cases, thorough automatic testing and support for a broader spectrum of rare features. The program is available at <span><span>gitlab.com/KomBioMol/gromologist</span><svg><path></path></svg></span> and via Python’s pip.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102118"},"PeriodicalIF":2.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-12DOI: 10.1016/j.softx.2025.102116
Michał Rokita, Mateusz Modrzejewski, Przemysław Rokita
In this paper, we present PyBrook - an original real-time cloud computing framework for the Internet of Things. PyBrook enables users to define complex data processing models declaratively, using the Python programming language. The framework also provides a generic web interface that presents the collected data in real time. PyBrook aims to make the development of real-time data processing services as easy as possible by utilising powerful mechanisms of the Python programming language and modern concepts like hot-reloading or deploying software in Linux Containers. To ensure reproducibility, PyBrook has been published both as a Python package available on PyPi and a Docker container image available on Docker Hub.
{"title":"PyBrook—A Python framework for processing and visualising real-time data","authors":"Michał Rokita, Mateusz Modrzejewski, Przemysław Rokita","doi":"10.1016/j.softx.2025.102116","DOIUrl":"10.1016/j.softx.2025.102116","url":null,"abstract":"<div><div>In this paper, we present PyBrook - an original real-time cloud computing framework for the Internet of Things. PyBrook enables users to define complex data processing models declaratively, using the Python programming language. The framework also provides a generic web interface that presents the collected data in real time. PyBrook aims to make the development of real-time data processing services as easy as possible by utilising powerful mechanisms of the Python programming language and modern concepts like hot-reloading or deploying software in Linux Containers. To ensure reproducibility, PyBrook has been published both as a Python package available on PyPi and a Docker container image available on Docker Hub.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102116"},"PeriodicalIF":2.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-12DOI: 10.1016/j.softx.2025.102117
Akshay Patil , Udhaya Chandiran Krishnan Paranjothi , Clara García-Sánchez
This paper presents a highly efficient signed-distance field (SDF) generator designed specifically for computational fluid dynamics (CFD) workflows. Our approach integrates the Message Passing Interface (MPI) for parallel computing with the performance benefits of modern Fortran, enabling efficient and scalable signed distance field (SDF) computations for complex geometries. The algorithm focuses on localized distance calculations to minimize computational overhead, ensuring efficiency across multiple processors. An adjustable stencil width allows users to balance computational cost with the desired level of accuracy in the distance approximation. Additionally, GenSDF supports the widely used Wavefront OBJ format, utilizing its encoded outward normal information to achieve accurate boundary definitions. Performance benchmarks demonstrate the tool’s ability to handle large-scale 3D models ( triangulation faces) and computational grid points with high fidelity and reduced computational demands. This makes it a practical and effective solution for CFD applications that require fast, reliable distance field computations while accommodating diverse geometric complexities.
{"title":"GenSDF: An MPI-Fortran based signed-distance-field generator for computational fluid dynamics applications","authors":"Akshay Patil , Udhaya Chandiran Krishnan Paranjothi , Clara García-Sánchez","doi":"10.1016/j.softx.2025.102117","DOIUrl":"10.1016/j.softx.2025.102117","url":null,"abstract":"<div><div>This paper presents a highly efficient signed-distance field (SDF) generator designed specifically for computational fluid dynamics (CFD) workflows. Our approach integrates the Message Passing Interface (MPI) for parallel computing with the performance benefits of modern Fortran, enabling efficient and scalable signed distance field (SDF) computations for complex geometries. The algorithm focuses on localized distance calculations to minimize computational overhead, ensuring efficiency across multiple processors. An adjustable stencil width allows users to balance computational cost with the desired level of accuracy in the distance approximation. Additionally, GenSDF supports the widely used Wavefront OBJ format, utilizing its encoded outward normal information to achieve accurate boundary definitions. Performance benchmarks demonstrate the tool’s ability to handle large-scale 3D models (<span><math><mrow><mo>∼</mo><mi>O</mi><mrow><mo>(</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>7</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> triangulation faces) and computational grid points <span><math><mrow><mo>∼</mo><mi>O</mi><mrow><mo>(</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>9</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> with high fidelity and reduced computational demands. This makes it a practical and effective solution for CFD applications that require fast, reliable distance field computations while accommodating diverse geometric complexities.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102117"},"PeriodicalIF":2.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-11DOI: 10.1016/j.softx.2025.102112
Kenneth J. Nieser , Bryan Saúl Ortiz-Torres , Gabriel Zayas-Cabán , Amy Cochran
Factor analysis is a widely used statistical method for describing a large number of observed, correlated variables in terms of a smaller number of unobserved variables. Applications of this method usually impose the same latent variable model on all individuals in the sample, but this assumption might not hold as individuals can differ in attributes (e.g., age, gender) that influence model parameters. REMLA is an R package that implements a robust expectation–maximization (REM) algorithm to estimate the parameters for factor analysis models in a way that automatically acknowledges, and even detects, differences among individuals within the sample. This paper explains the methodological background of the estimation process, describes the algorithms employed, and illustrates how REMLA can be used to perform exploratory and confirmatory factor analyses through examples. In the future, we plan to extend this package to other latent variable models, such as mixture models.
{"title":"REMLA: An R package for robust expectation-maximization estimation for latent variable models","authors":"Kenneth J. Nieser , Bryan Saúl Ortiz-Torres , Gabriel Zayas-Cabán , Amy Cochran","doi":"10.1016/j.softx.2025.102112","DOIUrl":"10.1016/j.softx.2025.102112","url":null,"abstract":"<div><div>Factor analysis is a widely used statistical method for describing a large number of observed, correlated variables in terms of a smaller number of unobserved variables. Applications of this method usually impose the same latent variable model on all individuals in the sample, but this assumption might not hold as individuals can differ in attributes (e.g., age, gender) that influence model parameters. REMLA is an R package that implements a robust expectation–maximization (REM) algorithm to estimate the parameters for factor analysis models in a way that automatically acknowledges, and even detects, differences among individuals within the sample. This paper explains the methodological background of the estimation process, describes the algorithms employed, and illustrates how REMLA can be used to perform exploratory and confirmatory factor analyses through examples. In the future, we plan to extend this package to other latent variable models, such as mixture models.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"30 ","pages":"Article 102112"},"PeriodicalIF":2.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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