Semiparametric distributional regression methods (expectile,
    quantile and mode regression) for time-to-event variables with
    right-censoring; uses inverse probability of censoring weights or
    accelerated failure time models with auxiliary likelihoods. Expectile
    regression using inverse probability of censoring weights has been
    introduced in Seipp et al. (2021) ``Weighted Expectile Regression for
    Right-Censored Data'' <doi:10.1002/sim.9137>, mode regression for
    time-to-event variables has been introduced in Seipp et al. (2022)
    ``Flexible Semiparametric Mode Regression for Time-to-Event Data''
    <doi:10.1177/09622802221122406>.

The twoStepsBenchmark() and threeRuleSmooth() functions allow you to 
    disaggregate a low-frequency time series with higher frequency time series, 
    using the French National Accounts methodology. The aggregated sum of the 
    resulting time series is strictly equal to the low-frequency time series within the 
    benchmarking window. Typically, the low-frequency time series is an annual one, 
    unknown for the last year, and the high frequency one is either quarterly or 
    monthly. See "Methodology of quarterly national accounts", Insee Méthodes 
    N°126, by Insee (2012, ISBN:978-2-11-068613-8, <https://www.insee.fr/en/information/2579410>).

First - Generates (potentially high-dimensional) high-frequency and low-frequency series for simulation studies in temporal disaggregation; Second - a toolkit utilizing temporal disaggregation and benchmarking techniques with a low-dimensional matrix of indicator series previously proposed in Dagum and Cholette (2006, ISBN:978-0-387-35439-2) ; and Third - novel techniques proposed by Mosley, Gibberd and Eckley (2021) <arXiv:2108.05783> for disaggregating low-frequency series in the presence of high-dimensional indicator matrices.

Fits disaggregation regression models using 'TMB' ('Template Model
    Builder'). When the response data are aggregated to polygon level but
    the predictor variables are at a higher resolution, these models can be
    useful. Regression models with spatial random fields. The package is 
    described in detail in Nandi et al. (2023) <doi:10.18637/jss.v106.i11>.