Imports: tm, quanteda, parallel, glmnet, stringr, english, textstem,

Title: Distributed Representations of Sentences and Documents
person('hiyijian', role = c('ctb', 'cph'), comment = "Code in src/doc2vec"))
Description: Learn vector representations of sentences, paragraphs or documents by using the 'Paragraph Vector' algorithms,
namely the distributed bag of words ('PV-DBOW') and the distributed memory ('PV-DM') model.
The techniques in the package are detailed in the paper "Distributed Representations of Sentences and Documents" by Mikolov et al. (2014), available at <arXiv:1405.4053>.
Suggests: tokenizers.bpe, udpipe (>= 0.8.4)
hiyijian [ctb, cph] (Code in src/doc2vec)

Packaged: 2021-01-21 08:42:35 UTC; jwijffels

Title: Automatic Reporter for Descriptive Analysis
Author: Rui Portocarrero Sarmento, Vera Costa
Description: Generation of a document (.docx file) with descriptive analysis reporting of a selected dataset. REFERENCE: R. Sarmento, V. Costa (2017) <DOI:10.4018/978-1-68318-016-6>.
Imports: officer, nortest, flextable, broom, tictoc, stats, utils,
DescTools, magrittr, dplyr, grDevices, graphics, plotrix,
Packaged: 2020-06-06 18:23:46 UTC; sarmentor

Title: Automatic Reporter for Inference Analysis
Description: Generation of a document (.docx file) with inference analysis reporting of a selected dataset. REFERENCE: R. Sarmento, V. Costa (2017) <DOI:10.4018/978-1-68318-016-6>.
Packaged: 2020-06-06 18:23:39 UTC; sarmentor

Description: Allows accessing 'Docker' 'SDK' from 'R' via the 'Docker' 'Python' 'SDK' using the 'reticulate' package.
This is a very thin wrapper that tries to do very little and get out of the way.
The user is expected to know how to use the 'reticulate' package to access 'Python' modules, and how the 'Docker' 'Python' 'SDK' works.

Authors@R: person('Colin', 'Fay', email = 'contact@colinfay.me', role = c('cre', 'aut'))
Description: Build a Dockerfile straight from your R session. 'dockerfiler' allows
you to create step by step a Dockerfile, and provides convenient tools to wrap