* Examples of Recursive Lists and Nested or Split Data Frames* (

**repurrrsive**)

Recursive lists in the form of R objects, ‘JSON’, and ‘XML’, for use in teaching and examples. Examples include color palettes, Game of Thrones characters, ‘GitHub’ users and repositories, and entities from the Star Wars universe. Data from the ‘gapminder’ package is also included, as a simple data frame and in nested and split forms.

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**Simpler Appearance Modification of ‘ggplot2’****ggconf**)

A flexible interface for ggplot2::theme(), potentially saving 50% of your typing.

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**Use Least Squares Polynomial Regression and Statistical Testing to Improve Savitzky-Golay****ADPF**)

This function takes a vector or matrix of data and smooths the data with an improved Savitzky Golay transform. The Savitzky-Golay method for data smoothing and differentiation calculates convolution weights using Gram polynomials that exactly reproduce the results of least-squares polynomial regression. Use of the Savitzky-Golay method requires specification of both filter length and polynomial degree to calculate convolution weights. For maximum smoothing of statistical noise in data, polynomials with low degrees are desirable, while a high polynomial degree is necessary for accurate reproduction of peaks in the data. Extension of the least-squares regression formalism with statistical testing of additional terms of polynomial degree to a heuristically chosen minimum for each data window leads to an adaptive-degree polynomial filter (ADPF). Based on noise reduction for data that consist of pure noise and on signal reproduction for data that is purely signal, ADPF performed nearly as well as the optimally chosen fixed-degree Savitzky-Golay filter and outperformed sub-optimally chosen Savitzky-Golay filters. For synthetic data consisting of noise and signal, ADPF outperformed both optimally chosen and sub-optimally chosen fixed-degree Savitzky-Golay filters. See Barak, P. (1995) <doi:10.1021/ac00113a006> for more information.

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**Tools to Work with the ‘Splash’ ‘JavaScript’ Rendering and Scraping Service****splashr**)

Splash’ <https://…/splash> is a ‘JavaScript’ rendering service. It is a lightweight web browser with an ‘HTTP’ API, implemented in ‘Python’ using ‘Twisted’ and ‘QT’ and provides some of the core functionality of the ‘RSelenium’ or ‘seleniumPipes’ R packages in a lightweight footprint. Some of ‘Splash’ features include the ability to process multiple web pages in parallel; retrieving ‘HTML’ results and/or take screen shots; disabling images or use ‘Adblock Plus’ rules to make rendering faster; executing custom ‘JavaScript’ in page context; getting detailed rendering info in ‘HAR’ format.

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**Parameter Inference and Optimal Designs for Grouped and/or Right-Censored Count Data****GRCdata**)

We implement two main functions. The first function uses a given grouped and/or right-censored grouping scheme and empirical data to infer parameters, and implements chi-square goodness-of-fit tests. The second function searches for the global optimal grouping scheme of grouped and/or right-censored count responses in surveys.