Contains, as a main contribution, a function to fit
             a regression model with possibly right, left or interval
             censored observations and with the error distribution
             expressed as a mixture of G-splines. Core part
             of the computation is done in compiled 'C++' written
             using the 'Scythe' Statistical Library Version 0.3.

Given independent and identically distributed observations X(1), ..., X(n) from a Generalized Pareto distribution with shape parameter gamma in [-1,0], offers several estimates to compute estimates of gamma. The estimates are based on the principle of replacing the order statistics by quantiles of a distribution function based on a log--concave density function. This procedure is justified by the fact that the GPD density is log--concave for gamma in [-1,0].

A list of methods for estimating a smooth tensor with an unknown permutation. It also contains several multi-variate functions for generating permuted signal tensors and corresponding observed tensors. For a detailed introduction for the model and estimation techniques, see the paper by Chanwoo Lee and Miaoyan Wang (2021) "Smooth tensor estimation with unknown permutations" <arXiv:2111.04681>.

A flexible moving average algorithm for modeling drug exposure in pharmacoepidemiology studies as presented in the article: Ouchi, D.,  Giner-Soriano, M., Gómez-Lumbreras, A., Vedia Urgell, C.,Torres, F., & Morros, R. (2022). "Automatic Estimation of the Most Likely Drug Combination in Electronic Health Records Using the Smooth Algorithm : Development and Validation Study." JMIR medical informatics, 10(11), e37976. <doi:10.2196/37976>.

The nonparametric trend and its derivatives in equidistant time 
    series (TS) with short-memory stationary errors can be estimated. The 
    estimation is conducted via local polynomial regression using an 
    automatically selected bandwidth obtained by a built-in iterative plug-in 
    algorithm or a bandwidth fixed by the user. A Nadaraya-Watson kernel 
    smoother is also built-in as a comparison. With version 1.1.0, a linearity 
    test for the trend function, forecasting methods and backtesting 
    approaches are implemented as well.
    The smoothing methods of the package are described in Feng, Y., Gries, T., 
    and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598>.