Utility functions to be used to analyse datasets obtained from seed germination/emergence assays. Fits several types of seed germination/emergence models, including those reported in Onofri et al. (2018) "Hydrothermal-time-to-event models for seed germination", European Journal of Agronomy, 101, 129-139 <doi:10.1016/j.eja.2018.08.011>. Contains several datasets for practicing.

A specific and comprehensive framework for the analyses of time-to-event data in agriculture. Fit non-parametric and parametric time-to-event models. Compare time-to-event curves for different experimental groups. Plots and other displays. It is particularly tailored to the analyses of data from germination and emergence assays. The methods are described in Onofri et al. (2022) "A unified framework for the analysis of germination, emergence, and other time-to-event data in weed science", Weed Science, 70, 259-271 <doi:10.1017/wsc.2022.8>.

Fit logistic functions to observed dose-response continuous
    data and evaluate goodness-of-fit measures. See Malyutina A., Tang J.,
    and Pessia A. (2023) <doi:10.18637/jss.v106.i04>.

Implements the locally efficient doubly robust difference-in-differences (DiD)
    estimators for the average treatment effect proposed by Sant'Anna and Zhao (2020)
    <doi:10.1016/j.jeconom.2020.06.003>. The estimator combines inverse probability weighting and outcome
    regression estimators (also implemented in the package) to form estimators with
    more attractive statistical properties. Two different estimation methods can be used
    to estimate the nuisance functions.

While it has been well established that drugs affect and help
  patients differently, personalized drug response predictions remain 
  challenging. Solutions based on single omics measurements have been proposed, 
  and networks provide means to incorporate molecular interactions into reasoning. 
  However, how to integrate the wealth of information contained in multiple omics 
  layers still poses a complex problem. 
  We present a novel network analysis pipeline, DrDimont, Drug response prediction 
  from Differential analysis of multi-omics networks. It allows for comparative 
  conclusions between two conditions and translates them into differential drug 
  response predictions. DrDimont focuses on molecular interactions. It establishes 
  condition-specific networks from correlation within an omics layer that are 
  then reduced and combined into heterogeneous, multi-omics molecular networks. 
  A novel semi-local, path-based integration step ensures integrative conclusions. 
  Differential predictions are derived from comparing the condition-specific 
  integrated networks. DrDimont's predictions are explainable, i.e., molecular 
  differences that are the source of high differential drug scores can be retrieved.
  Our proposed pipeline leverages multi-omics data for differential predictions,
  e.g. on drug response, and includes prior information on interactions.
  The case study presented in the vignette uses data published by
  Krug (2020) <doi:10.1016/j.cell.2020.10.036>. The package license applies only
  to the software and explicitly not to the included data.