Seed germinates through the physical process of water uptake by dry seed driven by the difference in water potential between the seed and the water. There exists seed-to-seed variability in the base seed water potential. Hence, there is a need for a distribution such that a viable seed with its base seed water potential germinates if and only if the soil water potential is more than the base seed water potential. This package estimates the stress tolerance and uniformity parameters of the seed lot for germination under various temperatures by using the hydro-time model of counts of germinated seeds under various water potentials. The distribution of base seed water potential has been considered to follow Normal, Logistic and Extreme value distribution. The estimated proportion of germinated seeds along with the estimates of stress and uniformity parameters are obtained using a generalised linear model. The significance test of the above parameters for within and between temperatures is also performed in the analysis. Details can be found in Kebreab and Murdoch (1999) <doi:10.1093/jxb/50.334.655> and Bradford (2002) <https://www.jstor.org/stable/4046371>.

Functions for the management and treatment of hydrology and 
  meteorology time-series stored in a 'Sqlite' data base.

A novel decision tree algorithm in the hypothesis testing framework. The algorithm examines the distribution difference between two child nodes over all possible binary partitions. The test statistic of the hypothesis testing is equivalent to the generalized energy distance, which enables the algorithm to be more powerful in detecting the complex structure, not only the mean difference. It is applicable for numeric, nominal, ordinal explanatory variables and the response in general metric space of strong negative type. The algorithm has superior performance compared to other tree models in type I error, power, prediction accuracy, and complexity.

High throughput toxicokinetics ("HTTK") is the 
             combination of 1) chemical-specific in vitro measurements or in
             silico predictions and 2) generic mathematical models, to
             predict absorption, distribution, metabolism, and excretion by the
             body. HTTK methods have been described by Pearce et al. (2017) 
             (<doi:10.18637/jss.v079.i04>) and Breen et al. (2021) (<doi:10.1080/17425255.2021.1935867>).
             Here we provide examples (vignettes) applying HTTK to solve various
             problems in bioinformatics, toxicology, and exposure science.
             In accordance with Davidson-Fritz et al. (2025) (<doi:10.1371/journal.pone.0321321>),
             whenever a new HTTK model is developed, the code to generate the 
             figures evaluating that model is added as a new vignettte.