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#' A Package for Fast and Stable Estimation of the Probability of Informed Trading (PIN)
#'
#' Utilities for fast and stable
#' estimation of the probability of informed trading (PIN) in the model introduced by Easley, Hvidkjaer and O'Hara (EHO, 2002) are implemented.
#' Since the model developed by Easley, Kiefer, O'Hara and Paperman (EKOP, 1996) is nested in the EHO model due to
#' equating the intensity of uninformed buys and sells, functionalities can also be applied
#' to this simpler model structure, if needed.
#' State-of-the-art factorization of the model likelihood function as well as hierarchical agglomerative clustering algorithm
#' for generating initial values for optimization routines are provided.
#' In total, two different likelihood factorizations and three methodologies generating starting values are implemented.
#' The probability of informed trading can be estimated for arbitrary length of daily buys and sells data with
#' \code{\link{pin_est}} function which is a wrapper around the workhorse function \code{\link{pin_est_core}}.
#' No information about the time span of the underlying data is required to perform optimizations.
#' However, recommendation given in the literature is using at least data for 60 trading days to ensure convergence of the
#' likelihood maximization.
#' The \code{\link{qpin}} function delivers quarterly estimates.
#' The number of available quarters in the data are detected utilizing functions from the \code{\link[lubridate]{lubridate}} package.
#' Quarterly estimates can be visualized with the \code{\link[ggplot2]{ggplot}} function.
#' Datasets of daily aggregated numbers of buys and sells can be simulated with \code{\link{simulateBS}}.
#' Calculation of confidence intervals for the probability of informed trading can be enabled by \code{confint} argument in
#' optimization routines (\code{\link{pin_est_core}}, \code{\link{pin_est}} and \code{\link{qpin}}) or by calling \code{\link{pin_confint}} directly.
#' Additionally, posterior probabilities for conditions of trading days can be computed with \code{\link{posterior}} and
#' plotted with \code{\link[ggplot2]{ggplot}}.
#'
#' @section Functions:
#' \describe{
#' \item{\code{\link{ggplot.posterior}}}{Visualization method for results of \code{\link{posterior}} with ggplot2.}
#' \item{\code{\link{ggplot.qpin}}}{Visualization method for results of \code{\link{qpin}} with ggplot2.}
#' \item{\code{\link{initial_vals}}}{Generating initial values by brute force grid search, hierarchical agglomerative clustering algorithm or
#' refined hierarchical agglomerative clustering technique.}
#' \item{\code{\link{posterior}}}{Calculation of posterior probabilities of trading days' conditions.}
#' \item{\code{\link{pin_calc}}}{Computing the probability of informed trading (PIN).}
#' \item{\code{\link{pin_confint}}}{Calculation of confidence intervals for the probability of informed trading.}
#' \item{\code{\link{pin_est_core}}}{Core function of maximization routines for PIN likelihood function. It grants the most control over optimization procedure.
#' However, the settings chosen in \code{\link{pin_est}} will be sufficient in most applications.}
#' \item{\code{\link{pin_est}}}{User-friendly wrapper around \code{\link{pin_est_core}}. Default method for creating initial values is set to
#' hierarchical agglomerative clustering, the likelihood formulation defaults to the one proposed by
#' Lin and Ke (2011).}
#' \item{\code{\link{pin_ll}}}{Evaluating likelihood function values either utilizing the factorization by Easley et. al (2010) or
#' Lin and Ke (2011).}
#' \item{\code{\link{qpin}}}{Returns quarterly estimates, function is a wrapper around \code{\link{pin_est}} and
#' inherits its optimization settings.}
#' \item{\code{\link{simulateBS}}}{Simulate datasets of aggregated daily buys and sells.}
#' }
#'
#' @section Datasets:
#' \describe{
#' \item{\code{\link{BSinfrequent}}}{A matrix containing three months of synthetic daily buys and sells data representing an infrequently traded stock.}
#' \item{\code{\link{BSfrequent}}}{A matrix containing three months of synthetic daily buys and sells data representing a frequently traded stock.}
#' \item{\code{\link{BSheavy}}}{A matrix containing three months of synthetic daily buys and sells data representing a heavily traded stock.}
#' \item{\code{\link{BSfrequent2015}}}{A matrix containing one year of synthetic daily buys and sells data representing a frequently traded stock.
#' Rownames equal the business days in 2015.}
#' }
#' Source of all included datasets: own simulation
#'
#' @section Author:
#' Andreas Recktenwald (Saarland University, Statistics & Econometrics) \cr
#' Email: \email{a.recktenwald@@mx.uni-saarland.de}
#'
#' @section Github:
#' \url{https://github.com/anre005/pinbasic}
#'
#' @section References:
#' Easley, David et al. (2002) \cr
#' Is Information Risk a Determinant of Asset Returns? \cr
#' \emph{The Journal of Finance}, Volume 57, Number 5, pp. 2185 - 2221 \cr
#' \doi{10.1111/1540-6261.00493}
#'
#' Easley, David et al. (1996) \cr
#' Liquidity, Information, and Infrequently Traded Stocks\cr
#' \emph{The Journal of Finance}, Volume 51, Number 4, pp. 1405 - 1436 \cr
#' \doi{10.1111/j.1540-6261.1996.tb04074.x}
#'
#' Easley, David et al. (2010) \cr
#' Factoring Information into Returns \cr
#' \emph{Journal of Financial and Quantitative Analysis}, Volume 45, Issue 2, pp. 293 - 309 \cr
#' \doi{10.1017/S0022109010000074}
#'
#' Ersan, Oguz and Alici, Asli (2016) \cr
#' An unbiased computation methodology for estimating the probability of informed trading (PIN) \cr
#' \emph{Journal of International Financial Markets, Institutions and Money}, Volume 43, pp. 74 - 94 \cr
#' \doi{10.1016/j.intfin.2016.04.001}
#'
#' Gan, Quan et al. (2015) \cr
#' A faster estimation method for the probability of informed trading
#' using hierarchical agglomerative clustering \cr
#' \emph{Quantitative Finance}, Volume 15, Issue 11, pp. 1805 - 1821 \cr
#' \doi{10.1080/14697688.2015.1023336}
#'
#' Grolemund, Garett and Wickham, Hadley (2011) \cr
#' Dates and Times Made Easy with lubridate \cr
#' \emph{Journal of Statistical Software}, Volume 40, Issue 3, pp. 1 - 25 \cr
#' \doi{10.18637/jss.v040.i03}
#'
#' Lin, Hsiou-Wei William and Ke, Wen-Chyan (2011) \cr
#' A computing bias in estimating the probability of informed trading \cr
#' \emph{Journal of Financial Markets}, Volume 14, Issue 4, pp. 625 - 640 \cr
#' \doi{10.1016/j.finmar.2011.03.001}
#'
#' Wickham, Hadley (2009) \cr
#' ggplot2: Elegant Graphics for Data Analysis \cr
#' \emph{Springer-Verlag New York} \cr
#' \doi{10.1007/978-0-387-98141-3}
#'
#' Wickham, Hadley (2007) \cr
#' Reshaping Data with the reshape Package \cr
#' \emph{Journal of Statistical Software}, Volume 21, Issue 12, pp. 1 - 20 \cr
#' \doi{10.18637/jss.v021.i12}
#'
#' Wickham, Hadley (2016) \cr
#' scales: Scale Functions for Visualization \cr
#' \emph{R package version 0.4.0}
#'
#' Yan, Yuxing and Zhang, Shaojun (2012) \cr
#' An improved estimation method and empirical properties of the probability of informed trading \cr
#' \emph{Journal of Banking & Finance}, Volume 36, Issue 2, pp. 454 - 467 \cr
#' \doi{10.1016/j.jbankfin.2011.08.003}
#'
#' @docType package
#' @name pinbasic
#' @importFrom Rcpp sourceCpp
#' @useDynLib pinbasic
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