Journal of open source software最新文献

{"title":"lys: interactive multi-dimensional data analysis and\u0000visualization platform","authors":"Asuka Nakamura","doi":"10.21105/joss.05869","DOIUrl":"https://doi.org/10.21105/joss.05869","url":null,"abstract":"","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138972018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HW2D: A reference implementation of the\u0000Hasegawa-Wakatani model for plasma turbulence in fusion reactors","authors":"Robin Greif","doi":"10.21105/joss.05959","DOIUrl":"https://doi.org/10.21105/joss.05959","url":null,"abstract":"","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139006891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"pudu: A Python library for agnostic feature selection\u0000and explainability of Machine Learning spectroscopic problems","authors":"Enric Grau‐Luque, Ignacio Becerril‐Romero, Alejandro Perez-Rodriguez, M. Guc, V. Izquierdo‐Roca","doi":"10.21105/joss.05873","DOIUrl":"https://doi.org/10.21105/joss.05873","url":null,"abstract":"","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139006954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"libcdict: fast dictionaries in C","authors":"R. Izzard, D. Hendriks, Daniel P. Nemergut","doi":"10.21105/joss.04756","DOIUrl":"https://doi.org/10.21105/joss.04756","url":null,"abstract":"A common requirement in science is to store and share large sets of simulation data in an efficient, nested, flexible and human-readable way. Such datasets contain number counts and distributions, i","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139008408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GECo: A collection of solvers for the self-gravitating\u0000Vlasov equations","authors":"Ellery Ames, Anders Logg","doi":"10.21105/joss.05979","DOIUrl":"https://doi.org/10.21105/joss.05979","url":null,"abstract":"","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138981393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"archeoViz: an R package for the Visualisation,\u0000Exploration, and Web Communication of Archaeological Spatial\u0000Data","authors":"Sébastien Plutniak","doi":"10.21105/joss.05811","DOIUrl":"https://doi.org/10.21105/joss.05811","url":null,"abstract":"","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138978725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"dcTensor: An R package for discrete matrix/tensor\u0000decomposition","authors":"Koki Tsuyuzaki","doi":"10.21105/joss.05664","DOIUrl":"https://doi.org/10.21105/joss.05664","url":null,"abstract":"Matrix factorization (MF) is a widely used approach to extract significant patterns in a data matrix. MF is formalized as the approximation of a data matrix X by the matrix product of two factor matrices U and V. Because this formalization has a large number of degrees of freedom, some constraints are imposed on the solution. Non-negative matrix factorization (NMF) imposing a non-negative solution for the factor matrices is a widely used algorithm to decompose non-negative matrix data matrix. Due to the interpretability of its non-negativity and the convenience of using decomposition results as clustering, there are many applications of NMF in image processing, audio processing, and bioinformatics (Cichocki et al., 2009).","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85065395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"sptotal: an R package for predicting totals and weighted sums from spatial data.","authors":"Matt Higham, Jay Ver Hoef, Bryce Frank, Michael Dumelle","doi":"10.21105/joss.05363","DOIUrl":"10.21105/joss.05363","url":null,"abstract":"<p><p>In ecological or environmental surveys, it is often desired to predict the mean or total of a variable in some finite region. However, because of time and money constraints, sampling the entire region is often unfeasible. The purpose of the sptotal R package is to provide software that gives a prediction for a quantity of interest, such as a total, and an associated standard error for the prediction. The predictor, referred to as the Finite-Population-Block-Kriging (FPBK) predictor in the literature (J. M. Ver Hoef, 2008), incorporates possible spatial correlation in the data and also incorporates an appropriate variance reduction for sampling from a finite population. In the remainder of the paper, we give an overview of both the background of the method and of the sptotal package.</p>","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10494889/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10228388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CVtreeMLE: Efficient Estimation of Mixed Exposures using Data Adaptive Decision Trees and Cross-Validated Targeted Maximum Likelihood Estimation in R.","authors":"David McCoy,&nbsp;Alan Hubbard,&nbsp;Mark Van der Laan","doi":"10.21105/joss.04181","DOIUrl":"https://doi.org/10.21105/joss.04181","url":null,"abstract":"<p><p>Statistical causal inference of mixed exposures has been limited by reliance on parametric models and, until recently, by researchers considering only one exposure at a time, usually estimated as a beta coefficient in a generalized linear regression model (GLM). This independent assessment of exposures poorly estimates the joint impact of a collection of the same exposures in a realistic exposure setting. Marginal methods for mixture variable selection such as ridge/lasso regression are biased by linear assumptions and the interactions modeled are chosen by the user. Clustering methods such as principal component regression lose both interpretability and valid inference. Newer mixture methods such as quantile g-computation (Keil et al., 2020) are biased by linear/additive assumptions. More flexible methods such as Bayesian kernel machine regression (BKMR)(Bobb et al., 2014) are sensitive to the choice of tuning parameters, are computationally taxing and lack an interpretable and robust summary statistic of dose-response relationships. No methods currently exist which finds the best flexible model to adjust for covariates while applying a non-parametric model that targets for interactions in a mixture and delivers valid inference for a target parameter. Non-parametric methods such as decision trees are a useful tool to evaluate combined exposures by finding partitions in the joint-exposure (mixture) space that best explain the variance in an outcome. However, current methods using decision trees to assess statistical inference for interactions are biased and are prone to overfitting by using the full data to both identify nodes in the tree and make statistical inference given these nodes. Other methods have used an independent test set to derive inference which does not use the full data. The CVtreeMLE R package provides researchers in (bio)statistics, epidemiology, and environmental health sciences with access to state-of-the-art statistical methodology for evaluating the causal effects of a data-adaptively determined mixed exposure using decision trees. Our target audience are those analysts who would normally use a potentially biased GLM based model for a mixed exposure. Instead, we hope to provide users with a non-parametric statistical machine where users simply specify the exposures, covariates and outcome, CVtreeMLE then determines if a best fitting decision tree exists and delivers interpretable results.</p>","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312067/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10127945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-performance neural population dynamics modeling enabled by scalable computational infrastructure.","authors":"Aashish N Patel,&nbsp;Andrew R Sedler,&nbsp;Jingya Huang,&nbsp;Chethan Pandarinath,&nbsp;Vikash Gilja","doi":"10.21105/joss.05023","DOIUrl":"https://doi.org/10.21105/joss.05023","url":null,"abstract":"Advances in neural interface technology are facilitating parallel, high-dimensional time series measurements of the brain in action. A powerful strategy for analyzing these measurements is to apply unsupervised learning techniques to uncover lower-dimensional latent dynamics that explain much of the variance in the high-dimensional measurements (Cunningham & Yu, 2014; Golub et al., 2018; Vyas et al., 2020). Latent factor analysis via dynamical systems (LFADS) (Pandarinath et al., 2018) provides a deep learning approach for extracting estimates of these latent dynamics from neural population data. The recently developed AutoLFADS framework (Keshtkaran et al., 2022) extends LFADS by using Population Based Training (PBT) (Jaderberg et al., 2017) to effectively and scalably tune model hyperparameters, a critical step for accurate modeling of neural population data. As hyperparameter sweeps are one of the most computationally demanding processes in model development, these workflows should be deployed in a computationally efficient and cost effective manner given the compute resources available (e.g., local, institutionally-supported, or commercial computing clusters). The initial implementation of AutoLFADS used the Ray library (Moritz et al., 2018) to enable support for specific local and commercial cloud workflows. We extend this support, by providing additional options for training AutoLFADS models using local clusters in a container-native approach (e.g., Docker,","PeriodicalId":16635,"journal":{"name":"Journal of open source software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10374446/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9922002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}