Alternatives to DataMelt
Compare DataMelt alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to DataMelt in 2025. Compare features, ratings, user reviews, pricing, and more from DataMelt competitors and alternatives in order to make an informed decision for your business.
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JMP Statistical Software
JMP Statistical Discovery
JMP, data analysis software for Mac and Windows, combines the strength of interactive visualization with powerful statistics. Importing and processing data is easy. The drag-and-drop interface, dynamically linked graphs, libraries of advanced analytic functionality, scripting language and ways of sharing findings with others, allows users to dig deeply into their data, with greater ease and speed. Originally developed in the 1980’s to capture the new value in GUI for personal computers, JMP remains dedicated to adding cutting-edge statistical methods and special analysis techniques from a variety of industries to the software’s functionality with each release. The organization's founder, John Sall, still serves as Chief Architect.Starting Price: $1320/year/user -
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NLREG
NLREG
NLREG is a powerful statistical analysis program that performs linear and nonlinear regression analysis, surface and curve fitting. NLREG determines the values of parameters for an equation, whose form you specify, that cause the equation to best fit a set of data values. NLREG can handle linear, polynomial, exponential, logistic, periodic, and general nonlinear functions. Unlike many "nonlinear" regression programs that can only handle a limited set of function forms, NLREG can handle essentially any function whose form you can specify algebraically. NLREG features a full programming language with a syntax similar to C for specifying the function that is to be fitted to the data. This allows you to compute intermediate work variables, use conditionals, and even iterate in loops. With NLREG it is easy to construct piecewise functions that change form over different domains. Since the NLREG language includes arrays, you can even use tabular look-up methods to define the function. -
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Altair Compose
Altair Engineering
Analyzing data, developing algorithms, or creating models - Altair Compose is designed to bring your ideas forward. Altair Compose is an environment for doing math calculations, manipulating, and visualizing data, programming, and debugging scripts useful for repeated computations and process automation. Altair Compose allows users to perform a wide variety of math operations including linear algebra and matrix manipulations, statistics, differential equations, signal processing, control systems, polynomial fitting, and optimization. The broad set of native CAE and test result readers accelerates system understanding and works with Altair Activate® to support model-based development, for multi-domain and system of systems simulations. Altair Embed® completes the model-based design portfolio with automated code generation, allowing for the testing and verification of embedded systems. -
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Statistix
Analytical Software
lf you have data to analyze—but you're a researcher, not a statistician—Statistix is designed for you. You'll be up and running in minutes—without programming or using the manual! This easy to learn and simple to use software saves you valuable time and money. Statistix combines all the basic and advanced statistics and powerful data manipulation tools you need in a single, inexpensive package. Statistix offers powerful data manipulation tools, import/export support for Excel and text files, linear models (including linear regression, logistic regression, Poisson regression, and ANOVA), nonlinear regression, nonparametric tests, time series, association tests, survival analysis, quality control, power analysis, and more.Starting Price: $395 one-time payment -
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Microsoft Cognitive Toolkit
Microsoft
The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK can be included as a library in your Python, C#, or C++ programs, or used as a standalone machine-learning tool through its own model description language (BrainScript). In addition you can use the CNTK model evaluation functionality from your Java programs. CNTK supports 64-bit Linux or 64-bit Windows operating systems. To install you can either choose pre-compiled binary packages, or compile the toolkit from the source provided in GitHub. -
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Deeplearning4j
Deeplearning4j
DL4J takes advantage of the latest distributed computing frameworks including Apache Spark and Hadoop to accelerate training. On multi-GPUs, it is equal to Caffe in performance. The libraries are completely open-source, Apache 2.0, and maintained by the developer community and Konduit team. Deeplearning4j is written in Java and is compatible with any JVM language, such as Scala, Clojure, or Kotlin. The underlying computations are written in C, C++, and Cuda. Keras will serve as the Python API. Eclipse Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Apache Spark, DL4J brings AI to business environments for use on distributed GPUs and CPUs. There are a lot of parameters to adjust when you're training a deep-learning network. We've done our best to explain them, so that Deeplearning4j can serve as a DIY tool for Java, Scala, Clojure, and Kotlin programmers. -
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R
The R Foundation
R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.Starting Price: Free -
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QMSys GUM
Qualisyst
The QMSys GUM Software is suitable for the analysis of the uncertainty of physical measurements, chemical analyses and calibrations. The software uses three different methods to calculate the measurement uncertainty. GUF Method for linear models, this method is applied to linear and quasi-linear models and corresponds to the GUM Uncertainty Framework. The software calculates the partial derivatives (the first term of a Taylor series) to determine the sensitivity coefficients of the equivalent linear model and then calculates the combined standard uncertainty in accordance with the Gaussian error propagation law. GUF Method for nonlinear models, this method is provided for nonlinear models with the symmetric distribution of the result quantities. In this method, a series of numerical methods are used, e.g. nonlinear sensitivity analysis, second and third-order sensitivity indices, quasi-Monte Carlo with Sobol sequences. -
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BASIC
BASIC
BASIC (Beginners' All-purpose Symbolic Instruction Code) is a family of general-purpose, high-level programming languages designed for ease of use. Initially, BASIC concentrated on supporting straightforward mathematical work, with matrix arithmetic support from its initial implementation as a batch language, and character string functionality being added by 1965. The emergence of BASIC took place as part of a wider movement towards time-sharing systems. Some dialects of BASIC supported matrices and matrix operations, which can be used to solve sets of simultaneous linear algebraic equations. These dialects would directly support matrix operations such as assignment, addition, multiplication (of compatible matrix types), and evaluation of a determinant. BASIC declined in popularity in the 1990s, as more powerful microcomputers came to market and programming languages with advanced features (such as Pascal and C) became tenable on such computers. -
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XLfit
IDBS
Industry-standard models built-in with support for designing and sharing your own models. XLfit® is a Microsoft® Excel add-in for Windows that brings the power of scientific mathematics and statistics to Excel, together with supporting charting capabilities. XLfit is the leading statistical and curve fitting package for Excel and is used by the world’s leading pharmaceutical, chemical, engineering industries, and research institutions and is validated by the National Physical Laboratory (NPL). There are over 70 out-of-the-box models for both linear and nonlinear curve fitting available in XLfit, including all commonly used models for describing data from drug discovery-related experiments. An unlimited number of custom or user-defined models may be added. Linear and nonlinear modelling, interactive 3D and 2D charting, automatic and interactive point knock out. Features every scientist needs out of the box. -
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ndCurveMaster
SigmaLab Tomas Cepowski
ndCurveMaster is a specialized software designed for multivariable curve fitting. It automatically applies nonlinear regression equations to your datasets, which can consist of observed or measured values. The software supports curve and surface fitting in 2D, 3D, 4D, 5D, ..., nD dimensions. This means that no matter how complex your data is or how many variables it has, ndCurveMaster can handle it with ease. For example, ndCurveMaster can efficiently derive an optimal equation for a dataset with six inputs (x1 to x6) and an output Y, such as: Y = a0 + a1 · exp(x1)^-0.5 + a2 · ln(x2)^8 + ... + a6 · x6^5.2, to accurately match measured values. Utilizing machine learning numerical methods, ndCurveMaster automatically fits the most suitable nonlinear regression functions to your dataset and discovers the relationships between the inputs and output. This robust tool offers linear, polynomial, and nonlinear curve fitting, utilizes crucial validation and goodness-of-fit tests.Starting Price: €289 -
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MXNet
The Apache Software Foundation
A hybrid front-end seamlessly transitions between Gluon eager imperative mode and symbolic mode to provide both flexibility and speed. Scalable distributed training and performance optimization in research and production is enabled by the dual parameter server and Horovod support. Deep integration into Python and support for Scala, Julia, Clojure, Java, C++, R and Perl. A thriving ecosystem of tools and libraries extends MXNet and enables use-cases in computer vision, NLP, time series and more. Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision-making process have stabilized in a manner consistent with other successful ASF projects. Join the MXNet scientific community to contribute, learn, and get answers to your questions. -
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SHARK
SHARK
SHARK is a fast, modular, feature-rich open-source C++ machine learning library. It provides methods for linear and nonlinear optimization, kernel-based learning algorithms, neural networks, and various other machine learning techniques. It serves as a powerful toolbox for real-world applications as well as research. Shark depends on Boost and CMake. It is compatible with Windows, Solaris, MacOS X, and Linux. Shark is licensed under the permissive GNU Lesser General Public License. Shark provides an excellent trade-off between flexibility and ease-of-use on the one hand, and computational efficiency on the other. Shark offers numerous algorithms from various machine learning and computational intelligence domains in a way that they can be easily combined and extended. Shark comes with a lot of powerful algorithms that are to our best knowledge not implemented in any other library. -
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Solver SDK
Frontline Systems
Use optimization and simulation models in your desktop, Web or mobile application. Use the same high-level objects (like Problem, Solver, Variable and Function), collections, properties and methods across different programming languages. The same object-oriented API is exposed "over the wire" through Web Services WS-* standards to remote clients in PHP, JavaScript, C# and other languages. Procedural languages can use conventional calls that correspond naturally to the properties and methods of the Object-Oriented API. Linear and quadratic programming, mixed-integer programming, smooth nonlinear optimization, global optimization, and non-smooth evolutionary and tabu search are all included. The world's best optimizers, from Gurobi™, XPRESS™ and MOSEK™ for linear, quadratic and conic models to KNITRO™, SQP and GRG methods for nonlinear models "plug into" Solver SDK. Easily create a sparse DoubleMatrix object with 1 million rows and columns.Starting Price: $2495 one-time payment -
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NXG Logic Explorer
NXG Logic
NXG Logic Explorer is a Windows-based machine learning package designed for data analytics, predictive analytics, unsupervised class discovery, supervised class prediction, and simulation. It enhances productivity by reducing the time required for various procedures, enabling users to identify novel patterns in exploratory datasets and perform hypothesis testing, simulations, and text mining to extract meaningful insights. Key features include automatic de-stringing of messy Excel input files, parallel feature analysis for generating summary statistics, Shapiro-Wilk tests, histograms, and count frequencies for multiple continuous and categorical variables. It allows simultaneous execution of ANOVA, Welch ANOVA, chi-squared, and Bartlett's tests on multiple variables, and automatically generates multivariable linear, logistic, and Cox proportional hazards regression models based on a default p-value criterion for filtering from univariate models. -
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Orange
University of Ljubljana
Open source machine learning and data visualization. Build data analysis workflows visually, with a large, diverse toolbox. Perform simple data analysis with clever data visualization. Explore statistical distributions, box plots and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, MDS and linear projections. Even your multidimensional data can become sensible in 2D, especially with clever attribute ranking and selections. Interactive data exploration for rapid qualitative analysis with clean visualizations. Graphic user interface allows you to focus on exploratory data analysis instead of coding, while clever defaults make fast prototyping of a data analysis workflow extremely easy. Place widgets on the canvas, connect them, load your datasets and harvest the insight! When teaching data mining, we like to illustrate rather than only explain. And Orange is great at that. -
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NVIDIA Modulus
NVIDIA
NVIDIA Modulus is a neural network framework that blends the power of physics in the form of governing partial differential equations (PDEs) with data to build high-fidelity, parameterized surrogate models with near-real-time latency. Whether you’re looking to get started with AI-driven physics problems or designing digital twin models for complex non-linear, multi-physics systems, NVIDIA Modulus can support your work. Offers building blocks for developing physics machine learning surrogate models that combine both physics and data. The framework is generalizable to different domains and use cases—from engineering simulations to life sciences and from forward simulations to inverse/data assimilation problems. Provides parameterized system representation that solves for multiple scenarios in near real time, letting you train once offline to infer in real time repeatedly. -
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AMPL
AMPL
AMPL is a powerful and intuitive modeling language designed to represent and solve complex optimization problems. It enables users to formulate mathematical models in a syntax that closely mirrors algebraic notation, facilitating a clear and concise representation of variables, objectives, and constraints. AMPL supports a wide range of problem types, including linear programming, nonlinear programming, mixed-integer programming, and more. One of its key strengths is the ability to separate models and data, allowing for flexibility and scalability in handling large-scale problems. The platform offers seamless integration with numerous solvers, both commercial and open-source, providing users with the flexibility to choose the most appropriate solver for their specific needs. AMPL is available across multiple operating systems, including Windows, macOS, and Linux, and offers various licensing options.Starting Price: $3,000 per year -
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Build your deep learning project quickly on Google Cloud: Quickly prototype with a portable and consistent environment for developing, testing, and deploying your AI applications with Deep Learning Containers. These Docker images use popular frameworks and are performance optimized, compatibility tested, and ready to deploy. Deep Learning Containers provide a consistent environment across Google Cloud services, making it easy to scale in the cloud or shift from on-premises. You have the flexibility to deploy on Google Kubernetes Engine (GKE), AI Platform, Cloud Run, Compute Engine, Kubernetes, and Docker Swarm.
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MeltPlan
MeltPlan
MeltPlan is a preconstruction-technology company focused on transforming some of the most tedious and time-consuming workflows in the built environment through deeply construction-aware AI. The company is building a unified platform, currently centered on solving two core problems with Melt Code and Melt Takeoff. Each solution addresses critical bottlenecks that slow down architects, engineers, and contractors throughout design and preconstruction, where decisions have the greatest impact on cost, feasibility, and overall project outcomes. The company’s flagship product, Melt Code, is an AI-powered building code research and compliance assistant designed to eliminate the hours that professionals spend flipping through code books or navigating fragmented, jurisdiction-specific websites. -
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QC Ware Forge
QC Ware
Unique and efficient turn-key algorithms for data scientists. Powerful circuit building blocks for quantum engineers. Turn-key algorithm implementations for data scientists, financial analysts, and engineers. Explore problems in binary optimization, machine learning, linear algebra, and monte carlo sampling on simulators and real quantum hardware. No prior experience with quantum computing is required. Use NISQ data loader circuits to load classical data into quantum states to use with your algorithms. Use circuit building blocks for linear algebra with distance estimation and matrix multiplication circuits. Use our circuit building blocks to create your own algorithms. Get a significant performance boost for D-Wave hardware and use the latest improvements for gate-based approaches. Try out quantum data loaders and algorithms with guaranteed speed-ups on clustering, classification, and regression.Starting Price: $2,500 per hour -
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Neural Designer
Artelnics
Neural Designer is a powerful software tool for developing and deploying machine learning models. It provides a user-friendly interface that allows users to build, train, and evaluate neural networks without requiring extensive programming knowledge. With a wide range of features and algorithms, Neural Designer simplifies the entire machine learning workflow, from data preprocessing to model optimization. In addition, it supports various data types, including numerical, categorical, and text, making it versatile for domains. Additionally, Neural Designer offers automatic model selection and hyperparameter optimization, enabling users to find the best model for their data with minimal effort. Finally, its intuitive visualizations and comprehensive reports facilitate interpreting and understanding the model's performance.Starting Price: $2495/year (per user) -
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Neural Magic
Neural Magic
GPUs bring data in and out quickly, but have little locality of reference because of their small caches. They are geared towards applying a lot of compute to little data, not little compute to a lot of data. The networks designed to run on them therefore execute full layer after full layer in order to saturate their computational pipeline (see Figure 1 below). In order to deal with large models, given their small memory size (tens of gigabytes), GPUs are grouped together and models are distributed across them, creating a complex and painful software stack, complicated by the need to deal with many levels of communication and synchronization among separate machines. CPUs, on the other hand, have large, much faster caches than GPUs, and have an abundance of memory (terabytes). A typical CPU server can have memory equivalent to tens or even hundreds of GPUs. CPUs are perfect for a brain-like ML world in which parts of an extremely large network are executed piecemeal, as needed. -
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Deep learning frameworks such as TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have contributed to the popularity of deep learning by reducing the effort and skills needed to design, train, and use deep learning models. Fabric for Deep Learning (FfDL, pronounced “fiddle”) provides a consistent way to run these deep-learning frameworks as a service on Kubernetes. The FfDL platform uses a microservices architecture to reduce coupling between components, keep each component simple and as stateless as possible, isolate component failures, and allow each component to be developed, tested, deployed, scaled, and upgraded independently. Leveraging the power of Kubernetes, FfDL provides a scalable, resilient, and fault-tolerant deep-learning framework. The platform uses a distribution and orchestration layer that facilitates learning from a large amount of data in a reasonable amount of time across compute nodes.
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MATLAB
The MathWorks
MATLAB® combines a desktop environment tuned for iterative analysis and design processes with a programming language that expresses matrix and array mathematics directly. It includes the Live Editor for creating scripts that combine code, output, and formatted text in an executable notebook. MATLAB toolboxes are professionally developed, rigorously tested, and fully documented. MATLAB apps let you see how different algorithms work with your data. Iterate until you’ve got the results you want, then automatically generate a MATLAB program to reproduce or automate your work. Scale your analyses to run on clusters, GPUs, and clouds with only minor code changes. There’s no need to rewrite your code or learn big data programming and out-of-memory techniques. Automatically convert MATLAB algorithms to C/C++, HDL, and CUDA code to run on your embedded processor or FPGA/ASIC. MATLAB works with Simulink to support Model-Based Design. -
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Zebra by Mipsology
Mipsology
Zebra by Mipsology is the ideal Deep Learning compute engine for neural network inference. Zebra seamlessly replaces or complements CPUs/GPUs, allowing any neural network to compute faster, with lower power consumption, at a lower cost. Zebra deploys swiftly, seamlessly, and painlessly without knowledge of underlying hardware technology, use of specific compilation tools, or changes to the neural network, the training, the framework, and the application. Zebra computes neural networks at world-class speed, setting a new standard for performance. Zebra runs on highest-throughput boards all the way to the smallest boards. The scaling provides the required throughput, in data centers, at the edge, or in the cloud. Zebra accelerates any neural network, including user-defined neural networks. Zebra processes the same CPU/GPU-based trained neural network with the same accuracy without any change. -
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Scilab
Scilab Enterprises
Numerical analysis or Scientific computing is the study of approximation techniques for numerically solving mathematical problems. Scilab provides graphics functions to visualize, annotate and export data and offers many ways to create and customize various types of plots and charts. Scilab is a high level programming language for scientific programming. It enables a rapid prototyping of algorithms, without having to deal with the complexity of other more low level programming language such as C and Fortran (memory management, variable definition). This is natively handled by Scilab, which results in a few lines of code for complex mathematical operations, where other languages would require much longer codes. It also comes with advanced data structure such as polynomials, matrices and graphic handles and provides an easily operable development environment. -
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Keel
Keel
KEEL (Knowledge Extraction based on Evolutionary Learning) is an open source (GPLv3) Java software tool that can be used for a large number of different knowledge data discovery tasks. KEEL provides a simple GUI based on data flow to design experiments with different datasets and computational intelligence algorithms (paying special attention to evolutionary algorithms) in order to assess the behavior of the algorithms. It contains a wide variety of classical knowledge extraction algorithms, preprocessing techniques (training set selection, feature selection, discretization, imputation methods for missing values, among others), computational intelligence based learning algorithms, hybrid models, statistical methodologies for contrasting experiments and so forth. It allows to perform a complete analysis of new computational intelligence proposals in comparison to existing ones. Moreover, KEEL has been designed with a two-fold goal: research and education. -
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Keras
Keras
Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides. Keras is the most used deep learning framework among top-5 winning teams on Kaggle. Because Keras makes it easier to run new experiments, it empowers you to try more ideas than your competition, faster. And this is how you win. Built on top of TensorFlow 2.0, Keras is an industry-strength framework that can scale to large clusters of GPUs or an entire TPU pod. It's not only possible; it's easy. Take advantage of the full deployment capabilities of the TensorFlow platform. You can export Keras models to JavaScript to run directly in the browser, to TF Lite to run on iOS, Android, and embedded devices. It's also easy to serve Keras models as via a web API. -
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PureScript
PureScript
PureScript is a strongly typed, purely functional programming language that compiles JavaScript. It enables developers to build robust web applications, web servers, and mobile apps using functional programming techniques. PureScript offers features such as algebraic data types, pattern matching, row polymorphism, extensible records, higher-kinded types, type classes with functional dependencies, and higher-rank polymorphism. The language emphasizes strong static typing and pure functions, ensuring code reliability and maintainability. Developers can compile PureScript code into readable JavaScript, facilitating seamless integration with existing JavaScript codebases. The ecosystem includes an extensive collection of libraries, excellent tooling, and editor support with instant rebuilds. An active community provides numerous learning resources, including the PureScript book, which offers practical projects for beginners. -
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Automaton AI
Automaton AI
With Automaton AI’s ADVIT, create, manage and develop high-quality training data and DNN models all in one place. Optimize the data automatically and prepare it for each phase of the computer vision pipeline. Automate the data labeling processes and streamline data pipelines in-house. Manage the structured and unstructured video/image/text datasets in runtime and perform automatic functions that refine your data in preparation for each step of the deep learning pipeline. Upon accurate data labeling and QA, you can train your own model. DNN training needs hyperparameter tuning like batch size, learning, rate, etc. Optimize and transfer learning on trained models to increase accuracy. Post-training, take the model to production. ADVIT also does model versioning. Model development and accuracy parameters can be tracked in run-time. Increase the model accuracy with a pre-trained DNN model for auto-labeling. -
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MathPapa
MathPapa
We offer an algebra calculator to solve your algebra problems step by step, as well as lessons and practice to help you master algebra. Use our algebra calculator at home with the MathPapa website, or on the go with MathPapa mobile app. You can master algebra at your own pace and build a strong foundation of math knowledge. We will help you get there. Regular practice with our exercises will solidify your algebra skills. Reach your personal goals for mastering algebra. MathPapa can solve your equations (and show the work) and help you when you're stuck on your math homework. Solves linear equations and quadratic equations, and solves linear and quadratic inequalities. Graphs equations, factors quadratic expressions. Order of operations step-by-step. Evaluates expressions and solves systems of two equations. MathPapa's goal is to help you learn algebra step-by-step. Get help on your algebra problems with the MathPapa algebra calculator.Starting Price: $4.99 per month -
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RunMat
Dystr
RunMat (by Dystr) is a fast, free, open-source alternative for running MATLAB code. Users can run their existing MATLAB code with complete language grammar and core semantics. No license fees, no lock-in. RunMat is built with a modern compiler, which enables blazing-fast calculations. It boots in 5 milliseconds, GPU optimization is enabled by default, and it's a single, compact, cross-platform binary. Typical engineering use cases - Controls/signal processing & numerics: accelerate MATLAB-style loops plus heavy linear algebra; enjoy faster iteration due to instant startup and tiered JIT. - Batch/CI & serverless jobs: snapshots + compact binaries make it easy to run .m workloads in containers or ephemeral runners at scale. - Plot-heavy workflows: interactive GPU plots for exploratory analysis and reportable exports for stakeholders. - Education: remove license friction and start labs instantly; Jupyter kernel supports reproducible worksheets.Starting Price: $0 -
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LiveLink for MATLAB
Comsol Group
Seamlessly integrate COMSOL Multiphysics® with MATLAB® to extend your modeling with scripting programming in the MATLAB environment. LiveLink™ for MATLAB® allows you to utilize the full power of MATLAB and its toolboxes in preprocessing, model manipulation, and postprocessing. Enhance your in-house MATLAB code with powerful multiphysics simulations. Base your geometry modeling on probabilistic or image data. Use multiphysics models together with Monte Carlo simulations and genetic algorithms. Export COMSOL models on state-space matrix format for incorporating into control systems. Interface in the COMSOL Desktop® environment enables the use of MATLAB® functions while modeling. Manipulate your models from the command line or script to parameterize the geometry, physics, or the solution scheme. -
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Apache Mahout
Apache Software Foundation
Apache Mahout is a powerful, scalable, and versatile machine learning library designed for distributed data processing. It offers a comprehensive set of algorithms for various tasks, including classification, clustering, recommendation, and pattern mining. Built on top of the Apache Hadoop ecosystem, Mahout leverages MapReduce and Spark to enable data processing on large-scale datasets. Apache Mahout(TM) is a distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms. Apache Spark is the recommended out-of-the-box distributed back-end or can be extended to other distributed backends. Matrix computations are a fundamental part of many scientific and engineering applications, including machine learning, computer vision, and data analysis. Apache Mahout is designed to handle large-scale data processing by leveraging the power of Hadoop and Spark. -
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JCov
OpenJDK
The JCov open-source project is used to gather quality metrics associated with the production of test suites. JCov is being opened in order to facilitate the practice of verifying test execution of regression tests in OpenJDK development. The main motivation behind JCov is the transparency of test coverage metrics. The advantage to promoting standard coverage based on JCov is that OpenJDK developers will be able to use a code coverage tool that stays in the 'lock step' with Java language and VM developments. JCov is a pure java implementation of a code coverage tool that provides a means to measure and analyze dynamic code coverage of Java programs. JCov provides functionality to collect method, linear block, and branch coverage, as well as show uncovered execution paths. It is also able to show a program's source code annotated with coverage information. From a testing perspective, JCov is most useful to determine execution paths.Starting Price: Free -
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JupyterLab
Jupyter
Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. JupyterLab is a web-based interactive development environment for Jupyter notebooks, code, and data. JupyterLab is flexible, configure and arrange the user interface to support a wide range of workflows in data science, scientific computing, and machine learning. JupyterLab is extensible and modular, write plugins that add new components and integrate with existing ones. The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include, data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. Jupyter supports over 40 programming languages, including Python, R, Julia, and Scala. -
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Calculix
Calculix
With CalculiX finite element models can be built, calculated, and post-processed. The pre-and post-processor is an interactive 3D tool using the OpenGL API. The solver is able to do linear and non-linear calculations. Static, dynamic, and thermal solutions are available. Because the solver makes use of the abaqus input format it is possible to use commercial pre-processors as well. In turn the pre-processor can write mesh-related data for nastran, abaqus, ansys, code-aster, and for the free-cfd codes dolfyn, duns, ISAAC and OpenFOAM. A simple step reader is included. In addition, external CAD interfaces are available. The program is designed to run on Unix platforms like Linux and Irix computers but also on MS Windows. -
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ChemStat
Starpoint Software
ChemStat is the easiest and fastest application available for the statistical analysis of ground water monitoring data at RCRA facilities. ChemStat includes most statistical analysis methods described in the 1989 and 1992 USEPA statistical analysis documents, USEPA Draft Unified Guidance Document, U.S. Navy Statistical Analysis Guidance document, and other guidance documents and methods documented in popular statistical texts. A unique combination of ease-of-use and innovative technologies make ChemStat the value leader for environmental statistical analysis. Data set size is limited only by computer memory for most tests. An unlimited number of parameters. An unlimited number of wells. An unlimited number of sample dates. Unlimited parameter name and well label length. Easily exclude individual data points from analyses.Starting Price: $990.00 -
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NVIDIA DIGITS
NVIDIA DIGITS
The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. DIGITS is completely interactive so that data scientists can focus on designing and training networks rather than programming and debugging. Interactively train models using TensorFlow and visualize model architecture using TensorBoard. Integrate custom plug-ins for importing special data formats such as DICOM used in medical imaging. -
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Stata
StataCorp LLC
Stata delivers everything you need for reproducible data analysis—powerful statistics, visualization, data manipulation, and automated reporting—all in one intuitive platform. Stata is fast and accurate. It is easy to learn through the extensive graphical interface yet completely programmable. With Stata's menus and dialogs, you get the best of both worlds. You can easily point and click or drag and drop your way to all of Stata's statistical, graphical, and data management features. Use Stata's intuitive command syntax to quickly execute commands. Whether you enter commands directly or use the menus and dialogs, you can create a log of all actions and their results to ensure the reproducibility and integrity of your analysis. Stata also has complete command-line scripting and programming facilities, including a full matrix programming language. You have access to everything you need to script your analysis or even to create new Stata commands.Starting Price: $48.00/6-month/student -
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MCM Alchimia
Alchimia Software
This is the latest release of the freeware application MCM Alchimia, which was developed specifically for estimating uncertainty of measurement and calibrations by Monte Carlo method consistent with JCGM 101. This release adds a complete GUM framework uncertainty budget, and, like the preceding version have support on correlated quantities and regression curves. Speed increase comparable to popular calculation and statistics software. Simulation in direct, inverse and total least squares. Custom application language through the external module. Output report with exhaustive statistical study of the simulation. -
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CVXOPT
CVXOPT
CVXOPT is a free software package for convex optimization based on the Python programming language. It can be used with the interactive Python interpreter, on the command line by executing Python scripts, or integrated in other software via Python extension modules. Its main purpose is to make the development of software for convex optimization applications straightforward by building on Python’s extensive standard library and on the strengths of Python as a high-level programming language. Efficient Python classes for dense and sparse matrices (real and complex), with Python indexing and slicing and overloaded operations for matrix arithmetic. Interfaces to the linear programming solver in GLPK, the semidefinite programming solver in DSDP5, and the linear, quadratic and second-order cone programming solvers in MOSEK.Starting Price: Free -
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Get free access to powerful SAS software for statistical analysis, data mining, and forecasting. Point-and-click functionality means there's no need to program. Use the most up-to-date statistical and quantitative methods whenever and wherever you are. With SAS OnDemand for Academics, you get the same world-class analytics software used by more than 82,000 business, government, and university sites around the world – including 100% of Fortune 500 companies in commercial and retail banking, health insurance, pharmaceuticals, aerospace manufacturing, ecommerce, and computer services. Whether you're a professor, teacher, student, or independent learner, you can get easy access to powerful SAS software via the cloud. Setup is easy, too. After you get set up, a broadband internet connection is all you'll need to run the best analytics software in the world. Connect with fellow SAS users to ask questions, share ideas and best practices, collaborate on projects, and get peer support.
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Deci
Deci AI
Easily build, optimize, and deploy fast & accurate models with Deci’s deep learning development platform powered by Neural Architecture Search. Instantly achieve accuracy & runtime performance that outperform SoTA models for any use case and inference hardware. Reach production faster with automated tools. No more endless iterations and dozens of different libraries. Enable new use cases on resource-constrained devices or cut up to 80% of your cloud compute costs. Automatically find accurate & fast architectures tailored for your application, hardware and performance targets with Deci’s NAS based AutoNAC engine. Automatically compile and quantize your models using best-of-breed compilers and quickly evaluate different production settings. Automatically compile and quantize your models using best-of-breed compilers and quickly evaluate different production settings. -
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Terrastation II
TERRASCIENCES
Provides data import and export capabilities, including LIS, DLIS, LAS, ASCII formats. Interactive data editing, depth shifting, curve splicing, along with many useful curve manipulation options. Full deterministic analysis including most Vshale, Porosity, and water saturation equations. A set of statistical analysis options including cluster analysis, Fourier analysis, multiple linear regression and more. Speed corrections, button correlation, pad/flap correlation, swing-arm, dead/faulty button, and more. Quality control plots of magnetometers and acceleration information. Build templates that are unique for each well in the section, allowing all down hole data including borehole image data to be displayed in section. -
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NumPy
NumPy
Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.Starting Price: Free -
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DeepCube
DeepCube
DeepCube focuses on the research and development of deep learning technologies that result in improved real-world deployment of AI systems. The company’s numerous patented innovations include methods for faster and more accurate training of deep learning models and drastically improved inference performance. DeepCube’s proprietary framework can be deployed on top of any existing hardware in both datacenters and edge devices, resulting in over 10x speed improvement and memory reduction. DeepCube provides the only technology that allows efficient deployment of deep learning models on intelligent edge devices. After the deep learning training phase, the resulting model typically requires huge amounts of processing and consumes lots of memory. Due to the significant amount of memory and processing requirements, today’s deep learning deployments are limited mostly to the cloud. -
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EViews
S&P Global
With an intuitive interface and one of the largest sets of data management tools available, this econometric modeling software helps you quickly and efficiently create statistical and forecasting equations. Benefit from best-in-class features, including 64-bit Windows large memory support, object linking and embedding (OLE) and smart edit windows. Rapidly analyze time series, cross-section and longitudinal data. Streamline statistical and econometric modeling. Produce presentation-quality graphs and tables. Conduct superior budgeting, strategic planning and academic research. Context-sensitive menus. Batch programming language. Tools to for add-ins or user objects. Full command line support. Drag-and-drop functionality. Generate forecasts and model simulations. Produce high-quality graphs and tables for publication or inclusion in other applications. EViews 12 offers more of the power and ease-of-use that you've come to expect.Starting Price: $610 one-time payment -
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GraphPad InStat
GraphPad Software
Most statistics programs are designed by statisticians, for statisticians. These programs are feature-packed and powerful, but can overwhelm scientists with thick manuals, obscure statistical jargon and high prices. GraphPad InStat is different. InStat is designed by a scientist for scientists. With InStat, even a statistical novice can analyze data in just a few minutes. Try InStat for statistics without all the fuss. InStat conquers the learning curve by escorting you through statistical analyses. You'll master the program in just a few minutes. You don't have to know the name of the test you need. InStat helps you pick an appropriate test by asking questions about your data. If you are unsure, consult the extensive help screens, which explain the statistical reasoning in plain language. InStat does not assume that you are a statistics whiz. It presents results in simple paragraphs, with a minimum of statistical jargon. InStat's help screens review the use of each test.
