Provides a tool for computing probabilities and other quantities that are relevant in selecting performance criteria for discrete trial training. The main function, miebl(), computes Bayesian and frequentist probabilities and bounds for each of n possible performance criterion choices when attempting to determine a student's true mastery level by counting their number of successful attempts at displaying learning among n trials. The reporting function miebl_re() takes output from miebl() and prepares it into a brief report for a specific criterion. miebl_cp() combines 2 to 5 distributions of true mastery level given performance criterion in one plot for comparison. Ramos (2025) <doi:10.1007/s40617-025-01058-9>.

Evolutionary black box optimization algorithms building on the
  'bbotk' package. 'miesmuschel' offers both ready-to-use optimization
  algorithms, as well as their fundamental building blocks that can be used to
  manually construct specialized optimization loops. The Mixed Integer Evolution
  Strategies as described by Li et al. (2013) <doi:10.1162/EVCO_a_00059> can be
  implemented, as well as the multi-objective optimization algorithms NSGA-II
  by Deb, Pratap, Agarwal, and Meyarivan (2002) <doi:10.1109/4235.996017>.

Impute the covariance matrix of incomplete data so that factor 
  analysis can be performed. Imputations are made using multiple imputation 
  by Multivariate Imputation with Chained Equations (MICE) and combined with 
  Rubin's rules. Parametric Fieller confidence intervals and nonparametric
  bootstrap confidence intervals can be obtained for the variance explained by 
  different numbers of principal components. The method is described in 
  Nassiri et al. (2018) <doi:10.3758/s13428-017-1013-4>.

Provides utilities for estimation for the multivariate inverse Gaussian distribution of Minami (2003) <doi:10.1081/STA-120025379>, including random vector generation and explicit estimators of the location vector and scale matrix. The package implements kernel density estimators discussed in Belzile, Desgagnes, Genest and Ouimet (2024) <doi:10.48550/arXiv.2209.04757> for smoothing multivariate data on half-spaces.