Provides tools for non-parametric Fourier deconvolution using the N-Power Fourier Deconvolution (NPFD) method. This package includes methods for density estimation (densprf()) and sample generation (createSample()), enabling users to perform statistical analyses on mixed or replicated data sets.

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>.

Implements the online Bayesian inference framework for joint
    state and parameter estimation in a stochastic
    Susceptible-Exposed-Infectious-Recovered (SEIR) epidemic model with a
    time-varying transmission rate. The log-transmission rate is modelled as
    a latent Ornstein-Uhlenbeck (OU) process with exact Gaussian discrete-time
    transitions. Inference is performed via the nested particle filter (NPF) of
    Crisan and Miguez (2018) <doi:10.3150/17-BEJ954>, which maintains an outer
    particle layer over the OU hyperparameters and, for each outer particle, an
    inner bootstrap filter over epidemic states. The Cori-style renewal-equation
    estimator follows Cori et al. (2013) <doi:10.1093/aje/kwt133>. The package
    also provides utilities for simulation, posterior summarisation, and
    forecasting.

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.