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.

Allows the user to estimate transition probabilities for
    migratory animals between any two phases of the annual cycle, using a
    variety of different data types. Also quantifies the strength of
    migratory connectivity (MC), a standardized metric to quantify the
    extent to which populations co-occur between two phases of the annual
    cycle. Includes functions to estimate MC and the more traditional
    metric of migratory connectivity strength (Mantel correlation)
    incorporating uncertainty from multiple sources of sampling error. For
    cross-species comparisons, methods are provided to estimate
    differences in migratory connectivity strength, incorporating
    uncertainty. See Cohen et al. (2018) <doi:10.1111/2041-210X.12916>,
    Cohen et al. (2019) <doi:10.1111/ecog.03974>, Roberts et al. (2023)
    <doi:10.1002/eap.2788>, and Hostetler et al. (2025)
    <doi:10.1111/2041-210X.14467> for details on some of these methods.