Tools designed to make it easier for users, particularly beginner/intermediate R users 
    to build ordinary least squares regression models. Includes comprehensive regression output, 
    heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence, 
    model fit assessment and variable selection procedures.

The 'OLStrajr' package provides comprehensive functions for ordinary
    least squares (OLS) trajectory analysis and case-by-case OLS regression as
    outlined in Carrig, Wirth, and Curran (2004) <doi:10.1207/S15328007SEM1101_9>
    and Rogosa and Saner (1995) <doi:10.3102/10769986020002149>. It encompasses two
    primary functions, OLStraj() and cbc_lm(). The OLStraj() function simplifies
    the estimation of individual growth curves over time via OLS regression, with
    options for visualizing both group-level and individual-level growth trajectories
    and support for linear and quadratic models. The cbc_lm() function facilitates
    case-by-case OLS estimates and provides unbiased mean population intercept and
    slope estimators by averaging OLS intercepts and slopes across cases. It further
    offers standard error calculations across bootstrap replicates and computation
    of 95% confidence intervals based on empirical distributions from the resampling
    processes.

'Shiny' Application to visualize Olympic Data. From 1896 to
    2016. Even Winter Olympics events are included. Data is from Kaggle at
    <https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results>.

It is a computer tool to estimate the item-sum score's reliability (composite reliability, CR) in multidimensional scales with overlapping items. An item that measures more than one domain construct is called an overlapping item. 
    The estimation is based on factor models allowing unlimited cross-factor loadings such as exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM). The factor models include correlated-factor models and bi-factor models. Specifically for bi-factor models, a type of hierarchical factor model, the package estimates the CR hierarchical subscale/hierarchy and CR subscale/scale total. The CR estimator 'Omega-generic' was proposed by Mai, Srivastava, and Krull (2021) <https://whova.com/embedded/subsession/enars_202103/1450751/1452993/>. The current version can only handle continuous data. 
    Yujiao Mai contributes to the algorithms, R programming, and application example. Deo Kumar Srivastava contributes to the algorithms and the application example. Kevin R. Krull contributes to the application example. The package 'OmegaG' was sponsored by American Lebanese Syrian Associated Charities (ALSAC). However, the contents of 'OmegaG' do not necessarily represent the policy of the ALSAC.