Title: Personalizes and Randomizes Exams Written in 'LaTeX'
Maintainer: Alejandro Gonzalez Recuenco <alejandrogonzalezrecuenco@gmail.com>
Description: Randomizing exams with 'LaTeX'.
If you can compile your main document with 'LaTeX', the program should be able to compile the randomized
versions without much extra effort when creating the document.
Imports: Rcpp (>= 0.12.13), assertthat, stringr, jsonlite, stats, utils

SystemRequirements: C++11, A modern compiler (>=gcc-4.9), And latexmk
is necessary to compile all output documents with the functions

Title: Supporting Functions and Data for Geo-Spatial Analytics Courses
Authors@R: c(person("Michael", "Tiefelsdorf", email="tiefelsdorf@utdallas.edu", role=c("aut","cre")),
Description: A collection of functions and data - mostly from Texas - is provided.
These are used as teaching tools for geo-spatial data analytics courses in the
GISciences program at The University of Texas at Dallas. In addition, several
vignettes illustrate geo-spatial data analytics practices, such as relative
risk kernel density estimations based on food store locations within Dallas
County or the identification of homogenous and spatially contiguous market areas
built on socio-economic, demographic and infrastructure census information. The
spatial resolution of the data-sets ranges from 1623 food store locations, over
geo-referenced areal data of 258 Texan counties, to 529 census tracts as well
as 1669 block groups in Dallas County. Cartographic, specialized regression and
Imports: sp, maptools, RColorBrewer, classInt, Formula
Suggests: spatstat, smacpod, colorspace, car, effects, rgdal, spdep,
DMwR, ClustGeo, knitr, rmarkdown, testthat

Title: Regression Analysis and Forecasting Using Textual Data from a
person("Luiz Renato", "Lima", email = "llima@utk.edu", role = "aut"),
person("Lucas", "Godeiro", email = "lucas.godeiro@hotmail.com", role = c("aut","cre")))
Description: Provides functionalities based on the paper "Time Varying Dictionary and the Predictive Power of FED Minutes" (Lima, 2018) <doi:10.2139/ssrn.3312483>. It selects the most predictive terms, that we call time-varying dictionary using unsupervised machine learning techniques as lasso and elastic net.
Imports: SnowballC, forecast, rpart, stats, text2vec, tidyr, tidytext,
tm, tsDyn, wordcloud, dplyr, plyr, udpipe, class, lars, tau,
RColorBrewer, forcats, ggplot2, glmnet, pdftools, parallel,
Packaged: 2019-02-07 18:06:38 UTC; lucasgodeirolucas