Functions to generate ensembles of generalized linear models using
             a greedy projected subset gradient descent algorithm. The sparsity 
             and diversity tuning parameters are selected by cross-validation.

Presentation of a new goodness-of-fit normality test based on the Lilliefors method. For details on this method see: Sulewski (2019) <doi:10.1080/03610918.2019.1664580>.

Implements projected sparse Gaussian process Kriging (Ingram 'et. al.', 2008, <doi:10.1007/s00477-007-0163-9>) as an additional method for the 'intamap' package. More details on implementation (Barillec 'et. al.', 2010, <doi:10.1016/j.cageo.2010.05.008>).

In the situation when multiple alternative treatments or
    interventions available, different population groups may respond differently
    to different treatments. This package implements a method that discovers
    the population subgroups in which a certain treatment has a better effect
    than the other alternative treatments. This is done by first estimating the
    treatment effect for a given treatment and its uncertainty by computing random
    forests, and the resulting model is summarized by a decision tree in which the
    probabilities that the given treatment is best for a given subgroup is shown in
    the corresponding terminal node of the tree.

Streamline the management, creation, and formatting of panel data from the Panel Study of Income Dynamics ('PSID') <https://psidonline.isr.umich.edu> using this user-friendly tool. Simply define variable names and input code book details directly from the 'PSID' official website, and this toolbox will efficiently facilitate the data preparation process, transforming raw 'PSID' files into a well-organized format ready for further analysis.

The Preference Selection Index Method was created in (2010) and provides an innovative approach to determining the relative importance of criteria without pairwise comparisons, unlike the Analytic Hierarchy Process. The Preference Selection Index Method uses statistical methods to calculate the criteria weights and reflects their relative importance in the final decision-making process, offering an objective and non-subjective solution. This method is beneficial in multi-criteria decision analysis. The 'PSIM' package provides a practical and accessible tool for implementing the Preference Selection Index Method in R. It calculates the weights of criteria and makes the method available to researchers, analysts, and professionals without the need to develop complex calculations manually. More details about the Preference Selection Index Method can be found in Maniya K. and Bhatt M. G.(2010) <doi:10.1016/j.matdes.2009.11.020>.