The public package registry npm is one of the biggest software registry. With its 216 911 software packages, it forms a big network of software dependencies. In this paper we evaluate various methods for finding similar packages in the npm network, using only the structure of the graph. Namely, we want to find a way of…
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48 results for “software packages”
Paper proposes algorithms for embedding directed networks with text associated nodes.
problem Learning embeddings for directed networks with text associated nodes.
method PCTADW-1 and PCTADW-2 neural network algorithms.
result Embeddings improve node classification quality on software package dependency networks.
fmeffects package interprets non-linear models in plain language.
problem Interpreting complex non-linear models.
method Forward marginal effects for model-agnostic explanations.
result First software implementation of forward marginal effects.
PyTorch Geometric Signed Directed fills the gap for GNNs on signed and directed graphs.
problem Lack of unified software packages for GNNs on signed and directed networks.
method Developed a software package with GNN models, synthetic and real-world data, and evaluation metrics.
result Demonstrates the effectiveness of the implemented methods through experiments.
Software package assesses spherical data distributions and clusters.
problem Assessing and clustering spherical data distributions.
method Innovative goodness-of-fit tests and clustering algorithms using kernel-based quadratic distances.
result Efficient and mathematically sound goodness-of-fit tests for spherical data.
Learning graphical models from data is an important problem with wide applications, ranging from genomics to the social sciences. Nowadays datasets often have upwards of thousands---sometimes tens or hundreds of thousands---of variables and far fewer samples. To meet this challenge, we have developed a new R package ca…
EnsembleSVM is a free software package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models. It currently offers ensemble methods based on binary SVM models. Our implementation avoids duplicate storage and evaluation of support vectors which are shared between constit…
Import2vec creates embeddings for software libraries to improve learning tasks.
problem Developing semantic representations for software libraries.
method Applied word embedding techniques from NLP to library packages.
result Library vectors capture meaningful relationships among libraries.
In this short article I introduce the knotR package, which creates two dimensional knot diagrams optimized for visual appearance using the R programming language. The knotR package is a systematic R-centric suite of software for the creation of production-quality artwork of knot diagrams, released under GPL2.
This paper introduces the R package sgmcmc; which can be used for Bayesian inference on problems with large datasets using stochastic gradient Markov chain Monte Carlo (SGMCMC). Traditional Markov chain Monte Carlo (MCMC) methods, such as Metropolis-Hastings, are known to run prohibitively slowly as the dataset size in…
We introduce the C++ application and R package ranger. The software is a fast implementation of random forests for high dimensional data. Ensembles of classification, regression and survival trees are supported. We describe the implementation, provide examples, validate the package with a reference implementation, and …
Background and objective: Stacking is an ensemble machine learning method that averages predictions from multiple other algorithms, such as generalized linear models and regression trees. An implementation of stacking, called super learning, has been developed as a general approach to supervised learning and has seen f…
Python package for projecting onto quadratic hypersurfaces.
problem Projections onto non-cylindrical central quadratic hypersurfaces.
method User-friendly Python package with documentation.
result Efficiently projects points onto quadratic hypersurfaces.
d3p package enables efficient Bayesian inference with differential privacy.
problem Efficiently performing Bayesian inference under differential privacy constraints.
method Differentially private variational inference for flexible probabilistic models.
result Achieves significant speed-up in runtime for complex models.
The R package psvmSDR: A Unified Algorithm for Sufficient Dimension Reduction via Principal Machinesstat.CO
R package psvmSDR simplifies SDR computation for machine learning.
problem Sufficient dimension reduction in machine learning.
method Principal machine (PM) generalized from PSVM.
result Efficient computation of SDR estimators in various scenarios.
KnotMosaics package simplifies knot theory computations in SageMath.
problem Efficiently computing knot mosaic diagrams and their properties.
method Developed a SageMath package for knot mosaic diagrams, implementing validation, strand tracing, and computation algorithms.
result Enabled easy computation of knot mosaic diagrams and their properties.
Software aids in estimating speciation parameters using trait-dependent models.
problem Lack of robust software to estimate numerous speciation parameters.
method Approximate Bayesian Computation approach implemented in R package pcmabc.
result Software allows estimation of parameters of trait-dependent speciation processes.
stCEG models spatial events using Chain Event Graphs in R.
problem Modeling events over spatial areas.
method Full specification of CEG models from data, interactive plots, web GUI.
result First software for CEGs with full model customisation.
Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvolstat.CO
R packages stochvol and factorstochvol simplify SV model estimation.
problem Efficient estimation of stochastic volatility models.
method Novel implementations of four SV models in R packages.
result Packages handle linear mean models, heavy-tailed SV, leverage, and multivariate SV.
MLJ offers a Julia package for composing machine learning models.
problem Complex model composition in machine learning.
method Flexible model composition tools and meta-algorithms.
result Clear benefits of Julia over multi-language alternatives.
HiPart offers an efficient, interactive tool for hierarchical clustering.
problem Efficient and interpretable hierarchical clustering for Big Data.
method Divisive hierarchical clustering algorithms with interactive visualizations.
result High computational efficiency and interpretability in Big Data applications.
arfpy simplifies data generation with adversarial random forests.
problem Synthesizing new data that matches given data.
method Adversarial Random Forests (ARF) integrated into a Python package.
result Effective and user-friendly data generation for various fields.
Bestvina and Handel have found an effective algorithm that determines whether a given homeomorphism of an orientable, possibly punctured surface is pseudo-Anosov. We present a software package in Java that realizes this algorithm for surfaces with one puncture. Moreover, the package allows the user to define homeomorph…
ERFit identifies dynamic equations from data with minimal supervision.
problem Data-driven sparse system identification in science and engineering.
method Entropic Regression method.
result ERFit package simplifies sparse system identification for various applications.
Python package for functional data analysis.
problem Handling and analysis of functional data.
method Comprehensive tools for representation, preprocessing, and exploratory analysis of functional data.
result Scikit-fda package provides a comprehensive set of tools for functional data analysis.
Software helps finance students construct optimal portfolios using VBA.
problem Finding the best portfolio of assets considering risk and return.
method Two methods: Markowitz and El-Khatib-Hatemi-J, both optimizing risk-adjusted return.
result Software constructs all possible portfolios and helps investors choose the best one.
theft package simplifies feature extraction for time series analysis in R.
problem Lack of a unified access point and methodological pipelines for feature-based time series analysis.
method theft package provides a unified framework for computing features from six open-source time series feature sets.
result theft enables comprehensive quantification and interpretation of time series structure.
A flexible Bayesian regression package for complex models.
problem Complex regression models and probabilistic predictions.
method Bayesian additive models with flexible components.
result Modular software for efficient Bayesian estimation.
reval package selects best clustering solutions via stability-based validation.
problem Challenges in determining best clustering solutions due to lack of validation methods.
method Stability-based relative clustering validation methods.
result Determines best clustering solutions that generalize to unseen data.
DADApy analyzes high-dimensional data manifolds in Python.
problem Analyzing complex, high-dimensional data.
method Estimating intrinsic dimension, density, clustering, comparing distance metrics.
result Effective analysis of data manifolds in Python.
Julia package for Gaussian processes offers fast, flexible, and scalable tools.
problem Modeling complex data sources in various sciences and industries.
method Utilizes Julia's computational benefits for fast, flexible, and user-friendly Gaussian processes.
result Fast, scalable, and user-friendly Gaussian processes package for Julia.
TomOpt: Differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomographyphysics.ins-det
TomOpt optimizes muon detector designs using differentiable programming.
problem Designing efficient particle detectors for muon tomography.
method Differentiable programming for muon interaction modeling, inference, and optimisation.
result Demonstrated end-to-end differentiable and inference-aware optimisation of particle physics instruments.
Unified R packages for forecast reconciliation of constrained series.
problem Improving accuracy and coherence of forecasts for linearly constrained multiple time series.
method Classical and machine learning-based linear reconciliation approaches for cross-sectional, temporal, and cross-temporal frameworks.
result Unified toolbox for forecast reconciliation in R.
FuncNN package enables deep learning with functional covariates.
problem Lack of software for deep learning with functional covariates.
method Developed an R package using keras architecture, introducing functions for model building, predictions, and cross-validation.
result First package for deep learning with functional covariates.
In (Franceschi et al., 2018) we proposed a unified mathematical framework, grounded on bilevel programming, that encompasses gradient-based hyperparameter optimization and meta-learning. We formulated an approximate version of the problem where the inner objective is solved iteratively, and gave sufficient conditions e…
ALT transforms time series data for better classification.
problem Efficiently classifying time series data with varying temporal scales.
method ALT algorithm using variable-length shifted time windows.
result State-of-the-art performance with minimal computational overhead.
RobPy offers robust statistical methods in Python.
problem Lack of robust statistical methods in Python.
method Built on NumPy, SciPy, and scikit-learn, RobPy includes robust tools for various statistical tasks.
result RobPy enables more users to perform robust data analysis in Python.
Software tool enhances sampling efficiency in neural networks.
problem Efficiently sampling from posterior distributions in machine learning models.
method Ensemble quasi-Newton preconditioning method for Langevin dynamics.
result Significant improvement in sampling efficiency demonstrated.
New GPU algorithm speeds up Gaussian Process analysis.
problem Reducing computational complexity for large spatial datasets.
method Implemented three GPU methods for Vecchia Approximation.
result New GPU method outperforms existing methods.
Auditor package assesses model performance and detects anomalies.
problem Model failure in real-world data.
method Model-agnostic audit techniques for goodness of fit and performance.
result Flexible and consistent grammar for validating models of any class.
Dynamic constructions of hyperbolisms of plane curves: an automated exploration of geometric locimath.AG
Automatically explores geometric loci of curves using software networking.
problem Exploring hyperbolisms and geometric loci of plane curves.
method Parametric equations, Groebner bases, and elimination for deriving polynomial equations.
result Derives new constructions of lemniscates and other geometric loci.
Following the seminal idea of Tukey, data depth is a function that measures how close an arbitrary point of the space is located to an implicitly defined center of a data cloud. Having undergone theoretical and computational developments, it is now employed in numerous applications with classification being the most po…
Python tool assesses European agricultural production resilience.
problem Estimating resilience of European agricultural production systems.
method Python software to compute annual production resilience indicator.
result Demonstrates resilience of European agricultural production.
HessFormer enables distributed Hessian computation for large models.
problem Limited Hessian computation for large deep learning models.
method Distributed stochastic Lanczos quadrature algorithm.
result Investigated Hessian spectral density of a 70 billion parameter model.
Kymatio simplifies scattering transforms for Python.
problem Signal processing and machine learning applications.
method Wavelet scattering transform implemented in Python.
result Efficient, GPU-accelerated implementation.
Causalfe estimates treatment effects in panel data with fixed effects.
problem Spurious heterogeneity in treatment effect estimates due to fixed effects in panel data.
method CFFE approach with node-level residualization during tree construction.
result Validates the estimator's performance through simulation studies.
New software package for scalable DPMM inference on large datasets.
problem Scalability and practical adoption of Dirichlet Process Mixture Models.
method Efficient distributed sampling-based inference on CPUs and GPUs.
result Significant speedups and fitting of larger datasets.
GeometricKernels package implements kernels for uncertain data on graphs, manifolds, and meshes.
problem Defining and computing kernels for structured data on graphs, manifolds, and meshes.
method Implementation of geometric analogs of Euclidean kernels (heat and Matérn) with automatic differentiation support.
result Ability to compute Fourier-feature-type expansions on geometric spaces.