Automatic model selection for structural time series decomposition into trend, cycle, and seasonal components, plus optionality for structural interpolation, using the Kalman filter. 
  Koopman, Siem Jan and Marius Ooms (2012) "Forecasting Economic Time Series Using Unobserved Components Time Series Models" <doi:10.1093/oxfordhb/9780195398649.013.0006>.
  Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.

Build and train a variational autoencoder (VAE) for mixed-type
    tabular data (continuous, binary, categorical).
    Models are implemented using 'TensorFlow' and 'Keras' via the 'reticulate' 
    interface, enabling reproducible VAE training for heterogeneous tabular 
    datasets.

Provides simple and intuitive functions for basic statistical analyses. 
    Methods include the t-test (Student 1908 <doi:10.1093/biomet/6.1.1>),
    the Mann-Whitney U test (Mann and Whitney 1947 <doi:10.1214/aoms/1177730491>),
    Pearson's correlation (Pearson 1895 <doi:10.1098/rspl.1895.0041>),
    and analysis of variance (Fisher 1925, <doi:10.1007/978-1-4612-4380-9_5>).
    Functions are compatible with 'ggplot2' and 'dplyr'.

Algorithms for automatically finding appropriate thresholds
    for numerical data, with special functions for thresholding images.
    Provides the 'ImageJ' 'Auto Threshold' plugin functionality to R
    users. See <https://imagej.net/plugins/auto-threshold> and Landini et
    al.  (2017) <DOI:10.1111/jmi.12474>.

A novel parametrization of log transformation and a shift parameter to automate the transformation process are proposed in R package 'AutoTransQF' based on Feng et al. (2016). Please read Feng et al. (2016) <doi:10.1002/sta4.104> for more details of the method.

Offers a set of functions to easily make predictions for univariate time series. 
             'autoTS' is a wrapper of existing functions of the 'forecast' and 'prophet' packages, 
             harmonising their outputs in tidy dataframes and using default values for each.
             The core function getBestModel() allows the user to effortlessly benchmark seven 
             algorithms along with a bagged estimator to identify which one performs the best 
             for a given time series.