Darwinizer: Darwin Core (DwC) Field Names Standardization
The 'shiny' application 'bdDwC' makes biodiversity data field names Darwin Core compatible.
c(
person(
"Povilas", "Gibas",
email = "povilasgibas@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = '0000-0001-5311-6021')),
person(
"Tomer", "Gueta",
email = "tomer.gu@gmail.com",
role = c("aut"),
comment = c(ORCID = '0000-0003-1557-8596')),
person(
"Vijay", "Barve", ,
email = "vijay.barve@gmail.com",
role = c("aut"),
comment = c(ORCID = '0000-0002-4852-2567')),
person(
"Thiloshon", "Nagarajah",
email = "thiloshon@gmail.com",
role = c("aut")),
person(
"Yohay", "Carmel",
email = "yohay@cv.technion.ac.il",
role = c("aut"))
)
shiny, shinyBS, shinydashboard, shinyjs, shinyFiles
Povilas Gibas [aut, cre] (<https://orcid.org/0000-0001-5311-6021>),
Tomer Gueta [aut] (<https://orcid.org/0000-0003-1557-8596>),
Vijay Barve [aut] (<https://orcid.org/0000-0002-4852-2567>),
Thiloshon Nagarajah [aut],
Yohay Carmel [aut]

This package collects functions developed for density estimation, regression and simultaneous confidence bands construction, EM based on data subject to measurement errors.

Algorithms for Binned/Weighted Data Analysis
This package collects functions and algorithms developed
for pre-binned and weighted data analyses.

Functions for density estimation based on
grouped data, and next-generation gene expression
data.

Algorithms developed for binned data analysis,
gene expression data analysis and
measurement error models for ordinal data analysis.

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

This package collects algorithms/functions developed for
density estimation based on weighted or grouped (pre-binned) data.

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

c(
person("Bruno", "Ribeiro", , "ribeiro.brr@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-7755-6715")),
person("Geiziane", "Tessarolo", , "geites@gmail.com", role = "aut",
comment = c(ORCID = "0000-0003-1361-0062")),
person("Santiago", "Velazco", , "sjevelazco@gmail.com", role = "aut",
comment = c(ORCID = "0000-0002-7527-0967")),
person("Lucas", "Jardim", , "lucas.ljardim9@gmail.com", role = "aut",
comment = c(ORCID = "0000-0003-2602-5575")),
person("Karlo", "Guidoni-Martins", , "kguidonimartins@gmail.com", role = "aut",
comment = c(ORCID = "0000-0002-8458-8467")),
person("Rafael", "Loyola", , "rdiasloyola@gmail.com", role = "ctb",
comment = c(ORCID = "0000-0001-5323-2735")),
person("Steven", "Bachman", , "S.Bachman@kew.org ", role = "ctb",
comment = c(ORCID = "0000-0003-1085-6075"))
)
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.
CoordinateCleaner, dplyr, DT, foreach, fs, ggplot2, here,
magrittr, purrr, qs, readr, rgnparser, rnaturalearth, sf (>=
1.0.5), stringdist, stringi, stringr, taxadb (>= 0.1.3),
tibble, tidyselect
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