Fast extrapolation of univariate and multivariate time features using K-Nearest Neighbors. The compact set of hyper-parameters is tuned via grid or random search.

Manage project dependencies from your DESCRIPTION file. Create a reproducible virtual environment with minimal additional files in your project. Provides tools to add, remove, and update dependencies as well as install existing dependencies with a single function.

On Affymetrix gene expression microarrays, a single gene may be measured by multiple probe sets. This can present a mild conundrum when attempting to evaluate a gene "signature" that is defined by gene names rather than by specific probe sets. This package provides a one-to-one mapping from gene to "best" probe set for four Affymetrix human gene expression microarrays: hgu95av2, hgu133a, hgu133plus2, and u133x3p. This package also includes the pre-calculated probe set quality scores that were used to define the mapping.

Estimates networks of conditional dependencies (Gaussian graphical models) from multiple classes of data (similar but not exactly, i.e. measurements on different equipment, in different locations or for various sub-types). Package also allows to generate simulation data and evaluate the performance. Implementation of the method described in Angelini, De Canditiis and Plaksienko (2022) <doi:10.3390/math10213983>.

Provides statistical methods for auditing as implemented in JASP
    for Audit (Derks et al., 2021 <doi:10.21105/joss.02733>). First, the package
    makes it easy for an auditor to plan a statistical sample, select the sample
    from the population, and evaluate the misstatement in the sample compliant
    with international auditing standards. Second, the package provides
    statistical methods for auditing data, including tests of digit
    distributions and repeated values. Finally, the package includes methods for
    auditing algorithms on the aspect of fairness and bias. Next to classical
    statistical methodology, the package implements Bayesian equivalents of
    these methods whose statistical underpinnings are described in Derks et al.
    (2021) <doi:10.1111/ijau.12240>, Derks et al. (2021)
    <doi:10.31234/osf.io/kzqp5>, and Derks et al. (2022)
    <doi:10.31234/osf.io/8nf3e>.