#' DoOR Functions
#'
#' Functions package providing manipulation and application of the DoOR.
#'
#' \tabular{ll}{ Package: \tab DoOR.functions\cr Type: \tab Package\cr Version:
#' \tab 2.0.0\cr Date: \tab 2016-01-25\cr License: \tab GPL-3\cr LazyLoad: \tab
#' yes\cr }
#'
#' \bold{Type \code{help(package = DoOR.functions)} to see a complete list of
#' datasets and functions. Below is what you need for a quick start.}
#'
#' First, load the DoOR packages, data and function package: \tabular{ll}{
#' \code{library(DoOR.functions)}: \tab \cr \code{library(DoOR.data)}: \tab \cr
#' }
#'
#' then, load all datasets including the precomputed response matrix:
#' \tabular{ll}{ \code{load_door_data}: \tab Load all data into current active
#' environment (function comes with DoOR.data) . \cr } or, load all odorant
#' reseponse data into a list: \tabular{ll}{ \code{\link{load2list}}: \tab Load
#' odorant response data only and compose them as a list. \cr }
#'
#' Try some visualizations (e.g. producing the plots from the paper):
#' \tabular{ll}{ \code{\link{dplot_al_map}}: \tab response to a chemical mapped
#' onto an image of the antennal lobe.\cr \code{\link{dplot_compare_profiles}}:
#' \tab compare the results of two studies. \cr
#' \code{\link{dplot_response_matrix}}: \tab Dot Plot of Odorant Responses
#' Across Receptors. \cr \code{\link{dplot_response_profile}}: \tab bar plot:
#' one receptor, all chemicals. \cr \code{\link{dplot_tuningCurve}}: \tab
#' pyramid diagram depicting a receptor's tuning breadth. \cr } Try some
#' queries: \tabular{ll}{ \code{\link{get_responses}}: \tab given a chemical,
#' get original responses from all studies in the database.\cr
#' \code{\link{get_normalized_responses}}: \tab given a chemical, get normalised
#' responses from all studies in the database.\cr}
#'
#' In case you wish to create your own response model (e.g. because you want to
#' include your own datasets): \tabular{ll}{ \code{\link{create_door_database}}:
#' \tab compute the complete response model for all receptors in the database
#' (calls \code{\link{model_response}} for all receptors). \cr
#' \code{\link{model_response}}: \tab run the DoOR algorithm, that merges all
#' measurements for one receptor. \cr }
#'
#' Estimate odorant responses: \tabular{ll}{
#' \code{\link{estimate_missing_value}}: \tab estimate NA entries in a consensus
#' response data. \cr } Project the model response values back to tested values:
#' \tabular{ll}{ \code{\link{back_project}}: \tab project the model response
#' values back to tested values. \cr }
#'
#' Introduce new data into DoOR and update the supported data sets:
#' \tabular{ll}{ \code{\link{import_new_data}}: \tab import new data into DoOR,
#' and update the weight, response range and receptor names. \cr
#' \code{\link{update_door_database}}: \tab update response matrix by
#' calculating new consensus response data for a given receptor. \cr }
#'
#' See the Vignettes and the help pages for more documentation.
#'
#' @name DoOR.functions.package
#' @aliases DoOR.functions DoOR.function
#' @docType package
#' @author C. Giovanni Galizia \cr Daniel Muench \cr Martin Strauch \cr Anja
#' Nissler \cr Shouwen Ma \cr
#'
#' Maintainer: Daniel Münch <daniel.muench@@uni-konstanz.de>
#' @seealso \code{DoOR.data}
#' @references \url{http://neuro.uni-konstanz.de/DoOR}
#' @keywords package
NULL
## quiets concerns of R CMD check re: the .'s that appear in pipelines
if (getRversion() >= "2.15.1")
utils::globalVariables(
c(
".",
"dataset",
"door_AL_map",
"door_data_format",
"door_excluded_data",
"door_global_normalization_weights",
"door_mappings",
"door_response_matrix",
"door_response_matrix_non_normalized",
"door_response_range",
"glomerulus",
"group",
"n",
"odor.dist",
"odorant",
"odorants",
"odor",
"ORs",
"OSN",
"receptors",
"response",
"sensillum",
"value",
"values",
"x"
)
)
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