Fast exponentiation when the exponent is an integer.

Perform user-friendly power analyses for the random
    intercept cross-lagged panel model (RI-CLPM) and the bivariate stable trait 
    autoregressive trait state (STARTS) model. The strategy as proposed by 
    Mulder (2023) <doi:10.1080/10705511.2022.2122467> is implemented. Extensions 
    include the use of parameter constraints over time, bounded estimation,
    generation of data with skewness and kurtosis, and the option to setup the power 
    analysis for Mplus.

Conduct a priori power analyses via Monte-Carlo style data simulation for linear and generalized linear mixed-effects models (LMMs/GLMMs). Provides a user-friendly workflow with helper functions to easily define fixed and random effects as well as diagnostic functions to evaluate the adequacy of the results of the power analysis.

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>.

Performs partial principal component analysis of a large sparse matrix. 
  The matrix may be stored as a list of matrices to be concatenated (implicitly) horizontally.
  Useful application includes cases where the number of total nonzero entries exceed the capacity of 
  32 bit integers (e.g., with large Single Nucleotide Polymorphism data).