Title: Density Estimation for Grouped Data
Description: Functions for density estimation based on
grouped data, and next-generation gene expression

Description: Algorithms developed for binned data analysis,
measurement error models for ordinal data analysis.

Packaged: 2020-09-30 20:29:20 UTC; liangenwang

Packaged: 2021-01-19 13:41:21 UTC; liangenwang

Packaged: 2021-08-19 00:50:32 UTC; liangenwang

Title: Density Estimation for Binned/Weighted Data
Description: This package collects algorithms/functions developed for
density estimation based on weighted or pre-binned data.

density estimation based on weighted or grouped (pre-binned) data.

Description: Functions for density estimation based on grouped (or pre-binned)

person("Bruno", "Ribeiro", , "ribeiro.brr@gmail.com", role = c("aut", "cre"),
person("Geiziane", "Tessarolo", , "geites@gmail.com", role = "aut",
person("Santiago", "Velazco", , "sjevelazco@gmail.com", role = "aut",
person("Lucas", "Jardim", , "lucas.ljardim9@gmail.com", role = "aut",
person("Karlo", "Guidoni-Martins", , "kguidonimartins@gmail.com", role = "aut",
person("Rafael", "Loyola", , "rdiasloyola@gmail.com", role = "ctb",
person("Steven", "Bachman", , "S.Bachman@kew.org ", role = "ctb",
Description: The bdc package brings together several aspects of
biodiversity data-cleaning in one place. It is organized in thematic
modules related to different biodiversity dimensions, including 1)
Standardization and integration of different datasets; 2) Pre-filter:
flagging and removal of invalid or non-interpretable information,
followed by data amendments; 3) Taxonomy: cleaning, parsing, and
harmonization of scientific names from several taxonomic groups
against taxonomic databases locally stored through the application of
exact and partial matching algorithms; 4) Space: flagging of
erroneous, suspect, and low-precision geographic coordinates; and 5)
Time: flagging and, whenever possible, correction of inconsistent
collection date. In addition, the package contains features to
visualize, document, and report data quality – which is essential for
making data quality assessment transparent and reproducible.
Imports: CoordinateCleaner, dplyr, DT, foreach, fs, ggplot2, here,
magrittr, purrr, qs, readr, rgnparser, rnaturalearth, sf (>=
1.0.5), stringdist, stringi, stringr, taxadb (>= 0.1.3),
Suggests: contentid (>= 0.0.15), countrycode, covr, cowplot, DBI,
doParallel, duckdb (>= 0.3.2), knitr, maps, markdown,
rangeBuilder, rappdirs, raster, remotes, rmarkdown,
rnaturalearthdata, rvest, sp, testthat (>= 3.0.0), xml2
Author: Bruno Ribeiro [aut, cre] (<https://orcid.org/0000-0002-7755-6715>),