To construct a model in 2-D space from 2-D nonlinear
    dimension reduction data and then lift it to the high-dimensional
    space. Additionally, provides tools to visualise the model overlay the
    data in 2-D and high-dimensional space. Furthermore, provides
    summaries and diagnostics to evaluate the nonlinear dimension
    reduction layout.

Collect  your data on digital marketing campaigns from 'Quora' Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.

A facility for writing functions that quote their arguments,
 may sometimes evaluate them in the environment where they were quoted,
 and may pass them as quoted to other functions.

Full text, in data frames containing one row per verse, of the 
    Qur'an in Arabic (with and without vowels) and in English (the Yusuf Ali 
    and Saheeh International translations), formatted to be convenient for 
    text analysis.

High-throughput analysis of growth curves and fluorescence
    data using three methods: linear regression, growth model fitting, and
    smooth spline fit. Analysis of dose-response relationships via
    smoothing splines or dose-response models. Complete data analysis
    workflows can be executed in a single step via user-friendly wrapper
    functions. The results of these workflows are summarized in detailed
    reports as well as intuitively navigable 'R' data containers. A 'shiny'
    application provides access to all features without
    requiring any programming knowledge. The package is described in further
    detail in Wirth et al. (2023) <doi:10.1038/s41596-023-00850-7>.

Provide a variety of Q-matrix validation methods for the generalized
    cognitive diagnosis models, including the method based on the generalized
    deterministic input, noisy, and gate model (G-DINA) by de la Torre (2011)
    <DOI:10.1007/s11336-011-9207-7> discrimination index (the GDI method) by
    de la Torre and Chiu (2016) <DOI:10.1007/s11336-015-9467-8>, the Hull method
    by Najera et al. (2021) <DOI:10.1111/bmsp.12228>, the stepwise Wald test method
    (the Wald method) by Ma and de la Torre (2020) <DOI:10.1111/bmsp.12156>, the
    multiple logistic regression‑based Q‑matrix validation method (the MLR-B method)
    by Tu et al. (2022) <DOI:10.3758/s13428-022-01880-x>, the beta method based on
    signal detection theory by Li and Chen (2024) <DOI:10.1111/bmsp.12371> and
    Q-matrix validation based on relative fit index by Chen et al. (2013)
    <DOI:10.1111/j.1745-3984.2012.00185.x>. Different research methods and iterative
    procedures during Q-matrix validating are available
    <DOI:10.3758/s13428-024-02547-5>.