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, Cox proportional hazards models, 
    quadratic programming constraints, and customizable working-correlation 
    structures, with options for parallel fitting. The core spline construction 
    builds on Ezhov et al. (2018) <doi:10.1515/jag-2017-0029>. 
    Quadratic-programming and SQP details follow Goldfarb & Idnani (1983) 
    <doi:10.1007/BF02591962> and Nocedal & Wright (2006) 
    <doi:10.1007/978-0-387-40065-5>. For smoothing spline and penalized spline 
    background, see Wahba (1990) <doi:10.1137/1.9781611970128> and Wood (2017)
    <doi:10.1201/9781315370279>. For variance-component and correlation-parameter
    estimation, see Searle et al. (2006) <ISBN:978-0470009598>. The default
    multivariate partitioning step uses k-means clustering as in MacQueen
    (1967).

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.