Estimators of Difference-in-Differences based on de Chaisemartin and D'Haultfoeuille.

Estimation of event-study Difference-in-Difference (DID) estimators in designs with multiple groups and periods, and with a potentially non-binary treatment that may increase or decrease multiple times.

This hosts the findRFM function which generates RFM scores on a 1-5 point scale for
             customer transaction data. The function consumes a data frame with Transaction Number,
             Customer ID, Date of Purchase (in date format) and Amount of Purchase as the attributes.
             The function returns a data frame with RFM data for the sales information.

Likelihood-based genome polarisation finds which alleles of genomic markers 
    belong to which side of the barrier. 
	Co-estimates which individuals belong to either side of the barrier and barrier strength.
	Uses expectation maximisation in likelihood framework. The method is described in 
	Baird et al. (2023) <doi:10.1111/2041-210X.14010>.

Estimates fractional trophic level from quantitative and qualitative diet data and calculates electivity indices in R. Borstein (2020) <doi:10.1007/s10750-020-04417-5>.

A wrapper for the ZEIT ONLINE Content API, available at <http://developer.zeit.de>. 'diezeit' gives access to articles and corresponding metadata from the ZEIT archive and from ZEIT ONLINE. A personal API key is required for usage.

Performs detection of Differential Item Functioning using the method DIFboost as proposed by Schauberger and Tutz (2016) <doi:10.1111/bmsp.12060>.

Estimation of DIFferential COexpressed NETworks using diverse and user metrics.
			 This package is basically used for three functions related to the estimation
			 of differential coexpression. 
			 First, to estimate differential coexpression where
			 the coexpression is estimated, by default, by Spearman correlation. For this,
			 a metric to compare two correlation distributions is needed. The package includes
			 6 metrics. Some of them needs a threshold. A new metric can also be specified as
			 a user function with specific parameters (see difconet.run). The significance is
			 be estimated by permutations.
			 Second, to generate datasets with controlled differential correlation data. This 
			 is done by either adding noise, or adding specific correlation structure.
			 Third, to show the results of differential correlation analyses. Please see
			 <http://bioinformatica.mty.itesm.mx/difconet> for further information.