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.

Provides automated visual inference of residual plots using computer vision models, facilitating diagnostic checks for classical normal linear regression models.

Weather indices are formed from weather variables in this package. The users can input any number of weather variables recorded over any number of weeks. This package has no restriction on the number of weeks and weather variables to be taken as input.The details of the method can be seen (i)'Joint effects of weather variables on rice yields' by  R. Agrawal, R. C. Jain  and M. P. Jha in Mausam, vol. 34, pp. 189-194, 1983,<doi:10.54302/mausam.v34i2.2392>,(ii)'Improved weather indices based Bayesian regression model for forecasting crop yield' by  M. Yeasin, K. N. Singh, A. Lama and B. Gurung in Mausam, vol. 72, pp.879-886, 2021,<doi:10.54302/mausam.v72i4.670>.