Computes probabilities related to group sequential designs for
    normally distributed test statistics. Enables to derive critical
    boundaries, power, drift, and confidence intervals of such designs.
    Supports the alpha spending approach by Lan-DeMets (1994)
    <doi:10.1002/sim.4780131308>.

Contains functions for a two-stage multiple testing procedure for grouped hypothesis, aiming at controlling both the total posterior false discovery rate and within-group false discovery rate.

A procedure that uses target-decoy competition (or knockoffs) to reject multiple hypotheses in the presence of group structure. The procedure controls the false discovery rate (FDR) at a user-specified threshold.

Fits weighted quantile sum (WQS) regressions for one or more chemical groups with continuous or binary outcomes. Wheeler D, Czarnota J.(2016) <doi:10.1289/isee.2016.4698>.

Functional denoising and functional ANOVA through wavelet-domain 
  Markov groves. Fore more details see: Ma L. and Soriano J. (2018) 
  Efficient functional ANOVA through wavelet-domain Markov groves. 
  <arXiv:1602.03990v2 [stat.ME]>.

Run grass growth simulations using a grass growth model based 
    on ModVege (Jouven, M., P. Carrère, and R. Baumont "Model Predicting 
    Dynamics of Biomass, Structure and Digestibility of Herbage in Managed 
    Permanent Pastures. 1. Model Description." (2006) 
    <doi:10.1111/j.1365-2494.2006.00515.x>). The implementation in
    this package contains a few additions to the above cited version of ModVege,
    such as simulations of management decisions, and influences of snow cover.
    As such, the model is fit to simulate grass growth in mountainous 
    regions, such as the Swiss Alps. The package also contains routines for 
    calibrating the model and helpful tools for analysing model outputs and 
    performance.

Functions for fitting various normal theory (growth
    curve) and elliptically-contoured repeated measurements models
    with ARMA and random effects dependence.