Uses a novel rank-based nonparametric approach to evaluate a surrogate marker in a small sample size setting. Details are described in Parast et al (2024) <doi:10.1093/biomtc/ujad035>.

Performs estimation and inference on a partially missing target outcome (e.g. gene expression in an inaccessible tissue) while borrowing information from a correlated surrogate outcome (e.g. gene expression in an accessible tissue). Rather than regarding the surrogate outcome as a proxy for the target outcome, this package jointly models the target and surrogate outcomes within a bivariate regression framework. Unobserved values of either outcome are treated as missing data. In contrast to imputation-based inference, no assumptions are required regarding the relationship between the target and surrogate outcomes. Estimation in the presence of bilateral outcome missingness is performed via an expectation conditional maximization either algorithm. In the case of unilateral target missingness, estimation is performed using an accelerated least squares procedure. A flexible association test is provided for evaluating hypotheses about the target regression parameters. For additional details, see: McCaw ZR, Gaynor SM, Sun R, Lin X: "Leveraging a surrogate outcome to improve inference on a partially missing target outcome" <doi:10.1111/biom.13629>.

To assess and compare the models' goodness of fit, R-squared is one of the most 
              popular measures. For categorical data analysis, however, no universally adopted 
              R-squared measure can resemble the ordinary least square (OLS) R-squared for 
              linear models with continuous data. This package implement the surrogate R-squared
              measure for categorical data analysis, which is proposed in the study of Dungang Liu,
              Xiaorui Zhu, Brandon Greenwell, and Zewei Lin (2022) <doi:10.1111/bmsp.12289>. It
              can generate a point or interval measure of the surrogate R-squared. It can also 
              provide a ranking measure of the percentage contribution of each variable to the 
              overall surrogate R-squared. This ranking assessment allows one to check the 
              importance of each variable in terms of their explained variance. This package can
              be jointly used with other existing R packages for variable selection and model 
              diagnostics in the model-building process.