Implements the navigated weighting (NAWT) proposed by Katsumata (2020)
    <arXiv:2005.10998>, which improves the inverse probability weighting by 
    utilizing estimating equations suitable for a specific pre-specified parameter 
    of interest (e.g., the average treatment effects or the average treatment 
    effects on the treated) in propensity score estimation. It includes the 
    covariate balancing propensity score proposed by Imai and Ratkovic (2014) 
    <doi:10.1111/rssb.12027>, which uses covariate balancing conditions in 
    propensity score estimation. The point estimate of the parameter of interest
    as well as coefficients for propensity score estimation and their 
    uncertainty are produced using the M-estimation. The same functions can be 
    used to estimate average outcomes in missing outcome cases.

Model-based clustering of high-dimensional non-negative
             data that follow Generalized Negative Binomial distribution. All functions 
             in this package applies to either continuous or integer data. Correlation
            between variables are allowed, while samples are assumed to be independent.

Provides interface to the online basketball data resources such as
      Basketball reference API <https://www.basketball-reference.com/> and helps
      R users analyze basketball data.

Palettes generated from NBA jersey colorways.

Neighbour-balanced designs ensure that no treatment is disadvantaged unfairly by its surroundings. The treatment allocation in these designs is such that every treatment appears equally often as a neighbour with every other treatment. Neighbour Balanced Designs are employed when there is a possibility of neighbour effects from treatments used in adjacent experimental units. In the literature, a vast number of such designs have been developed. This package generates some efficient neighbour balanced block designs which are balanced and partially variance balanced for estimating the contrast pertaining to direct and neighbour effects, as well as provides a function for analysing the data obtained from such trials (Azais, J.M., Bailey, R.A. and Monod, H. (1993). "A catalogue of efficient neighbour designs with border plots". Biometrics, 49, 1252-1261 ; Tomar, J. S., Jaggi, Seema and Varghese, Cini (2005)<DOI: 10.1080/0266476042000305177>. "On totally balanced block designs for competition effects"). This package contains functions named nbbd1(),nbbd2(),nbbd3(),pnbbd1() and pnbbd2() which generates neighbour balanced block designs within a specified range of number of treatment (v). It contains another function named anlys()for performing the analysis of data generated from such trials.