Functionalities to automatically generate interactive visualizations for
             statistical results supported by 'ggfortify', such as time series, PCA,
             clustering and survival analysis, with 'plotly.js' <https://plotly.com/>  and
             'ggplot2' style. The generated visualizations can also be easily extended
             using 'ggplot2' and 'plotly' syntax while staying interactive.

The image of the amino acid transform on the protein level is drawn, and the automatic routing of the functional elements such as the domain and the mutation site is completed.

Create beautiful and interactive visualizations in a single function call. The 'data.table' package is utilized to perform the data wrangling necessary to prepare your data for the plot types you wish to build, along with allowing fast processing for big data. There are two broad classes of plots available: standard plots and machine learning evaluation plots. There are lots of parameters available in each plot type function for customizing the plots (such as faceting) and data wrangling (such as variable transformations and aggregation).

Performs Rasch analysis (semi-)automatically, which has been shown to be comparable with the standard Rasch analysis (Feri Wijayanto et al. (2021) <doi:10.1111/bmsp.12218>, Feri Wijayanto et al. (2022) <doi:10.3758/s13428-022-01947-9>, Feri Wijayanto et al. (2022) <doi:10.1177/01466216221125178>).

Make summary tables for descriptive statistics and select explanatory variables 
    automatically in various regression models. Support linear models, generalized linear 
    models and cox-proportional hazard models. Generate publication-ready tables summarizing 
    result of regression analysis and plots. The tables and plots can be exported in "HTML", 
    "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents.

Consider autoregressive model of order p where the distribution function of innovation is unknown, but innovations are independent and symmetrically distributed. The package contains a function named ARMDE which takes X (vector of n observations) and p (order of the model) as input argument and returns minimum distance estimator of the parameters in the model.