Showing 3 open source projects for "state-thread"

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  • 1
    forecast

    forecast

    Forecasting Functions for Time Series and Linear Models

    ...ARIMA, exponential smoothing, etc.), tools for automatic model selection, diagnostics, plotting, forecasting future values, etc. It's widely used in statistics, economics, business forecasting, environmental science, etc. Exponential smoothing state space models (ETS) including seasonal components. Residual checks, model accuracy, plots, forecast error measures etc.
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  • 2
    gganimate

    gganimate

    A Grammar of Animated Graphics

    ...Here we take a simple boxplot of fuel consumption as a function of cylinders and let it transition between the number of gears available in the cars. As this is a discrete split (gear being best described as an ordered factor) we use transition_states and provide a relative length to use for transition and state view. As not all combinations of data are present there are states missing a box. We define that when a box appears it should fade into view, whereas it should shrink away when it disappears. Lastly, we decide to use a sinusoidal easing for all our aesthetics (here, only y is changing) gganimate is available on CRAN and can be installed with install.packages('gganimate').
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  • 3
    ProgrammingAssignment2

    ProgrammingAssignment2

    Repository for Programming Assignment 2 for R Programming on Coursera

    ...The assignment walks you through creating a special matrix object that stores both a matrix and its cached inverse, avoiding repeated calls to costly operations. It builds on a worked example that caches the mean of a numeric vector, demonstrating how the operator preserves state across function calls. You then implement analogous logic for matrices via two functions, one to construct the cache-aware object and another to compute or retrieve the cached inverse. The instructions emphasize using solve for inversion and assuming that the supplied matrix is always invertible. The repository outlines the workflow for forking, editing the provided R stub, committing your solution, and submitting your repository URL as the final deliverable.
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