krinsman
by krinsman
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Packages
| Name | Latest Version | Summary | Updated | License |
|---|
| jupyterlab-toc | 5.0.8 | — | Mar 25, 2025 | BSD 3-Clause |
| jupyterlab-variableinspector | v0.1.0 | — | Mar 25, 2025 | MIT |
| ijavascript | v5.0.20 | — | Mar 25, 2025 | — |
| jupyterlab-google-drive | v0.11.1 | — | Mar 25, 2025 | BSD 3-Clause |
| jupyterlab-latex | 0.4.0 | — | Mar 25, 2025 | — |
| jupyterlab-vim | v0.8.0 | — | Mar 25, 2025 | MIT |
| jupyterlab-github | v0.7.1 | — | Mar 25, 2025 | BSD 3-Clause |
| jupyterlab-flake8 | 0.2.1 | — | Mar 25, 2025 | None |
| jupyterlab_html | v0.1.0 | — | Mar 25, 2025 | BSD 3-Clause |
| r-rugarch | 1.4_0 | ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting. | Mar 25, 2025 | GPL-3 |
| jupyterlab_geojson-extension | 0.16.0 | — | Mar 25, 2025 | BSD 3-Clause |
| binaryornot | 0.4.4 | Ultra-lightweight pure Python package to check if a file is binary or text. | Mar 25, 2025 | BSD |
| jupyterlab-git | v0.1.0 | — | Mar 25, 2025 | BSD 3-Clause |
| jupyterlab-slurm | 0.1.0 | — | Mar 25, 2025 | BSD 3-Clause |
| jupyterlab-hub | v0.10.0 | — | Mar 25, 2025 | BSD 3-Clause |
| jupyterlab_katex-extension | v0.16.0 | — | Mar 25, 2025 | BSD 3-Clause |
| r-h2o | 3.18.0.8 | R scripting functionality for 'H2O', the open source math engine for big data that computes parallel distributed machine learning algorithms such as generalized linear models, gradient boosting machines, random forests, and neural networks (deep learning) within various cluster environments. | Mar 25, 2025 | Apache License (== 2.0) |
| jupyterlab_bokeh | v0.1.0 | — | Mar 25, 2025 | BSD 3-Clause |
| jupyterlab_fasta-extension | 0.16.0 | — | Mar 25, 2025 | BSD 3-Clause |
| r-pma | 1.1 | Performs Penalized Multivariate Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described in the following papers: (1) Witten, Tibshirani and Hastie (2009) A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10(3):515-534. (2) Witten and Tibshirani (2009) Extensions of sparse canonical correlation analysis, with applications to genomic data. Statistical Applications in Genetics and Molecular Biology 8(1): Article 28. | Mar 25, 2025 | GPL (>= 2) |
| r-runit | 0.4.31 | R functions implementing a standard Unit Testing framework, with additional code inspection and report generation tools. | Mar 25, 2025 | GPL-2 |
| r-tsdyn | 0.9_46 | Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006). | Mar 25, 2025 | GPL (>= 2) |
| r-mclust | 5.4 | Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference. | Mar 25, 2025 | GPL (>= 2) |
| r-distributionutils | 0.5_1 | This package contains utilities which are of use in the packages I have developed for dealing with distributions. Currently these packages are GeneralizedHyperbolic, VarianceGamma, and SkewHyperbolic and NormalLaplace. Each of these packages requires DistributionUtils. Functionality includes sample skewness and kurtosis, log-histogram, tail plots, moments by integration, changing the point about which a moment is calculated, functions for testing distributions using inversion tests and the Massart inequality. Also includes an implementation of the incomplete Bessel K function. | Mar 25, 2025 | GPL (>= 2) |
| r-spd | 2.0_1 | The Semi Parametric Piecewise Distribution blends the Generalized Pareto Distribution for the tails with a kernel based interior. | Mar 25, 2025 | GPL |