A toolkit for the analysis and management of data for genes in the so-called "Human Leukocyte Antigen" (HLA) region. Functions extract reference data from the Anthony Nolan HLA Informatics Group/ImmunoGeneTics HLA 'GitHub' repository (ANHIG/IMGTHLA) <https://github.com/ANHIG/IMGTHLA>, validate Genotype List (GL) Strings, convert between UNIFORMAT and GL String Code (GLSC) formats, translate HLA alleles and GLSCs across ImmunoPolymorphism Database (IPD) IMGT/HLA Database release versions, identify differences between pairs of alleles at a locus, generate customized, multi-position sequence alignments, trim and convert allele-names across nomenclature epochs, and extend existing data-analysis methods.

Provides access to datasets published by 'Hlídač státu' <https://www.hlidacstatu.cz/>, 
    a Czech watchdog, via their API.

A suite of diagnostic tools for hierarchical
    (multilevel) linear models. The tools include
    not only leverage and traditional deletion diagnostics (Cook's
    distance, covratio, covtrace, and MDFFITS) but also 
    convenience functions and graphics for residual analysis. Models
    can be fit using either lmer in the 'lme4' package or lme in the 'nlme' package.

Fits latent space models for single networks and hierarchical latent space models for ensembles of networks as described in Sweet, Thomas & Junker (2013).

Higher-order latent trait theory (item response theory). We
    implement the generalized partial credit model with a second-order latent
    trait structure. Latent regression can be done on the second-order latent
    trait. For a pre-print of the methods,
    see, "Latent Regression in Higher-Order Item Response Theory with the R
    Package hlt" <https://mkleinsa.github.io/doc/hlt_proof_draft_brmic.pdf>.

Provides interpretable high-dimensional mean comparison methods (HMC). 
    For example, users can apply these methods to assess the difference in 
    gene expression between two treatment groups. It is not a gene-by-gene 
    comparison. Instead, the methods focus on the interplay between features 
    and identify those that are predictive of the group label. The tests are 
    valid frequentist procedures and yield sparse estimates indicating which 
    features contribute to the group differences.