Description: Short for 'linear binning', the linbin package provides functions
for manipulating, binning, and plotting linearly referenced data. Although
developed for data collected on river networks, it can be used with any interval
or point data referenced to a 1-dimensional coordinate system. Flexible bin
generation and batch processing makes it easy to compute and visualize variables
at multiple scales, useful for identifying patterns within and between variables
and investigating the influence of scale of observation on data interpretation.

Title: Sampling Multivariate Normal Distribution under Linear
Authors@R: c(person("Yunyi", "Shen", email = "yshen99@wisc.edu", role = c("aut", "cre"),comment = c(ORCID = "0000-0003-2779-6507")))
Description: Sample truncated multivariate Normal distribution following Gessner, A., Kanjilal, O., & Hennig, P. (2019). Integrals over Gaussians under Linear Domain Constraints. 108. <arxiv:1910.09328>.
Author: Yunyi Shen [aut, cre] (<https://orcid.org/0000-0003-2779-6507>)

Title: Automated Linear Regression Diagnostic
Description: Provides a set of streamlined functions that allow
easy generation of linear regression diagnostic plots necessarily
This package is meant for easy scheming of linear
regression diagnostics, while preserving merits of
"The Grammar of Graphics" as implemented in 'ggplot2'.
See the 'ggplot2' website for more information regarding the
Packaged: 2017-05-10 02:16:28 UTC; edwardlee
Maintainer: Yeuk Yu Lee <lyeukyu@gmail.com>

person("Claudio", "Paladini", , "clode.paladini@gmail.com", role = c("aut", "cre"),
Description: An interface to accelerate linear regression (Ordinary least squares) modelling, which
allows users to build models quickly, while automatically generating interactive visualizations of the results.
Non-linear models specification (e.g. y = b1*x1 + b2*log(x2)) can be easily constructed using user-defined transformations.
Functions for testing wide ranges of model specifications (e.g. y = b*log(x,10) , y = b*log(x,20) , ...), all at once, are also available.
Finally, models can be imported and exported as Excel files where all the information necessary for re-running the models is stored in separate sheets.
Imports: anytime, dplyr , gtrendsR, lubridate , magrittr , methods ,
openxlsx , plotly , purrr , RColorBrewer , readr , readxl ,
reshape2 , rlist, sjmisc , stringr , tibble , tidyr , tidyverse
Author: Claudio Paladini [aut, cre] (<https://orcid.org/0000-0002-5551-1380>)