Suite of utilities for accessing and manipulating
    data from the 'KoboToolbox' API. 'KoboToolbox' is a robust
    platform designed for field data collection in various disciplines.
    This package aims to simplify the process of fetching and handling
    data from the API. Detailed documentation for the 'KoboToolbox' API
    can be found at <https://support.kobotoolbox.org/api.html>.

Provides functions to download and parse 'robots.txt' files.
        Ultimately the package makes it easy to check if bots
        (spiders, crawler, scrapers, ...) are allowed to access specific
        resources on a domain.

Calculates periodograms based on (robustly) fitting periodic functions to light curves (irregularly observed time series, possibly with measurement accuracies, occurring in astroparticle physics). Three main functions are included: RobPer() calculates the periodogram. Outlying periodogram bars (indicating a period) can be detected with betaCvMfit(). Artificial light curves can be generated using the function tsgen(). For more details see the corresponding article: Thieler, Fried and Rathjens (2016), Journal of Statistical Software 69(9), 1-36, <doi:10.18637/jss.v069.i09>.

Three-step regression and inference for cellwise and casewise contamination.

We implement the algorithm estimating the parameters of the robust regression model with compositional covariates. The model simultaneously treats outliers and provides reliable parameter  estimates. Publication reference: Mishra, A., Mueller, C.,(2019) <arXiv:1909.04990>.

Robust methods for estimating the parameters of multivariate Gaussian linear models.

A framework for estimating ensembles of parametric survival models
    with different parametric families. The RoBSA framework uses Bayesian 
    model-averaging to combine the competing parametric survival models into 
    a model ensemble, weights the posterior parameter distributions based on 
    posterior model probabilities and uses Bayes factors to test for the 
    presence or absence of the individual predictors or preference for a 
    parametric family (Bartoš, Aust & Haaf, 2022, <doi:10.1186/s12874-022-01676-9>).
    The user can define a wide range of informative priors for all parameters 
    of interest. The package provides convenient functions for summary, visualizations, 
    fit diagnostics, and prior distribution calibration.