An extension to the Regression Modeling Strategies package that
    facilitates plotting ordinal regression  model predictions together with
    confidence intervals for each dependent variable level. 
    It also adds a functionality to plot the  model summary as a modifiable 
    object.

Data input/output functions for data that conform to the
    Digital Imaging and Communications in Medicine (DICOM) standard, part
    of the Rigorous Analytics bundle.

Functions for the input/output and visualization of
    medical imaging data that follow either the 'ANALYZE', 'NIfTI' or 'AFNI'
    formats.  This package is part of the Rigorous Analytics bundle.

Image analysis techniques for positron emission tomography
    (PET) that form part of the Rigorous Analytics bundle.

A data set package with the "Orsi" and "Park/Durand" fronts as
    'SpatialLinesDataFrame' objects. The Orsi et al. (1995) fronts are published at
    the Southern Ocean Atlas Database Page, and the Park et al. (2019) fronts are published at the 'SEANOE' 
    Altimetry-derived Antarctic Circumpolar Current fronts page, please see package CITATION for details.

Convert odds ratio to relative risk in cohort studies with partial data information (Wang (2013) <doi:10.18637/jss.v055.i05>).

A modified version of alternating logistic regressions (ALR) with estimation based on orthogonalized residuals (ORTH) is implemented, which use paired estimating equations to jointly estimate parameters in marginal mean and within-association models. The within-cluster association between ordinal responses is modeled by global pairwise odds ratios (POR). A finite-sample bias correction is provided to POR parameter estimates based on matrix multiplicative adjusted orthogonalized residuals (MMORTH) for correcting estimating equations, and different bias-corrected variance estimators such as BC1, BC2, and BC3.

Tools to analyze and infer orthology and paralogy relationships between glutamine synthetase proteins in seed plants.

Utilize an orthogonality constrained optimization algorithm of
    Wen & Yin (2013) <DOI:10.1007/s10107-012-0584-1> to solve a variety of
    dimension reduction problems in the semiparametric framework, such as
    Ma & Zhu (2012) <DOI:10.1080/01621459.2011.646925>, Ma & Zhu (2013) 
    <DOI:10.1214/12-AOS1072>, Sun, Zhu, Wang & Zeng (2019) <DOI:10.1093/biomet/asy064>
    and Zhou, Zhu & Zeng (2021) <DOI:10.1093/biomet/asaa087>. The package also
    implements some existing dimension reduction methods such as hMave by Xia, Zhang,
    & Xu (2010) <DOI:10.1198/jasa.2009.tm09372> and partial SAVE by Feng, Wen & Zhu (2013)
    <DOI:10.1080/01621459.2012.746065>. It also serves as a general purpose 
    optimization solver for problems with orthogonality constraints, i.e., in Stiefel 
    manifold. Parallel computing for approximating the gradient is enabled through 'OpenMP'.