person(given = "Franco",
family = "Marsico",
role = c("aut", "cre"),
email = "franco.lmarsico@gmail.com",
comment = c(ORCID = "0000-0002-0740-5516"))
Open-source software for computing Likelihood ratios thresholds and error rates in DNA kinship testingi. Marsico FL. et al (2021) <doi:10.1016/j.fsigen.2021.102519>.
forrel, pedtools, plotly, dplyr, highcharter
Franco Marsico [aut, cre] (<https://orcid.org/0000-0002-0740-5516>)

Open-source software for computing Likelihood ratios thresholds and error rates in DNA kinship testing. Marsico FL. et al (2021) <doi:10.1016/j.fsigen.2021.102519>.
forrel, pedtools, plotly, dplyr, highcharter, tidyr

forrel, pedtools, plotly, dplyr, highcharter, tidyr,
DirichletReg, stats

forrel, pedtools, plotly, dplyr, highcharter, tidyverse,
tidyr, DirichletReg, stats

forrel, pedtools, plotly, dplyr, highcharter, tidyr,
tidyverse, DirichletReg, stats

forrel, pedtools, plotly, dplyr, highcharter, tidyr,
tidyverse, DirichletReg, stats, purrr

Multiple Imputation with Sequential Penalized Regression
Generates multivariate imputations using sequential regression with L2 penalty. For more details see Zahid and Heumann (2018) <doi:10.1177/0962280218755574>.

Statistical Analysis of Misreporting on Sensitive Survey
Questions
Enables investigation of the predictors of misreporting on sensitive survey questions through a multivariate list experiment regression method. The method permits researchers to model whether a survey respondent's answer to the sensitive item in a list experiment is different from his or her answer to an analogous direct question.
person("Gregory", "Eady", email = "gregory.eady@gmail.com", role = c("aut", "cre"))

Intuitive Missing Data Imputation Framework
c(person("Tibor", "V. Varga", email = "tirgit@hotmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-2383-699X")),
person("David", "Westergaard", email = "david.westergaard@cpr.ku.dk", role= c("aut"), comment = c(ORCID = "0000-0003-0128-8432")))
Offers a convenient pipeline to test and compare various missing data
imputation algorithms on simulated and real data. The central assumption behind missCompare is that structurally
different datasets (e.g. larger datasets with a large number of correlated variables vs. smaller datasets
with non correlated variables) will benefit differently from different missing data imputation algorithms.
missCompare takes measurements of your dataset and sets up a sandbox to try a curated list of standard and
sophisticated missing data imputation algorithms and compares them assuming custom missingness patterns.
missCompare will also impute your real-life dataset for you after the selection of the best performing algorithm
in the simulations. The package also provides various post-imputation diagnostics and visualizations to help you
assess imputation performance.
Amelia, data.table, dplyr, ggdendro, ggplot2, Hmisc, ltm,
magrittr, MASS, Matrix, mi, mice, missForest, missMDA,
pcaMethods, plyr, rlang, stats, utils, tidyr, VIM
Tibor V. Varga [aut, cre] (<https://orcid.org/0000-0002-2383-699X>),
David Westergaard [aut] (<https://orcid.org/0000-0003-0128-8432>)