Linear Liu regression coefficient's estimation and testing with different Liu related measures such as MSE, R-squared etc.
  REFERENCES
  i.   Akdeniz and Kaciranlar (1995) <doi:10.1080/03610929508831585>
  ii.  Druilhet and Mom (2008) <doi:10.1016/j.jmva.2006.06.011>
  iii. Imdadullah, Aslam, and Saima (2017)
  iv.  Liu (1993) <doi:10.1080/03610929308831027>
  v.   Liu (2001) <doi:10.1016/j.jspi.2010.05.030>.

Interpretability of complex machine learning models is a growing concern.
    This package helps to understand key factors that drive the 
    decision made by complicated predictive model (so called black box model). 
    This is achieved through local approximations that are either based on 
    additive regression like model or CART like model that allows for 
    higher interactions. The methodology is based on Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>.
    More details can be found in Staniak, Biecek (2018) <doi:10.32614/RJ-2018-072>.

Offers a suite of helper functions to simplify various data science techniques for non-experts. This package aims to enable individuals with only a minimal level of coding knowledge to become acquainted with these techniques in an accessible manner. Inspired by an ancient Persian idiom, we liken this process to "eating the liver of data science," suggesting a deep and intimate engagement with the field of data science. This package includes functions for tasks such as data partitioning for out-of-sample testing, calculating Mean Squared Error (MSE) to assess prediction accuracy, and data transformations (z-score and min-max). In addition to these helper functions, the 'liver' package also features several intriguing datasets valuable for multivariate analysis.

Fits and tests logistic joinpoint models.

Computes Logistic Knowledge Tracing ('LKT') which is a general method for tracking human learning in an educational software system. Please see Pavlik, Eglington, and Harrel-Williams (2021) <https://ieeexplore.ieee.org/document/9616435>. 'LKT' is a method to compute features of student data that are used as predictors of subsequent performance. 'LKT' allows great flexibility in the choice of predictive components and features computed for these predictive components. The system is built on top of 'LiblineaR', which enables extremely fast solutions compared to base glm() in R.