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.

A novel interpretable machine learning-based framework to automate the development of a clinical scoring model for predefined outcomes. Our novel framework consists of six modules: variable ranking with machine learning, variable transformation, score derivation, model selection, domain knowledge-based score fine-tuning, and performance evaluation.The The original AutoScore structure is described in the research paper<doi:10.2196/21798>. A full tutorial can be found here<https://nliulab.github.io/AutoScore/>. Users or clinicians could seamlessly generate parsimonious sparse-score risk models (i.e., risk scores), which can be easily implemented and validated in clinical practice. We hope to see its application in various medical case studies.