Title: Missing Person Identification Tools
Description: 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>.
Imports: forrel, pedtools, plotly, dplyr, highcharter
Author: Franco Marsico [aut, cre] (<https://orcid.org/0000-0002-0740-5516>)

Description: 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>.
Imports: forrel, pedtools, plotly, dplyr, highcharter, tidyr

Imports: forrel, pedtools, plotly, dplyr, highcharter, tidyr,

Imports: forrel, pedtools, plotly, dplyr, highcharter, tidyverse,

Title: Multiple Imputation with Sequential Penalized Regression
Maintainer: Faisal Maqbool Zahid <faisalmz99@yahoo.com>
Description: Generates multivariate imputations using sequential regression with L2 penalty. For more details see Zahid and Heumann (2018) <doi:10.1177/0962280218755574>.

Title: Statistical Analysis of Misreporting on Sensitive Survey
Description: 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.
Authors@R: person("Gregory", "Eady", email = "gregory.eady@gmail.com", role = c("aut", "cre"))
Packaged: 2016-12-05 12:56:39 UTC; gregoryeady

Packaged: 2017-02-27 04:20:39 UTC; gregoryeady

Package: missCompare
Type: Package
Title: Intuitive Missing Data Imputation Framework
Version: 1.0.1
Authors@R: 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")))
Maintainer: Tibor V. Varga <tirgit@hotmail.com>
Description: 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.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
BugReports: https://github.com/Tirgit/missCompare/issues
Depends: R (>= 3.5.0)
biocViews:
Imports: Amelia, data.table, dplyr, ggdendro, ggplot2, Hmisc, ltm,
magrittr, MASS, Matrix, mi, mice, missForest, missMDA,
pcaMethods, plyr, rlang, stats, utils, tidyr, VIM
Suggests: testthat, knitr, rmarkdown, devtools
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-01-31 07:55:03 UTC; med-tv_
Author: Tibor V. Varga [aut, cre] (<https://orcid.org/0000-0002-2383-699X>),
David Westergaard [aut] (<https://orcid.org/0000-0003-0128-8432>)
Repository: CRAN
Date/Publication: 2019-02-05 22:22:07 UTC