Maps of France in 1830, multivariate datasets from A.-M. Guerry and others, and statistical and 
	graphic methods related to Guerry's "Moral Statistics of France". The goal is to facilitate the exploration and
	development of statistical and graphic methods for multivariate data in a geospatial context of historical interest.

Real-time quantitative polymerase chain reaction (qPCR) data
    by Guescini et al. (2008) <doi:10.1186/1471-2105-9-326> in tidy format.
    This package provides two data sets where the amplification efficiency has
    been modulated: either by changing the amplification mix concentration, or
    by increasing the concentration of IgG, a PCR inhibitor.
    Original raw data files:
    <https://static-content.springer.com/esm/art%3A10.1186%2F1471-2105-9-326/MediaObjects/12859_2008_2311_MOESM1_ESM.xls> and
    <https://static-content.springer.com/esm/art%3A10.1186%2F1471-2105-9-326/MediaObjects/12859_2008_2311_MOESM5_ESM.xls>.

Provides tools to adjust estimates of learning for guessing-related
    bias in educational and survey research. Implements standard guessing
    correction methods and latent class models that leverage informative
    pre-post test transitions to account for guessing behavior.
    The package helps researchers obtain more accurate estimates of actual
    learning when respondents may guess on closed-ended knowledge items.
    For theoretical background and empirical validation, see Cor and Sood (2016)
    <https://gsood.com/research/papers/guess.pdf>.

We consider the ultrahigh-dimensional and error-prone data. Our goal aims to estimate the precision matrix and identify the graphical structure of the random variables with measurement error corrected. We further adopt the estimated precision matrix to the linear discriminant function to do classification for multi-label classes.

Guided partial least squares (guided-PLS) is the combination of partial least squares by singular value decomposition (PLS-SVD) and guided principal component analysis (guided-PCA). This package provides implementations of PLS-SVD, guided-PLS, and guided-PCA for supervised dimensionality reduction. The guided-PCA function (new in v1.1.0) automatically handles mixed data types (continuous and categorical) in the supervision matrix and provides detailed contribution analysis for interpretability. For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/guidedPLS>.