A collection of utilities that grew out of day-to-day non-life
    actuarial work at Com-PASS Advisory. Provides helpers for building
    chain-ladder triangles (cumulative, decumulative, run-off, development
    factors with optional weighting), constructing exposure columns from
    policy start/end dates, parsing Czech birth numbers ('rodné číslo')
    into dates, generating smooth RGB color palettes for charts, and
    loading multi-sheet 'xlsx'/'xlsb' files into a list of data frames.
    The chain-ladder helpers follow the standard methodology of
    Mack (1993) <doi:10.2143/AST.23.2.2005092>.

Provides functions for importing, validating, and analyzing 'Viva Glint'
    survey data exports, with optional API-based import via the 'Microsoft Graph'
    API. Includes tools for data reshaping, question-level analysis, multi-cycle
    comparisons, organizational hierarchy analysis, factor analysis, and
    correlation analysis. Harman (1960, ISBN: 0226316513); Husser (2017)
    <doi:10.1002/9781118901731.iecrm0048>.

Provides a versatile range of functions, including exploratory data analysis, time-series analysis, organizational network analysis, and data validation, whilst at the same time implements a set of best practices in analyzing and visualizing data specific to 'Microsoft Viva Insights'.

Analysis of minor alleles in Illumina sequencing data of viral
    genomes. Functions in 'vivaldi' primarily operate on vcf files.

A suite of plots for displaying variable importance and two-way variable interaction jointly. Can also display partial dependence plots laid out in a pairs plot or 'zenplots' style.

Provides an easy to calculate local variable importance measure based on Ceteris Paribus profile 
  and global variable importance measure based on Partial Dependence Profiles.

Provides tools to explore and visualize transitions between clusters in multivariate data. The package generates pseudo-samples by interpolating between cluster medoids, enabling the study of gradual changes in feature space. It also computes k-nearest neighbors (KNN)-based statistics to relate pseudo-samples to real data and summarize variable behavior using mean, median, or standard deviation. Finally, the package offers interactive visualizations of variable trajectories along cluster transitions, including both direct trajectory plots and bootstrap-based interactive plots with confidence intervals to assess variability and uncertainty across the transition path.