Showing 9 open source projects for "estimates"

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    ConformalPrediction.jl

    ConformalPrediction.jl

    Predictive Uncertainty Quantification through Conformal Prediction

    ...Conformal Prediction is easy-to-understand, easy-to-use and model-agnostic and it works under minimal distributional assumptions. Intuitively, CP works under the premise of turning heuristic notions of uncertainty into rigorous uncertainty estimates through repeated sampling or the use of dedicated calibration data.
    Downloads: 1 This Week
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  • 2
    Lasso.jl

    Lasso.jl

    Lasso/Elastic Net linear and generalized linear models

    Lasso.jl is a pure Julia implementation of the glmnet coordinate descent algorithm for fitting linear and generalized linear Lasso and Elastic Net models.
    Downloads: 1 This Week
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  • 3
    FiniteDifferences.jl

    FiniteDifferences.jl

    High accuracy derivatives, estimated via numerical finite differences

    FiniteDifferences.jl estimates derivatives with finite differences. See also the Python package FDM. FiniteDiff.jl and FiniteDifferences.jl are similar libraries: both calculate approximate derivatives numerically. You should definitely use one or the other, rather than the legacy Calculus.jl finite differencing, or reimplementing it yourself. At some point in the future, they might merge, or one might depend on the other.
    Downloads: 0 This Week
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  • 4
    see

    see

    Visualisation toolbox for beautiful and publication-ready figures

    see is an R package that serves as the visualization component of the easystats ecosystem, providing plotting utilities to produce publication-ready visualizations of statistical model parameters, diagnostics, predictions, and performance metrics. It works in conjunction with other easystats packages (such as parameters, performance, modelbased, bayestestR, etc.) to convert model outputs or summary objects into visual forms (dot-and-whisker plots, diagnostic plots, residual plots, etc.). It...
    Downloads: 1 This Week
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  • 5
    NannyML

    NannyML

    Detecting silent model failure. NannyML estimates performance

    NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, and interactive visualizations, is completely model-agnostic, and currently supports all tabular classification use cases. NannyML closes the loop with performance monitoring and post deployment data...
    Downloads: 0 This Week
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  • 6
    Cubature.jl

    Cubature.jl

    One- and multi-dimensional adaptive integration routines for Julia

    ...Adaptive integration works by evaluating the integrand at more and more points until the integrand converges to a specified tolerance (with the error estimated by comparing integral estimates with different numbers of points). The Cubature module implements two schemes for this adaptation: h-adaptivity (routines hquadrature, hcubature, hquadrature_v, and hcubature_v) and p-adaptivity (routines pquadrature, pcubature, pquadrature_v, and pcubature_v). The h- and p-adaptive routines accept the same parameters, so you can use them interchangeably, but they have very different convergence characteristics.
    Downloads: 1 This Week
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  • 7
    ImagingAnalysis

    ImagingAnalysis

    Direct tissue-level image quantification package for Mathematica

    ImagingAnalysis is a Mathematica package that performs grid-based analysis of time-lapse imaging data saved in a sequence of TIFF files. This package requires Mathematica 7.0. Revised on 14 May 2017: Bugs are fixed and incompatibility issues are resolved. The current version runs on Mathematica 11.
    Downloads: 0 This Week
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  • 8

    PRADA

    PRADA : Pipeline for RNA-Sequencing Data Analysis

    Massively parallel sequencing of cDNA reverse transcribed from RNA (RNASeq) provides an accurate estimate of the quantity and composition of mRNAs. To characterize the transcriptome through the analysis of RNA-seq data, we developed PRADA. PRADA focuses on the processing and analysis of gene expression estimates, supervised and unsupervised gene fusion identification, and supervised intragenic deletion identification. PRADA currently supports 7 modules to process and identify abnormalities from RNAseq data: preprocess: Generates aligned and recalibrated BAM files. expression: Generates gene expression (RPKM) and quality metrics. ...
    Downloads: 0 This Week
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  • 9

    SVG_plot

    Plot data in SVG format using C++ library

    ...It has been maintained and enhanced since then in Boost Sandbox, but is judged unsuitable for a Boost Library, so now being made more widely available here. Version 2 adds representation of uncertainty estimates as numbers and zones.
    Downloads: 1 This Week
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