c(
person("Mohieddin", "Jafari", role = c("aut"),email = "mohieddin.jafari@helsinki.fi"),
person("Cheng", "Chen", role = c("aut","cre"),email = "cheng.chen@helsinki.fi")
)
Functions for nominal data mining based on bipartite graphs, which build a pipeline for analysis and missing values imputation. Methods are mainly from the paper: Jafari, Mohieddin, et al. (2021) <doi:10.1101/2021.03.18.436040>, some new ones are also included.
knitr, utils, rmarkdown, htmltools, testthat (>= 3.0.0)
plotly, tidyr, bipartite, crayon, dplyr, ggplot2, igraph,
purrr, skimr, bnstruct, RColorBrewer, fpc, mice, missMDA,
networkD3, scales, softImpute, tibble, tidytext, visNetwork,
stats
Mohieddin Jafari [aut],
Cheng Chen [aut, cre]

c(
person("Mohieddin", "Jafari", role = c("aut","cre"),email = "mohieddin.jafari@helsinki.fi"),
person("Cheng", "Chen", role = c("aut"),email = "cheng.chen@helsinki.fi")
)
Mohieddin Jafari [aut, cre],
Cheng Chen [aut]

Fast and Accurate Trisomy Prediction in Non-Invasive Prenatal
Testing
Fast and accurate trisomy prediction in non-invasive prenatal
testing.

Fast and Accurate Trisomy Prediction in Non-Invasive Prenatal
Testing.

Novel Statistical Methods for Studying Near-Infrared
Spectroscopy (NIRS) Time Series Data
Provides transfusion-related differential tests on Near-infrared spectroscopy (NIRS) time series with detection limit, which contains two testing statistics: Mean Area Under the Curve (MAUC) and slope statistic. This package applied a penalized spline method within an imputation setting. Testing is conducted by a nested permutation approach within imputation. Refer to Guo et al (2018) <arXiv:1801.08153> for further details.

Yikai Wang [Emory University], Xiao Wang [ICF]
Provides transfusion-related differential tests on Near-infrared spectroscopy (NIRS) time series with detection limit, which contains two testing statistics: Mean Area Under the Curve (MAUC) and slope statistic. This package applied a penalized spline method within imputation setting. Testing is conducted by a nested permutation approach within imputation. Refer to Guo et al (2018) <doi:10.1177/0962280218786302> for further details.