Additional appenders for the logging package 'lgr' that
    support logging to 'Elasticsearch', 'Dynatrace', 'AWSCloudWatchLog',
    databases, 'syslog', email- and push notifications, and more.

Functions for the longitudinal genetic random field method (He et al., 2015, <doi:10.1111/biom.12310>) to test the association between a longitudinally measured quantitative outcome and a set of genetic variants in a gene/region.

Implements Lagrangian multiplier smoothing splines for flexible
    nonparametric regression and function estimation. Provides tools for fitting,
    prediction, and inference using a constrained optimization approach to 
    enforce smoothness.
    Supports generalized linear models, Weibull accelerated failure time (AFT) models, 
    quadratic programming problems, and customizable arbitrary correlation structures. 
    Options for fitting in parallel are provided. The method builds upon the framework described 
    by Ezhov et al. (2018) <doi:10.1515/jag-2017-0029> using Lagrangian multipliers 
    to fit cubic splines. For more information on correlation structure estimation, 
    see Searle et al. (2009) <ISBN:978-0470009598>. For quadratic programming and 
    constrained optimization in general, see Nocedal & Wright (2006) <doi:10.1007/978-0-387-40065-5>. 
    For a comprehensive background on smoothing splines, see Wahba (1990) 
    <doi:10.1137/1.9781611970128> and Wood (2006) <ISBN:978-1584884743> 
    "Generalized Additive Models: An Introduction with R".

A very simple implementation of a class for 
	     longitudinal data.

Contains different algorithms and construction methods for optimal Latin hypercube designs (LHDs) with flexible sizes. Our package is comprehensive since it is capable of generating maximin distance LHDs, maximum projection LHDs, and orthogonal and nearly orthogonal LHDs. Detailed comparisons and summary of all the algorithms and construction methods in this package can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <doi:10.48550/arXiv.2010.09154>. This package is particularly useful in the area of Design and Analysis of Experiments (DAE). More specifically, design of computer experiments.