Offers a gene-based meta-analysis test with filtering to detect gene-environment interactions (GxE) with association data, proposed by Wang et al. (2018) <doi:10.1002/gepi.22115>. It first conducts a meta-filtering test to filter out unpromising SNPs by combining all samples in the consortia data. It then runs a test of omnibus-filtering-based GxE meta-analysis (ofGEM) that combines the strengths of the fixed- and random-effects meta-analysis with meta-filtering. It can also analyze data from multiple ethnic groups.

A comprehensive system for designing and implementing on-farm precision field agronomic trials. You provide field data, tell 'ofpetrial' how to design a trial, and get readily-usable trial design files and a report checks the validity and reliability of the trial design.

Consider a data matrix of n individuals with p variates. The objective general index (OGI)
    is a general index that combines the p variates into a univariate index in order to rank the n
    individuals. The OGI is always positively correlated with each of the variates.
    More details can be found in Sei (2016) <doi:10.1016/j.jmva.2016.02.005>.

Provides tools for processing and analyzing data from the 'O-GlcNAcAtlas' database <https://oglcnac.org/>, 
    as described in Ma (2021) <doi:10.1093/glycob/cwab003>. It integrates 'UniProt' <https://www.uniprot.org/> API calls 
    to retrieve additional information. It is specifically designed for research workflows 
    involving 'O-GlcNAcAtlas' data, providing a flexible and user-friendly interface for customizing 
    and downloading processed results. Interactive elements allow users to easily adjust parameters 
    and handle various biological datasets.

Ordered models such as ordered probit and ordered logit presume that the error variance is constant across observations. In the case that this assumption does not hold estimates of marginal effects are typically biased (Weiss (1997)). This package allows for generalization of ordered probit and ordered logit models by allowing the user to specify a model for the variance. Furthermore, the package includes functions to calculate the marginal effects. Wrapper functions to estimate the standard limited dependent variable models are also included.