Search Results for "structural equation modeling"
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Showing 36 open source projects for "structural equation modeling"
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DataDrivenDiffEq.jl
Data driven modeling and automated discovery of dynamical systems
DataDrivenDiffEq.jl is a package for finding systems of equations automatically from a dataset. The methods in this package take in data and return the model which generated the data. A known model is not required as input. These methods can estimate equation-free and equation-based models for discrete, continuous differential equations or direct mappings. -
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StructuralEquationModels.jl
A fast and flexible Structural Equation Modelling Framework
This is a package for Structural Equation Modeling in development. It is written for extensibility, that is, you can easily define your own objective functions and other parts of the model. At the same time, it is (very) fast. We provide fast objective functions, gradients, and for some cases hessians as well as approximations thereof. As a user, you can easily define custom loss functions. -
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ModelingToolkit.jl
Modeling framework for automatically parallelized scientific ML
ModelingToolkit.jl is a modeling language for high-performance symbolic-numeric computation in scientific computing and scientific machine learning. It then mixes ideas from symbolic computational algebra systems with causal and acausal equation-based modeling frameworks to give an extendable and parallel modeling system. It allows for users to give a high-level description of a model for symbolic preprocessing to analyze and enhance the model. -
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blavaan
An R package for Bayesian structural equation modeling
blavaan is a free, open-source R package for Bayesian latent variable analysis. It relies on JAGS and Stan to estimate models via MCMC. The blavaan functions and syntax are similar to lavaan. The development version of blavaan (containing updates not yet on CRAN) can be installed via the command provided in the documentation. Compilation is required; this may be a problem for users who currently rely on a binary version of blavaan from CRAN. The blavaan package depends on the lavaan package... -
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JUDI.jl
Julia Devito inversion
...JUDI's modeling operators can also be used as layers in (convolutional) neural networks to implement physics-augmented deep learning algorithms thanks to its implementation of ChainRules's rrule for the linear operators representing the discre wave equation. -
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CausalImpact
An R package for causal inference in time series
...It uses Bayesian modeling to fit a structural time series to the pre-period and extrapolate a counterfactual prediction for the post period, then compares observed vs predicted to infer the causal effect. The package supports plotting, summary tables, and verbal narratives for interpretive reports. -
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AlphaFold 3
AlphaFold 3 inference pipeline
...This repository provides the complete inference pipeline for running AlphaFold 3, though access to the model parameters is restricted and must be obtained directly from Google under specific terms of use. The system is designed for scientific research applications in structural biology, biochemistry, and bioinformatics, enabling accurate modeling of proteins, ligands, and covalent modifications. Users can perform local predictions via Docker containers, integrating AlphaFold 3’s inference process with provided JSON input configurations. The software includes flexible options for running both data preprocessing and GPU-accelerated inference, allowing users to adapt to available computational resources. -
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DSGE.jl
Solve and estimate Dynamic Stochastic General Equilibrium models
DSGE.jl is a Julia package developed by the Federal Reserve Bank of New York for estimating and analyzing dynamic stochastic general equilibrium (DSGE) models. It provides tools for Bayesian estimation, filtering, forecasting, and model comparison, supporting both academic research and policy applications. DSGE.jl includes pre-configured models used by central banks and offers extensibility for custom macroeconomic modeling. -
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statsmodels
Statsmodels, statistical modeling and econometrics in Python
statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license. Generalized linear models with support for all... -
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Protenix
A trainable PyTorch reproduction of AlphaFold 3
Protenix is an open-source, trainable PyTorch reimplementation of AlphaFold 3, developed by ByteDance with the goal of democratizing high-accuracy protein structure prediction for computational biology and drug-discovery research. Protenix provides a complete pipeline for turning protein sequences (with optional MSA / sequence alignment) or structural inputs (e.g. PDB/CIF) into full 3D atomic-level structure predictions. It supports both “full” models and lightweight variants such as “Protenix-Mini,” offering a trade-off between speed/compute cost and predictive accuracy — making structure prediction accessible even in resource-constrained environments. The project also includes support for constraints (e.g., specifying residue- or atom-level contact constraints, or pocket constraints) to guide predictions toward biologically or experimentally relevant conformations, which enhances its utility for tasks like modeling complexes, ligands, or antibody–antigen interactions. -
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Math Model
Code, resources, and templates for mathematical modeling
Math_Model is a repository collecting resources, code, and algorithm templates for mathematical modeling and competition (e.g. Chinese modeling contests, US undergraduate modeling competitions). It includes LaTeX templates for writing solutions, records of past contest problems and winning solutions, algorithm implementations in MATLAB / M scripts for optimization, intelligent algorithms, numerical methods, and model frameworks. In effect, it is a curated library of modeling code, papers,... -
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No-code system is for the visual creation of structural-functional models and the automatic generation of R language simulation models. The program can be used to describe information, production, organizational, and other processes. For graphical representation, the EdPM/EPM notation is used, which allowed us to implement: - structural-functional modeling using graphical methods; - the study of the efficiency of structural-functional models using simulation methods, that allow (e.g. unlike Petri nets) to process queries in groups, which is important for the study of the efficiency of using such methods as volumetric calendar planning and AI methods in process activities, since the operating time of these methods depends on the number of parameters and changes nonlinearly; - the study of multiprocess systems; - the results were obtained, that allow you to find efficient topologies of structural-functional models.
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GNNPCSAFT
Smart Thermodynamic Modeling with Graph Neural Networks
The GNNPCSAFT app is an implementation of our project that focuses on using Graph Neural Networks (GNN) to estimate the pure-component parameters of the Equation of State PC-SAFT. We developed this app so the scientific community can access the model's results easily. In this app, the estimated pure-component parameters can be used to calculate thermodynamic properties and compare them with experimental data from the ThermoML Archive. To install the GNNPCSAFT app, download the... -
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GNNPCSAFT Web App
Smart Thermodynamic Modeling with Graph Neural Networks
The GNNPCSAFT Web App is an implementation of our project that focuses on using Graph Neural Networks (GNN) to estimate the pure-component parameters of the Equation of State PC-SAFT. We developed this app so the scientific community can access the model's results easily. In this app, the estimated pure-component parameters can be used to calculate thermodynamic properties and compare them with experimental data from the ThermoML Archive. More info on github repository. -
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codeforces-go
Solutions to Codeforces by Go
...Reference links or book chapters (good material) Template code (can contain some comments, usage instructions) Template supplements (extra codes in common question types, modeling tips, etc.) Related topic links (template questions, classic questions, thinking conversion questions, etc.) The main goal of this stage is to improve the ability to observe problems. Doing construction questions can train this point in a targeted manner. Choose the structural questions (tag: constructive algorithms) whose difficulty ranges from your own rating to rating+200, and do the questions in descending order according to the number of people who have passed the questions. -
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DiPoly
Diffraction analysis of Polymers
...The Scherrer equation is used to determine the sizes of coherent scattering regions. The software can be used for structural diagnostics both in research and production control. To start the program run file Wpf_Polly.exe -
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AliceMind
ALIbaba's Collection of Encoder-decoders from MinD
This repository provides pre-trained encoder-decoder models and its related optimization techniques developed by Alibaba's MinD (Machine IntelligeNce of Damo) Lab. Pre-trained models for natural language understanding (NLU). We extend BERT to a new model, StructBERT, by incorporating language structures into pre-training. Specifically, we pre-train StructBERT with two auxiliary tasks to make the most of the sequential order of words and sentences, which leverage language structures at the... -
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Virtual Laboratory Environment
A multi-modeling and simulation environment to study complex systems
VLE is a multi-modeling and simulation environment to study complex dynamic systems. VLE is based on the discrete event specification DEVS. and it implements the DSDE formalism (A merge of Dynamic Structure DEVS, DSDEVS, with Parallel DEVS, PDEVS). VLE provides a complete set of C++ libraries, called VFL (VLE Foundation Libraries), to develop DEVS models, to gets results of simulations, to launch simulation on cluster. -
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BioNet-Finder
Network-based modeling and analysis for complex biological data
...This is carried out applying network analysis methods from different fields, such as Structural Equation Modeling (SEM), together with data reduction algorithms (e.g., random walkers, spectral analysis, shortest path-based or topology-based algorithms). The BioNet-Finder project is at its initial steps, and it will continuously update with new methods and code. -
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...Implementing the non-linear mixed effects theory, b. Maximum log-likelihood algorithms (stiff, non-stiff Ordinary Differential Equation solving methods with linearization)
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Indeterminate Beam Analysis Program
Calculates SF, BM, Slope and Deflection.
IBAP is a structural beam analysis software that can be used by Civil, Mechanical and Structural Engineers, Beam designers and Engineering students for both professional and academic purposes. -
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Gekko Timeseries Software
Timeseries handling, and solving of large-scale economic models
Gekko Timeseries Software is a free time-series oriented software package for timeseries handling, and solving and analyzing large-scale economic models. Since 2009, Gekko is being used by Danish ministeries, banks, interest groups and universities, for the simulation of economic and energy-related models. The software runs under Windows (.NET), and is open source (GNU GPL) -
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These scripts demonstrate the ability of one-dimensional formulations to reproduce the axial strain field of tensile single-walled carbon nanotubes (SWCNTs). The LAMMPS scripts return the molecular mechanics solution for some armchair and zigzag SWCNTs, while the Matlab scripts compute the numerical solution of a one-dimensional integro-differential tensile rod by B-spline finite elements. By using the proper set of nonlocal parameters, it is possible to correctly capture size and chirality...
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FPF_predict
Fine Particle Fraction (FPF) predictor
Application implements models described by classical mathematical equation for in vitro deposition prediction based on characteristics of formulation and assay conditions. This work was funded by Poland-Singapore bilateral cooperation project no 2/3/POL-SIN/2012. Published article: https://www.dovepress.com/empirical-modeling-of-the-fine-particle-fraction-fornbspcarrier-based--peer-reviewed-fulltext-article-IJN -
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SEM_bestShortestPath
A SEM-based method for disease module detection
...SEM-bsp is based on Structural Equation Modeling techniques to detect significantly perturbed sub-networks (disease modules) from microarray data, relevant for the disease phenotype under investigation.