Simulation and Resampling Methods for Epistemic Fuzzy Data

Random simulations of fuzzy numbers are still a challenging problem. The aim of this package is to provide the respective procedures to simulate fuzzy random variables, especially in the case of the piecewise linear fuzzy numbers (PLFNs, see Coroianua et al. (2013) for the further details). Additionally, the special resampling algorithms known as the epistemic bootstrap are provided (see Grzegorzewski and Romaniuk (2022) , Grzegorzewski and Romaniuk (2022) , Romaniuk et al. (2024) ) together with the functions to apply statistical tests and estimate various characteristics based on the epistemic bootstrap. The package also includes real-life datasets of epistemic fuzzy triangular and trapezoidal numbers. The fuzzy numbers used in this package are consistent with the 'FuzzyNumbers' package.

International Assessment Data Manager

Provides tools for importing, merging, and analysing data from international assessment studies (TIMSS, PIRLS, PISA, ICILS, and PIAAC).

Explainable Machine Learning in Survival Analysis

Survival analysis models are commonly used in medicine and other areas. Many of them are too complex to be interpreted by human. Exploration and explanation is needed, but standard methods do not give a broad enough picture. 'survex' provides easy-to-apply methods for explaining survival models, both complex black-boxes and simpler statistical models. They include methods specific to survival analysis such as SurvSHAP(t) introduced in Krzyzinski et al., (2023) , SurvLIME described in Kovalev et al., (2020) as well as extensions of existing ones described in Biecek et al., (2021) .

PLINK 2 Binary (.pgen) Reader

A thin wrapper over PLINK 2's core libraries which provides an R interface for reading .pgen files. A minimal .pvar loader is also included. Chang et al. (2015) \doi{10.1186/s13742-015-0047-8}.

Bayesian Hierarchical Analysis of Cognitive Models of Choice

Fit Bayesian (hierarchical) cognitive models using a linear modeling language interface using particle Metropolis Markov chain Monte Carlo sampling with Gibbs steps. The diffusion decision model (DDM), linear ballistic accumulator model (LBA), racing diffusion model (RDM), and the lognormal race model (LNR) are supported. Additionally, users can specify their own likelihood function and/or choose for non-hierarchical estimation, as well as for a diagonal, blocked or full multivariate normal group-level distribution to test individual differences. Prior specification is facilitated through methods that visualize the (implied) prior. A wide range of plotting functions assist in assessing model convergence and posterior inference. Models can be easily evaluated using functions that plot posterior predictions or using relative model comparison metrics such as information criteria or Bayes factors. References: Stevenson et al. (2024) .

Efficient Serialization of R Objects

Streamlines and accelerates the process of saving and loading R objects, improving speed and compression compared to other methods. The package provides two compression formats: the 'qs2' format, which uses R serialization via the C API while optimizing compression and disk I/O, and the 'qdata' format, featuring custom serialization for slightly faster performance and better compression. Additionally, the 'qs2' format can be directly converted to the standard 'RDS' format, ensuring long-term compatibility with future versions of R.

Procedures Related to the Zadeh's Extension Principle for Fuzzy Data

Procedures for calculation, plotting, animation, and approximation of the outputs for fuzzy numbers (see A.I. Ban, L. Coroianu, P. Grzegorzewski "Fuzzy Numbers: Approximations, Ranking and Applications" (2015)) based on the Zadeh's Extension Principle (see de Barros, L.C., Bassanezi, R.C., Lodwick, W.A. (2017) ).