Regression Analysis and Forecasting Using Textual Data from a Time-Varying Dictionary

Provides functionalities based on the paper "Time Varying Dictionary and the Predictive Power of FED Minutes" (Lima, 2018) . It selects the most predictive terms, that we call time-varying dictionary using supervised machine learning techniques as lasso and elastic net.


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The goal of TextForecast is to carry out forecasting and regression analysis using textual analysis and supervised machine learning techniques as LASSO, Elastic Net and Ridge Regression to select the most predictive words/terms.

Installation

You can install the released version of TextForecast from github with:

install.packages("TextForecast")

News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("TextForecast")

0.1.2 by Lucas Godeiro, a month ago


https://github.com/lucasgodeiro/TextForecast


Report a bug at https://github.com/lucasgodeiro/TextForecast/issues


Browse source code at https://github.com/cran/TextForecast


Authors: Luiz Renato Lima [aut] , Lucas Godeiro [aut, cre]


Documentation:   PDF Manual  


GPL-3 license


Imports stats, tidyr, tidytext, tm, wordcloud, dplyr, plyr, udpipe, RColorBrewer, ggplot2, glmnet, pdftools, parallel, doParallel, pracma, forcats, Matrix

Suggests knitr, rmarkdown, covr


See at CRAN