Packaged: 2021-06-03 16:29:19 UTC; bernardolares

Description: Auxiliary package for better/faster analytics, visualization, data mining, and machine learning
openxlsx, patchwork, pROC, rlang, rpart, rvest (>= 1.0.0),
googlesheets4, quantmod, plotly, rdrop2, rmarkdown, rtweet, tm,
Packaged: 2021-06-07 13:04:24 UTC; bernardolares

openxlsx, patchwork, pROC, rlang, rpart, rpart.plot, rvest,
Suggests: beepr, DALEX, DBI, forecast, googleAuthR, googlesheets4,
knitr, quantmod, plotly, rdrop2, rmarkdown, rtweet, tm,
Packaged: 2021-09-10 13:18:38 UTC; bernardolares

Packaged: 2021-11-16 05:13:48 UTC; bernardolares

Packaged: 2021-12-03 20:11:33 UTC; bernardolares

Title: Analytics & Machine Learning Sidekick
Packaged: 2022-01-31 20:46:20 UTC; bernardolares

Packaged: 2022-02-09 13:50:00 UTC; bernardolares

Packaged: 2022-02-18 19:33:14 UTC; bernardolares

Packaged: 2022-03-24 14:41:08 UTC; bernardolares

Description: Auxiliary package for better/faster analytics, visualization, data mining, and machine
learning tasks. With a wide variety of family functions, like Machine Learning, Data Wrangling,
Exploratory, API, and Scrapper, it helps the analyst or data scientist to get quick and robust
results, without the need of repetitive coding or extensive R programming skills.
Imports: dplyr, ggplot2, h2o, httr, jsonlite, lubridate, openxlsx,
patchwork, pROC, rlang, rpart, rpart.plot, rvest, stringr,
Suggests: beepr, DALEX, googleAuthR, googlesheets4, knitr, quantmod,
Packaged: 2022-04-05 19:24:02 UTC; bernardolares

Packaged: 2022-08-26 20:22:22 UTC; bernardolares

Packaged: 2022-09-08 16:55:43 UTC; bernardolares

Title: Serialization Interface for Large List Objects
Description: Functions to write or append a R list to a file, read or remove elements from it without restoring the whole list.

Description: Functions to write or append a R list to a file, as well as read, remove, modify elements from it without restoring the whole list.

Title: High-Quality Visualizations of Large, High-Dimensional Datasets
Description: Implements the largeVis algorithm (see Tang, et al. (2016)  <https://arxiv.org/abs/1602.00370>) for visualizing very large high-dimensional datasets. Also very fast search for approximate nearest neighbors; outlier detection; and optimized implementations of the HDBSCAN*, DBSCAN and OPTICS clustering algorithms; plotting functions for visualizing the above.
LinkingTo: Rcpp,RcppProgress (>= 0.2.1),RcppArmadillo (>=
Suggests: testthat, covr, knitr, rmarkdown, wesanderson, RColorBrewer,