The PP package includes estimation of (MLE, WLE, MAP, EAP, ROBUST)
    person parameters for the 1,2,3,4-PL model and the GPCM (generalized
    partial credit model). The parameters are estimated under the assumption
    that the item parameters are known and fixed. The package is useful e.g. in
    the case that items from an item pool / item bank with known item parameters
    are administered to a new population of test-takers and an ability
    estimation for every test-taker is needed.

Perform 1-dim/2-dim projection pursuit, grand tour and guided tour for big data based on data nuggets. 
    Reference papers: [1] Beavers et al., (2024) <doi:10.1080/10618600.2024.2341896>. 
    [2] Duan, Y., Cabrera, J., & Emir, B. (2023). "A New Projection Pursuit Index for Big Data." <doi:10.48550/arXiv.2312.06465>.

Calculates the Probability Plot Correlation Coefficient (PPCC) 
             between a continuous variable X and a specified distribution. The corresponding 
	     composite hypothesis test that was first introduced by 
	     Filliben (1975) <doi: 10.1080/00401706.1975.10489279> 
	     can be performed to test whether the sample 
	     X is element of either the Normal, log-Normal, Exponential,
	     Uniform, Cauchy, Logistic, Generalized Logistic, Gumbel (GEVI), Weibull,
	     Generalized Extreme Value, Pearson III (Gamma 2), Mielke's Kappa, Rayleigh
	     or Generalized Logistic Distribution. The PPCC test is performed with
	     a fast Monte-Carlo simulation.

In the era of big data, data redundancy and distributed characteristics pose new challenges to data analysis. This article proposes a method of optimal subset estimation for redundant distributed data based on PPC. Through PPC technology, this method can effectively extract useful information from redundant distributed data and estimate the optimal subset. Experimental results show that this method can improve data quality and utilization efficiency and evaluate its performance.The philosophy of the package is described in Guo G. (2020) <doi:10.1007/s00180-020-00974-4>.