Beta-binomial Mixture Model is used to infer the pattern from count data.
		It can be used for clustering of RNA methylation sequencing data.

Use dynamic programming method to solve l1 convex clustering with identical weights.

The automated clustering and quantification of the digital PCR
            data is based on the combination of 'DBSCAN' (Hahsler et al. (2019)
            <doi:10.18637/jss.v091.i01>) and 'c-means' (Bezdek et al. (1981) 
            <doi:10.1007/978-1-4757-0450-1>) algorithms.
            The analysis is independent of multiplexing geometry, dPCR system, 
            and input amount.
            The details about input data and parameters are available in the
            vignette.

Compares distributions with one another in terms of their fit to each sample in a dataset that contains multiple samples, as described in Joo, Aguinis, and Bradley (in press). Users can examine the fit of seven distributions per sample: pure power law, lognormal, exponential, power law with an exponential cutoff, normal, Poisson, and Weibull. Automation features allow the user to compare all distributions for all samples with a single command line, which creates a separate row containing results for each sample until the entire dataset has been analyzed.

Perform tree-ring analyses such as detrending, chronology
        building, and cross dating.  Read and write standard file formats
        used in dendrochronology.

A fast, consistent tool for working with data frame like
    objects, both in memory and out of memory.

An RStudio addin for teaching and learning data manipulation using the 'dplyr' package.
    You can learn each steps of data manipulation by clicking your mouse without coding.
    You can get resultant data (as a 'tibble') and the code for data manipulation.

Implements the dynamic panel models described by Allison, Williams,
 and Moral-Benito (2017 <doi:10.1177/2378023117710578>) in R. 
 This class of models uses structural equation modeling to specify dynamic 
 (lagged dependent variable) models with fixed effects for panel data. 
 Additionally, models may have predictors that are only weakly exogenous, i.e.,
 are affected by prior values of the dependent variable. Options also allow for
 random effects, dropping the lagged dependent variable, and a number of other 
 specification choices.