Most existing approaches for network reconstruction can only infer an overall network 
	and, also, fail to capture a complete set of network properties. To address these issues, 
	a new model has been developed, which converts static data into their 'dynamic' form. 
	'idopNetwork' is an 'R' interface to this model, it can inferring informative, dynamic, 
	omnidirectional and personalized networks. For more information on functional 
	clustering part, see Kim et al. (2008) <doi:10.1534/genetics.108.093690>, 
	Wang et al. (2011) <doi:10.1093/bib/bbr032>. For more information on our model, 
	see Chen et al. (2019) <doi:10.1038/s41540-019-0116-1>, and Cao et al. (2022) 
	<doi:10.1080/19490976.2022.2106103>.

A method to integrate molecular profiles of cancer patients (gene copy number and mRNA abundance) to 
	identify candidate gain of function alterations. These candidate alterations can be subsequently further tested  
	to discover cancer driver alterations. Briefly, this method tests of genomic correlates of mRNA dysregulation and prioritise 
	those where DNA gains/amplifications are associated with elevated mRNA expression of the same gene. For details see,
	Haider S et al. (2016) "Genomic alterations underlie a pan-cancer metabolic shift associated with tumour hypoxia", Genome Biology, <https://pubmed.ncbi.nlm.nih.gov/27358048/>.

Implements a nonparametric maximum likelihood method for assessing 
  potentially time-varying vaccine efficacy (VE) against SARS-CoV-2 infection 
  under staggered enrollment and time-varying community transmission, allowing 
  crossover of placebo volunteers to the vaccine arm. 
  Lin, D. Y., Gu, Y., Zeng, D., Janes, H. E., and Gilbert, P. B. (2021) 
  <doi:10.1093/cid/ciab630>.

Different high-level graphics functions for displaying large datasets, displaying circular data in a very flexible way, finding local maxima, brewing color ramps, drawing nice arrows, zooming 2D-plots, creating figures with differently colored margin and plot region.  In addition, the package contains auxiliary functions for data manipulation like omitting observations with irregular values or selecting data by logical vectors, which include NAs. Other functions are especially useful in spectroscopy and analyses of environmental data: robust baseline fitting, finding peaks in spectra, converting humidity measures.