It is sometimes useful to perform a computation in a separate
    R process, without affecting the current R process at all.  This
    packages does exactly that.

Intended to analyse recordings from multiple microphones (e.g., backpack
  microphones in captive setting). It allows users to align recordings even if there is non-linear 
  drift of several minutes between them. A call detection and assignment pipeline can be used 
  to find vocalisations and assign them to the vocalising individuals (even if the vocalisation
  is picked up on multiple microphones). The tracing and measurement functions allow for detailed
  analysis of the vocalisations and filtering of noise. Finally, the package includes a function
  to run spectrographic cross correlation, which can be used to compare vocalisations. It also 
  includes multiple other functions related to analysis of vocal behaviour.

The CalMaTe method calibrates preprocessed allele-specific copy number estimates (ASCNs) from DNA microarrays by controlling for single-nucleotide polymorphism-specific allelic crosstalk. The resulting ASCNs are on average more accurate, which increases the power of segmentation methods for detecting changes between copy number states in tumor studies including copy neutral loss of heterozygosity. CalMaTe applies to any ASCNs regardless of preprocessing method and microarray technology, e.g. Affymetrix and Illumina.

Implementations of canonical associative learning models,
    with tools to run experiment simulations, estimate model parameters,
    and compare model representations. Experiments and results are
    represented using S4 classes and methods.

Provides a Shiny application to conduct comprehensive analysis of latent means including the examination of group equivalency, propensity score analysis, 
 measurement invariance analysis, and assessment of latent mean differences of 
 equivalent groups with invariant data. Group equivalency and propensity score analyses are implemented using the 'MatchIt' package [Ho et al. (2011) <doi:10.18637/jss.v042.i08>], 
 ensuring robust control for covariates. Structural equation modeling and invariance testing rely heavily on the 'lavaan' package [Rosseel (2012) <doi:10.18637/jss.v048.i02>], 
 providing a flexible and powerful modeling framework. The application also integrates modified functions from Hammack-Brown et al. (2021) <doi:10.1002/hrdq.21452> 
 to support factor ratio testing and the list-and-delete procedure.