The BACON algorithms are methods for multivariate outlier
    nomination (detection) and robust linear regression by Billor, Hadi,
    and Velleman (2000) <doi:10.1016/S0167-9473(99)00101-2>. The extension
    to weighted problems is due to Beguin and Hulliger (2008)
    <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X200800110616>; see
    also <doi:10.21105/joss.03238>.

Provides efficient implementation of the Wild Binary Segmentation and Binary
    Segmentation algorithms for estimation of the number and locations of
    multiple change-points in the piecewise constant function plus Gaussian
    noise model.

Implements the diagnostic "theta" developed in Poetscher and Preinerstorfer (2020) "How Reliable are Bootstrap-based Heteroskedasticity Robust Tests?" <arXiv:2005.04089>. This diagnostic can be used to detect and weed out bootstrap-based procedures that provably have size equal to one for a given testing problem. The implementation covers a large variety of bootstrap-based procedures, cf. the above mentioned article for details. A function for computing bootstrap p-values is provided.

Search and download data from the World Bank Data API.

Implements detection for the number and locations of
    the change-points in a time series using the Wild Binary Segmentation and
    the Locally Stationary Wavelet model of Korkas and Fryzlewicz (2017) <doi:10.5705/ss.202015.0262>.

Download and plot education specific demographic data from the Wittgenstein Centre for Demography and Human Capital Data Explorer <http://dataexplorer.wittgensteincentre.org/>.

A flexible method for modeling cumulative effects of time-varying exposures, weighted according to their relative proximity in time, and represented by time-dependent covariates. The current implementation estimates the weight function in the Cox proportional hazards model. The function that assigns weights to doses taken in the past is estimated using cubic regression splines.

Analyze given data frame with multiple endpoints and return Kaplan-Meier survival probabilities together with the specified confidence interval. See Nabipoor M, Westerhout CM, Rathwell S, and Bakal JA (2023) <doi:10.1186/s12874-023-01857-0>.