Reconstruct birth-year specific probabilities of immune imprinting to influenza A, using the methods of Gostic et al. (2016) <doi:10.1126/science.aag1322>. Plot, save, or export the calculated probabilities for use in your own research. By default, the package calculates subtype-specific imprinting probabilities, but with user-provided frequency data, it is possible to calculate probabilities for arbitrary kinds of primary exposure to influenza A, including primary vaccination and exposure to specific clades, strains, etc.

A variety of improved shrinkage estimators in the area of statistical analysis: unrestricted; restricted; preliminary test; improved preliminary test; Stein; and positive-rule Stein. More details can be found in chapter 7 of Saleh, A. K. Md. E. (2006) <ISBN: 978-0-471-56375-4>.

Imputes missing glucose values in repeated-measures continuous
    glucose monitoring (CGM) data. Workflows create time-series features from
    raw timestamps, support model selection, and return the user's original
    columns plus an imputed glucose column. Methods include multiple imputation
    by chained equations using 'mice' (Azur et al. (2011)
    <doi:10.1002/mpr.329>), Random Forest regression using 'ranger'
    (Breiman (2001) <doi:10.1023/A:1010933404324>),
    k-nearest-neighbor regression using 'FNN' (Zhang (2016)
    <doi:10.21037/atm.2016.03.37>), 'XGBoost' using 'xgboost' (Chen and
    Guestrin (2016) <doi:10.1145/2939672.2939785>), 'LightGBM' using
    'lightgbm' (Ke et al. (2017)
    <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>),
    and ARIMA forecasting using 'forecast' (Hyndman and Khandakar (2008)
    <doi:10.18637/jss.v027.i03>). A 'Python'-compatible backend uses
    'reticulate' to call 'pandas', 'scikit-learn', 'statsmodels', 'xgboost',
    and optional 'lightgbm'.

Missing values often occur in financial data due to a variety 
    of reasons (errors in the collection process or in the processing stage, 
    lack of asset liquidity, lack of reporting of funds, etc.). However, 
    most data analysis methods expect complete data and cannot be employed 
    with missing values. One convenient way to deal with this issue without 
    having to redesign the data analysis method is to impute the missing 
    values. This package provides an efficient way to impute the missing 
    values based on modeling the time series with a random walk or an 
    autoregressive (AR) model, convenient to model log-prices and log-volumes 
    in financial data. In the current version, the imputation is 
    univariate-based (so no asset correlation is used). In addition,
    outliers can be detected and removed.
    The package is based on the paper:
    J. Liu, S. Kumar, and D. P. Palomar (2019). Parameter Estimation of 
    Heavy-Tailed AR Model With Missing Data Via Stochastic EM. IEEE Trans. on 
    Signal Processing, vol. 67, no. 8, pp. 2159-2172. <doi:10.1109/TSP.2019.2899816>.