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>.

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.

Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions
             modeling oriented towards flow-cytometry data preprocessing applications. Method is 
             detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) <doi: 10.1214/18-AOAS1209>.

Piecewise constant hazard functions are used to flexibly model survival distributions with non-proportional hazards and 
	to simulate data from the specified distributions. A function to calculate weighted log-rank tests for the comparison of two
	hazard functions is included. Also, a function to calculate a test using the maximum of a set of test statistics from weighted
	log-rank tests (MaxCombo test) is provided. This test utilizes the asymptotic multivariate normal joint distribution of the
	separate test statistics. The correlation is estimated from the data. These methods are described in Ristl et al. (2021) 
	<doi:10.1002/pst.2062>.
	Finally, a function is provided for the estimation and inferential statistics of various parameters that quantify the difference
	between two survival curves. Eligible parameters are differences in survival probabilities, log survival probabilities,
	complementary log log (cloglog) transformed survival probabilities, quantiles of the survival functions, log transformed quantiles,
	restricted mean survival times, as well as an average hazard ratio, the Cox model score statistic (logrank statistic), and the
	Cox-model hazard ratio. Adjustments for multiple testing and simultaneous confidence intervals are calculated using a multivariate
	normal approximation to the set of selected parameters.