FunChisq-deprecated

cp.chisq.test

<p>These functions are provided for compatibility with older versions of package <span class="pkg">FunChisq</span> only, and may be removed eventually.</p>

Statistical hypothesis testing methods for
inferring model-free functional dependency using asymptotic
chi-squared or exact distributions. Functional test
statistics are asymmetric and functionally optimal, unique
from other related statistics. Tests in this package reveal
evidence for causality based on the causality-by-
functionality principle. They include asymptotic functional
chi-squared tests (Zhang & Song 2013) <doi:10.48550/arXiv.1311.2707>,
an adapted functional chi-squared test (Kumar & Song 2022)
<doi:10.1093/bioinformatics/btac206>,
and an exact functional test (Zhong & Song 2019)
<doi:10.1109/TCBB.2018.2809743> (Nguyen et al. 2020)
<doi:10.24963/ijcai.2020/372>. The normalized functional
chi-squared test was used by Best Performer 'NMSUSongLab'
in HPN-DREAM (DREAM8) Breast Cancer Network Inference
Challenges (Hill et al. 2016) <doi:10.1038/nmeth.3773>. A
function index (Zhong & Song 2019)
<doi:10.1186/s12920-019-0565-9> (Kumar et al. 2018)
<doi:10.1109/BIBM.2018.8621502> derived from the
functional test statistic offers a new effect size measure
for the strength of functional dependency, a better
alternative to conditional entropy in many aspects. For
continuous data, these tests offer an advantage over
regression analysis when a parametric functional form
cannot be assumed; for categorical data, they provide a
novel means to assess directional dependency not possible
with symmetrical Pearson's chi-squared or Fisher's exact
tests.

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FunChisq-deprecated: Deprecated Functions in Package FunChisq

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