* Latin Hypercube Designs (LHDs) Algorithms* (

**LHD**)

Contains functions for finding space-filling Latin Hypercube Designs (LHDs), e.g. maximin distance LHDs. Unlike other packages, our package is particularly useful in the area of Design and Analysis of Experiments (DAE). More specifically, it is very useful in design of computer experiments. One advantage of our package is its comprehensiveness. It contains a variety of heuristic algorithms (and their modifications) for searching maximin distance LHDs. In addition to that, it also contains other useful tools for developing and constructing maximin distance LHDs. In the future, algebraic construction methods will be added. Please refer to the function documentations for the detailed references of each function. Among all the references we used, one reference should be highlighted here, which is Ruichen Jin, Wei Chen, Agus Sudjianto (2005) <doi:10.1016/j.jspi.2004.02.014>. They provided a new form of phi_p criterion, which does not lose the space-filling property and simultaneously reduces the computational complexity when evaluating (or re-evaluating) an LHD. Their new phi_p criterion is a fundamental component of our many functions. Besides, the computation nature of the new phi_p criterion enables our functions to have less CPU time.

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**Generalized Gauss Markov Regression****ggmr**)

Implements the generalized Gauss Markov regression, this is useful when both predictor and response have uncertainty attached to them and also when covariance within the predictor, within the response and between the predictor and the response is present. Base on the results published in guide ISO/TS 28037 (2010) <https://…/44473.html>.

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**Tools for Matrix Algebra, Optimization and Inference****maotai**)

Matrix is an universal and sometimes primary object/unit in applied mathematics and statistics. We provide a number of algorithms for selected problems in optimization and statistical inference. For general exposition to the topic with focus on statistical context, see the book by Banerjee and Roy (2014, ISBN:9781420095388).

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**Profiling Compliers and Non-Compliers for Instrumental Variable Analysis****ivdesc**)

Estimating the mean and variance of a covariate for the complier, never-taker and always-taker subpopulation in the context of instrumental variable estimation. This package implements the method described in Marbach and Hangartner (2019) <doi:10.2139/ssrn.3380247>.

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**Kumaraswamy Complementary Weibull Geometric (Kw-CWG) Probability Distribution****elfDistr**)

Density, distribution function, quantile function and random generation for the Kumaraswamy Complementary Weibull Geometric (Kw-CWG) lifetime probability distribution proposed in Afify, A.Z. et al (2017) <doi:10.1214/16-BJPS322>.