Authors@R: c(person("Folashade", "Daniel", role="cre", email = "fdaniel@microsoft.com"),
Packaged: 2022-01-16 18:34:00 UTC; folashade

Title: Dimension Reduction for Outlier Detection
Description: A dimension reduction technique for outlier detection. DOBIN: a Distance
based Outlier BasIs using Neighbours, constructs a set of basis vectors for outlier
detection. This is not an outlier detection method; rather it is a pre-processing
method for outlier detection. It brings outliers to the fore-front using fewer basis
Suggests: knitr, rmarkdown, OutliersO3, mbgraphic, ggplot2

Suggests: knitr, rmarkdown, OutliersO3, ggplot2

Suggests: knitr, rmarkdown, OutliersO3, ggplot2, FNN

Suggests: knitr, rmarkdown, OutliersO3, FNN

Title: Data from the GLM Book by Dobson and Barnett
person("Adrian", "Barnett", , "a.barnett@qut.edu.au", role = c("aut", "cre"))
Description: Example datasets from the book "An Introduction to Generalised Linear Models" (Year: 2008, ISBN:9781584889502) by Dobson and Barnett. The data were read into R from the 'Excel' files available here: <https://www.crcpress.com/downloads/C9500/GLM_data.zip>.

Description: Example datasets from the book "An Introduction to Generalised Linear Models" (Year: 2018, ISBN:9781138741515) by Dobson and Barnett.

Title: Measuring Concreteness in Natural Language
Description: Models for detecting concreteness in natural language. This package is built in support of Yeomans (2020) <doi:10.17605/OSF.IO/DYZN6>, which reviews linguistic models of concreteness in several domains. Here, we provide an implementation of the best-performing domain-general model (from Brysbaert et al., (2014) <doi:10.3758/s13428-013-0403-5>) as well as two pre-trained models for the feedback and plan-making domains.
Imports: tm, quanteda, ggplot2, parallel, glmnet, stringr, dplyr,

Description: Models for detecting concreteness in natural language. This package is built in support of Yeomans (2021) <doi:10.1016/j.obhdp.2020.10.008>, which reviews linguistic models of concreteness in several domains. Here, we provide an implementation of the best-performing domain-general model (from Brysbaert et al., (2014) <doi:10.3758/s13428-013-0403-5>) as well as two pre-trained models for the feedback and plan-making domains.