Self-Attention algorithm helper functions and demonstration vignettes of increasing depth on how to construct the Self-Attention algorithm, this is based on Dan Jurafsky and James H. Martin (2022, ISBN:978-0131873216) <https://web.stanford.edu/~jurafsky/slp3/> "Speech and Language Processing (3rd ed.)" and Alex Graves (2020) <https://www.youtube.com/watch?v=AIiwuClvH6k> "Attention and Memory in Deep Learning".

Self-Attention algorithm helper functions and demonstration vignettes of increasing depth on how to construct the Self-Attention algorithm, this is based on Vaswani et al. (2017) <doi:10.48550/arXiv.1706.03762>, Dan Jurafsky and James H. Martin (2022, ISBN:978-0131873216) <https://web.stanford.edu/~jurafsky/slp3/> "Speech and Language Processing (3rd ed.)" and Alex Graves (2020) <https://www.youtube.com/watch?v=AIiwuClvH6k> "Attention and Memory in Deep Learning".

Correcting for Attenuation Due to Measurement Error
c(
person("Jonas", "Moss", , "jonas.gjertsen@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-6876-6964")
))
Confidence curves, confidence intervals and p-values for
correlation coefficients corrected for attenuation due to measurement error.
Implements the methods described in Moss (2019, <arxiv:1911.01576>).

Evaluation and comparison of expression data and GRNs
This packagage facilitates evaluation of expression data
sets compendia.  They can either be based on real data or be
the result of a simulation.  Some statics make use of a gene
regulatory network, on which the expression data is based, and
some require the presence of replicates of microarray
experiments.

This package facilitates evaluation of expression data
sets compendia.  They can either be based on real data or be
the result of a simulation.  Some statics make use of a gene
regulatory network, on which the expression data is based, and
some require the presence of replicates of microarray
experiments.

Tools for Attribute VSI CUSUM Control Chart
c(
person("Hanwool", "Kim", email = "stat.hanwool@gmail.com",
role = c("aut", "cre")),
person("Sangyeol", "Lee", email = "sylee@stat.snu.ac.kr",
role = "aut"))
An implementation of tools for design of attribute
variable sampling interval cumulative sum chart.
It currently provides information for monitoring of mean increase such as
average number of sample to signal, average time to signal,
a matrix of transient probabilities, suitable control limits when the data are
(zero inflated) Poisson/binomial distribution.
Functions in the tools can be easily applied to other count processes.
Also, tools might be extended to more complicated cumulative sum control chart.
We leave these issues as our perpetual work.
Hanwool Kim [aut, cre],
Sangyeol Lee [aut]