Provides utility functions to deal with Italian fiscal
    code ('codice fiscale').

Implements an S7 class for estimates based on influence functions, 
  with forward mode automatic differentiation defined for standard arithmetic 
  operations.

Implementation of two multi-criteria decision making methods (MCDM): Intuitionistic Fuzzy Synthetic Measure (IFSM) and Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (IFTOPSIS) for intuitionistic fuzzy data sets for multi-criteria decision making problems. References describing the methods: Jefmański (2020) <doi:10.1007/978-3-030-52348-0_4>; Jefmański, Roszkowska, Kusterka-Jefmańska (2021) <doi:10.3390/e23121636>.

Download ifo business survey data and more time series from
    ifo institute <https://www.ifo.de/en/ifo-time-series>.

Compute onestep and multistep time series forecasts for machine learning models.

A suite for identifying causal models using relative concordances and identifying causal polymorphisms in case-control genetic association data, especially with large controls re-sequenced data.

Imputation of missing values using the last observation carried forward technique on Indonesia food prices data that is time series data. Also, this technique applies imputation to data whose dates do not appear directly. So that the series assumptions in the time series data are met.

Iterated Function Systems Estimator as in Iacus and La Torre (2005) <doi:10.1155/JAMDS.2005.33>.

This function predicts item response probabilities and item 
  responses using the item-focused tree model. The item-focused tree model
  combines logistic regression with recursive partitioning to detect 
  Differential Item Functioning in dichotomous items. The model applies 
  partitioning rules to the data, splitting it into homogeneous subgroups, and 
  uses logistic regression within each subgroup to explain the data. 
  Differential Item Functioning detection is achieved by examining potential 
  group differences in item response patterns. This method is useful for 
  understanding how different predictors, such as demographic or psychological 
  factors, influence item responses across subgroups.