The lognormal distribution  
  (Limpert et al. (2001) <doi:10.1641/0006-3568(2001)051%5B0341:lndats%5D2.0.co;2>)
  can characterize uncertainty that is bounded by zero.
  This package provides estimation of distribution parameters, computation of
  moments and other basic statistics, and an approximation of the distribution
  of the sum of several correlated lognormally distributed variables 
  (Lo 2013 <doi:10.12988/ams.2013.39511>) and the approximation of the 
  difference of two correlated lognormally distributed variables
  (Lo 2012 <doi:10.1155/2012/838397>).

Functions to fits simple linear regression models with log normal errors 
		and identity link, i.e. taking the responses on the original scale. See
		Muggeo (2018) <doi:10.13140/RG.2.2.18118.16965>.

Uses approximations to compute the natural logarithm of the Gamma
    function for large values.

Contains functions to help create log files.  The 
    package aims to overcome the difficulty of the base R sink() command.  The
    log_print() function will print to both the console and the file log, 
    without interfering in other write operations.

Tools for assessing equivalence of similar Logistic Regression models.

A utility to facilitate the logging and review of R programs in clinical trial programming workflows.

Contains routines for logspline density estimation.
	The function oldlogspline() uses the same algorithm as the logspline package
	version 1.0.x; i.e. the Kooperberg and Stone (1992) 
	algorithm (with an improved interface).  The recommended routine logspline()
	uses an algorithm from Stone et al (1997)  <DOI:10.1214/aos/1031594728>.

Kernel regression smoothing with adaptive local or global plug-in
	     bandwidth selection.

Draw lolliplot using GRanges objects. this package was designed 
  only for drawing lolliplot. So, it's faster than 'trackViewer', but un-related 
  functions has been derived.

Estimation of Latent Order Logistic (LOLOG) Models for Networks.
    LOLOGs are a flexible and fully general class of statistical graph models. 
    This package provides functions for performing MOM, GMM and variational 
    inference. Visual diagnostics and goodness of fit metrics are provided. 
    See Fellows (2018) <arXiv:1804.04583> for a detailed description of the methods.