Hospital machine learning and ai data analysis workflow tools, modeling, and automations. 
    This library provides many useful tools to review common administrative 
    hospital data. Some of these include predicting length of stay, and readmits.
    The aim is to provide a simple and consistent verb framework that takes the 
    guesswork out of everything.

Provides data for functions typically used in the 'healthyR' package.

Hospital time series data analysis workflow tools, modeling, and automations. 
    This library provides many useful tools to review common administrative time 
    series hospital data. Some of these include average length of stay, and 
    readmission rates. The aim is to provide a simple and consistent verb 
    framework that takes the guesswork out of everything.

Provides methods for correcting heaping (digit preference) in
    survey data at the individual record level. Age heaping, where respondents
    disproportionately report ages ending in 0 or 5, is a common phenomenon that
    can distort demographic analyses. Unlike traditional smoothing methods that
    only correct aggregated statistics, this package corrects individual values
    by replacing a calculated proportion of heaped observations with draws from
    fitted truncated distributions (log-normal, normal, or uniform). Supports
    5-year and 10-year heaping patterns, single heap correction, survey
    weights, and optional covariate-conditional (model-based) correction via
    quantile regression forests or linear models to preserve relationships. A
    multiple-imputation wrapper repeats the correction to propagate the added
    uncertainty into downstream inference.

Makes it easy to download a large number of files such as PDF files 
    and CSV files, while automatically slowing down requests, letting you know 
    where it is up to, and adjusting for files that have already been downloaded.

Given one or multiple paths to files produced by a PULSE multi-channel or a PULSE one-channel system (<https://electricblue.eu/pulse>) from a single experiment: [1] check pulse files for inconsistencies and read/merge all data, [2] split across time windows, [3] interpolate and smooth to optimize the dataset, [4] compute the heart rate frequency for each channel/window, and [5] facilitate quality control, summarising and plotting.
    Heart rate frequency is calculated using the Automatic Multi-scale Peak Detection algorithm proposed by Felix Scholkmann and team. For more details see Scholkmann et al (2012) <doi:10.3390/a5040588>. Check original code at <https://github.com/ig248/pyampd>.
    ElectricBlue is a non-profit technology transfer startup creating research-oriented solutions for the scientific community (<https://electricblue.eu>).