Estimates the per-person conditional standard error of
    measurement (CSEM) under the persons-by-items single-facet
    crossed design of Generalizability Theory, following
    Brennan (1998) <doi:10.1177/014662169802200401>. Implements
    three estimators of the relative error variance (full, large_a,
    uncorrelated) and the closed-form absolute error variance, with
    both analytical and item-resampling bootstrap sampling variances,
    quadratic smoothing of CSEMs on observed score, D-study
    extrapolation, and base-graphics plotting.

Compute and compare conditional
    standard errors of measurement (CSEM) across score distributions
    using methods from classical test theory. Includes approaches for
    smoothing, bootstrapped CSEM, standardized CSEM, CSEM for scale scores,
    and assessment of properties of split-half scores. Also supports
    comparison with global standard errors derived from reliability
    coefficients and graphical visualization of CSEM curves and
    relative precision across observed score ranges. Some of these implemented
    methods are based on work by
    Lord (1955) <doi:10.1002/j.2333-8504.1955.tb00054.x>,
    Feldt and Qualls (1996) <doi:10.1111/j.1745-3984.1996.tb00486.x>,
    McNeish and Dumas (2025) <doi:10.3758/s13428-025-02611-8>.

Mines contiguous sequential patterns in text.

Perform bulk and cell type-specific expression quantitative trait loci mapping with our novel method (Little et al. (2023) <doi:10.1038/s41467-023-38795-w>).

Salmonella enterica is a major cause of bacterial food-borne disease worldwide. Serotype identification is the most commonly used typing method to characterize Salmonella isolates. However, experimental serotyping needs great cost on manpower and resources. Recently, we found that the newly incorporated spacer in the clustered regularly interspaced short palindromic repeat (CRISPR) could serve as an effective marker for typing of Salmonella. It was further revealed by Li et. al (2014) <doi:10.1128/JCM.00696-14> that recognized types based on the combination of two newly incorporated spacer in both CRISPR loci showed high accordance with serotypes. Here, we developed an R package 'CSESA' to predict the serotype based on this finding. Considering it’s time saving and of high accuracy, we recommend to predict the serotypes of unknown Salmonella isolates using 'CSESA' before doing the traditional serotyping.