Performs nonparametric estimation in mixture cure models, and significance tests for the cure probability. For details, see López-Cheda et al. (2017a) <doi:10.1016/j.csda.2016.08.002> and López-Cheda et al. (2017b) <doi:10.1007/s11749-016-0515-1>.

Performs nonparametric estimation in mixture cure models when the cure status is partially known. For details, see Safari et al (2021) <doi:10.1002/bimj.202100156>, Safari et al (2022) <doi:10.1177/09622802221115880> and Safari et al (2023) <doi:10.1007/s10985-023-09591-x>.

Provides routines to compute normalised prediction distribution errors, a metric designed to evaluate non-linear mixed effect models such as those used in pharmacokinetics and pharmacodynamics.

A novel integral estimator for estimating the causal effects with continuous treatments (or dose-response curves) and a localized derivative estimator for estimating the derivative effects. The inference on the dose-response curve and its derivative is conducted via nonparametric bootstrap.
    The reference paper is Zhang, Chen, and Giessing (2024) <doi:10.48550/arXiv.2405.09003>.

Simulate demand and attributes for ready to launch new products 
    during their life cycle, or during their introduction and growth phases.
    You provide the number of products, attributes, time periods and/or other parameters 
    and 'npdsim' can simulate for you the demand for each product during the considered 
    time periods, and the attributes of each product. The simulation for the demand
    is based on the idea that each product has a shape and a level, where the level
    is the cumulative demand over the considered time periods, and the shape is
    the normalized demand across those time periods.

Provides several novel exact hypothesis tests with minimal assumptions on the errors. The tests are exact, meaning that their p-values are correct for the given sample sizes (the p-values are not derived from asymptotic analysis). The test for stochastic inequality is for ordinal comparisons based on two independent samples and requires no assumptions on the errors. The other tests include tests for the mean and variance of a single sample and comparing means in independent samples. All these tests only require that the data has known bounds (such as percentages that lie in [0,100]. These bounds are part of the input.