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.

Implements methods for querying data from CalPASS using its API.
 CalPASS Plus.  MMAP API V1. <https://mmap.calpassplus.org/docs/index.html>.

Generates the calibration simplex (a generalization of the reliability diagram) for three-category probability forecasts, as proposed by Wilks (2013) <doi:10.1175/WAF-D-13-00027.1>.

Tools for the analysis of finite semiparametric mixtures.
        These are useful when data is heterogeneous, e.g. in
        pharmacokinetics or meta-analysis. The NPMLE and VEM algorithms
        (flexible support size) and EM algorithms (fixed support size)
        are provided for univariate (Bohning et al., 1992; 
        <doi:10.2307/2532756>) and bivariate data (Schlattmann et 
        al., 2015; <doi:10.1016/j.jclinepi.2014.08.013>).