qcr: Quality Control Review

##' Quality Control Review
##' 
##' Univariate and multivariate SQC tools that completes and increases
##' the SQC techniques available in R. Apart from integrating different R packages devoted to SQC
##' ('qcc','MSQC'), provides nonparametric tools that are highly useful when Gaussian assumption is not
##' met. This package computes standard univariate control charts for individual measurements, X-bar,
##' S, R, p, np, c, u, EWMA and CUSUM. In addition, it includes functions to perform multivariate
##' control charts such as Hotelling T2, MEWMA and MCUSUM. As representative feature, multivariate
##' nonparametric alternatives based on data depth are implemented in this package: r, Q and S control
##' charts. In addition, Phase I and II control charts for functional data are included. This package also allows 
##' the estimation of the most complete set of capability indices from first to fourth generation, covering the
##' nonparametric alternatives, and performing the corresponding capability analysis graphical outputs,
##'including the process capability plots.

##' @name qcr
##' @aliases qcr
##' @docType package
##' @title Quality Control Review 
##' @import qcc
##' @import fda.usc
##' @import mvtnorm
##' @import MASS 
##' @importFrom graphics plot abline axis box grid hist layout legend lines par points rect text title
##' @importFrom utils str
##' @importFrom stats complete.cases cov dnorm pnorm qbeta qchisq qf qnorm quantile sd
##' @importFrom graphics plot.default
##' @importFrom stats coef lm optim optimize pgamma plnorm ppoints qcauchy qexp qgamma qlnorm qlogis qweibull

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##' @title Vickers hardness data
##' @description A known chemical company is developing a patent for 
##' a new variant of artificial stone composed mostly of quartz ( 93wt %) 
##' and polyester resin . This company is launching a pilot plant where 
##' it begins to produce plates of this material to industry scale. In order 
##' to measure the degree of product homogeneity, 50 samples were taken, 
##' performed 5 measurements per plate corresponding to different areas 
##' of artificial stone Vickers hardness
##'
##' @name plates
##' @docType data
##' @format A data frame with 250 observations on the following 2 variables:
##' \describe{
##'   \item{hardness}{A quantitative variable that measure Vickers hardness corresponding to different
##'    areas of artificial stone.}
##'   \item{sample}{Sample ID}
##' }
##' @keywords datasets
##' @examples
##' 
##' data(plates)
##' attach(plates)
##' summary(plates)
##' plot(hardness, type="b")
##' detach(plates)
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##' @title Level of pressure data
##' @description A shipyard of recreational boats manufacturing, 
##' intended to optimize and control the mechanical properties hull yacht models. 
##' This has made a study in which the modulus of elasticity tensile strength of the epoxy resin 
##' (polymer) used, after applying different curing pressures measured: 0.1 y 10 MPa.  
##' 60 subsamples composed of three measurements taken on the same day are taken.
##'
##' @name presion
##' @docType data
##' @format A data frame with 180 observations on the following 3 variables:
##' \describe{
##'   \item{presion}{Presion level.}
##'   \item{sample}{sample id}
##'   \item{measur}{Pressures measured: 0.1 y 10 MPa}
##' }
##' @keywords datasets
##' @examples
##' 
##' data(presion)
##' attach(presion)
##' summary(presion)
##' plot(presion$presion, type="b")
##' detach(presion)
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##' @title The performance of the counters data
##' @description A water supply company wants to control the performance of 
##' the water counters installed throughout a city. For this purpose, 
##' 60 rational samples have been taken, each one composed by 3 measurements, 
##' from the same age (10 years) and caliber water counters corresponding to 
##' two different brands, and during a period of 5 years. This dataset is
##' based on a study case of A Coruña’s water supply company, Empresa 
##' Municipal de Aguas de La Coruña (Emalcsa).
##'  
##'
##' @name counters
##' @docType data
##' @format A data frame with 180 observations on the following 3 variables:
##' \describe{
##'   \item{error}{The measurement error of the counters (Error:  (Real Volume - Measured Volume)/Real Volume)}
##'   \item{sample}{Sample ID}
##'   \item{brand}{Brands of providers of counters}
##' }
##' @keywords datasets
##' @examples
##' 
##' data(counters)
##' attach(counters)
##' summary(counters)
##' plot(error, type="b")
##' detach(counters)
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##' @title Level of employment data
##' @description A Spaniard-Argentinian hotel company wants to control the 
##' level of occupancy (measured in %) in their establishments through the
##' application of a continuous control. For this purpose, 48 subsamples have
##' been taken from six hotels corresponding to two different countries.
##' 
##'
##' @name employment
##' @docType data
##' @format A data frame with 288 observations on the following 3 variables:
##' \describe{
##'   \item{occupantion}{The amount of occupants in terms of percentage}
##'   \item{sample}{Sample ID}
##'   \item{hemisphere}{Hemisphere}
##' }
##' @keywords datasets
##' @examples
##' 
##' data(employment)
##' attach(employment)
##' summary(employment)
##' boxplot(occupantion ~ hemisphere)
##' plot(occupantion, type="b")
##' detach(employment)
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##' @title Oxidation Onset Temperature
##' @description This database contains information about the level of purity 
##' of each batch of Picual varities. Then we have the type of oil 
##' by measuring the Oxidation Onset Temperature (OOT). 
##' We have 50 subsamples of oil with their temperature to oxide, each subsample is size 5.
##'
##' @name oxidation
##' @docType data
##' @format A data frame with 250 observations on the following 2 variables:
##' \describe{
##'   \item{OOT}{A quantitative variable that controls the quality of oil.}
##'   \item{sample}{sample id}
##' }
##' @keywords datasets
##' @examples
##' 
##' data(oxidation)
##' attach(oxidation)
##' summary(oxidation)
##' plot(OOT, type="b",xlab='Observations')
##' detach(oxidation)
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##' @title Circuit boards data
##' @description Number of nonconformities observed in 26 successive samples of 100 printed
##' circuit boards. Sample 6 and 20 are out of control limits. Sample 6
##' was examined by a new inspector and he did not recognize several type of
##' nonconformities that could have been present. Furthermore, the unusually
##' large number of nonconformities in sample 20 resulted from a temperature
##' control problem in the wave soldering machine, which was subsequentely
##' repaired. The last 20 samples are further samples collected on inspection
##' units (each formed by 100 boards).
##' 
##' @name circuit
##' @docType data
##' @format A data frame with 46 observations on the following 4 variables:
##' \describe{
##'   \item{x}{Number of defectives in 100 printed circuit boards (inspection unit)}
##'   \item{sample}{Sample ID}
##'   \item{size}{Sample size}
##'   \item{trial}{Trial sample indicator (TRUE/FALSE)}
##' }
##' @references Montgomery, D.C. (1991) \emph{Introduction to Statistical
##' Quality Control}, 2nd ed, New York, John Wiley & Sons, pp. 173--175
##' @keywords datasets
##' @examples
##' 
##' data(circuit)
##' attach(circuit)
##' summary(circuit)
##' boxplot(x ~ trial)
##' plot(x, type="b")
##' detach(circuit)
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##' @title Orange juice data
##' @description Frozen orange juice concentrate is packed in 6-oz cardboard cans. These
##' cans are formed on a machine by spinning them from cardboard stock and
##' attaching a metal bottom panel. A can is then inspected to determine
##' whether, when filled, the liquid could possible leak either on the side
##' seam or around the bottom joint. If this occurs a can is considered
##' nonconforming. The data were collected as 30 samples of 50 cans each at
##' half-hour intervals over a three-shift period in which the machine was in
##' continuous operation. From sample 15 used, a new bacth of cardboard stock
##' was punt into production. Sample 23 was obtained when an inexperienced
##' operator was temporarily assigned to the machine. After the first 30
##' samples, a machine adjustment was made. Then further 24 samples were taken
##' from the process.
##'  
##' @name orangejuice
##' @docType data
##' @format A data frame with 54 observations on the following 4 variables:
##' \describe{ 
##' \item{sample}{Sample ID}
##' \item{D}{Number of defectives}
##' \item{size}{Sample sizes} 
##' \item{trial}{Trial samples (TRUE/FALSE)}
##'  }
##' @references Montgomery, D.C. (1991) \emph{Introduction to Statistical
##' Quality Control}, 2nd ed, New York, John Wiley & Sons, pp. 152--155.
##' @keywords datasets
##' @examples
##' 
##' data(orangejuice)
##' orangejuice$d <- orangejuice$D/orangejuice$size
##' attach(orangejuice)
##' summary(orangejuice)
##' boxplot(d ~ trial)
##' mark <- ifelse(trial, 1, 2)
##' plot(sample, d, type="b", col=mark, pch=mark)
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##' @title Personal computer manufacturer data
##' @description A personal computer manufacturer counts the number of nonconformities per
##' unit on the final assembly line. He collects data on 20 samples of 5
##' computers each.
##' 
##' @name pcmanufact
##' @docType data
##' @format A data frame with 20 observations on the following 3 variables.
##' \describe{
##' \item{x}{A quantitative variable that measures the number of nonconformities in each sample.}
##' \item{sample}{Sample ID}
##' \item{size}{Number of computers inspected.}
##' } 
##' @references Montgomery, D.C. (1991) \emph{Introduction to Statistical
##' Quality Control}, 2nd ed, New York, John Wiley & Sons, pp. 181--182
##' @keywords datasets
##' @examples
##' 
##' data(pcmanufact)
##' summary(pcmanufact)
##' plot(pcmanufact$x/pcmanufact$size, type="b")
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##' @title Piston rings data
##' @description This is a database that contains information on the inside 
##' diameter of piston rings. Piston rings are manufactured for an automobile 
##' engine that are produced through a forging process. This process is 
##' considered "in control" and is measured on 25 samples, each of size 5.
##' 
##' @name pistonrings
##' @docType data
##' @format A data frame with 200 observations on the following 3 variables.
##' \describe{ 
##' \item{diameter}{A numeric vector.}
##' \item{sample}{Sample ID}
##' \item{trial}{Trial sample indicator (\code{TRUE}/\code{FALSE})}
##'  }
##' @references Montgomery, D.C. (1991) \emph{Introduction to Statistical
##' Quality Control}, 2nd ed, New York, John Wiley & Sons, pp. 206--213
##' @keywords datasets
##' @examples
##' 
##' data(pistonrings)
##' attach(pistonrings)
##' summary(pistonrings)
##' boxplot(diameter ~ sample)
##' plot(sample, diameter, cex=0.7)
##' lines(tapply(diameter,sample,mean))
##' detach(pistonrings)
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##' @title Target archery dataset in the ranking round (used as Phase I)
##' @description It consists of a stage in which the archer shoots 72 arrows in 12 ends of six arrows. 
##' The information is given in x and y coordinates.
##' 
##' @name archery1
##' @docType data
##' @format An array of (24 x 2 x 3).
##' \describe{ 
##' \item{x-coordinate}{x-coordinate}
##' \item{y-coordinate}{y-coordinate}
##' }
##' @keywords datasets
##' @examples
##'
##' data(archery1)
##' str(archery1) ; plot(archery1)
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##' @title Dowel pin dataset
##' @description Diameter and length of a manufacturing process of a dowel pin. 
##' 
##' @name dowel1
##' @docType data
##' @format A data frame with 40 observations on the following 2 variables.
##' \describe{ 
##' \item{diameter}{A numeric vector}
##' \item{length}{A numeric vector}
##' }
##' @keywords datasets
##' @examples
##'
##' data(dowel1)
##' str(dowel1) ; plot(dowel1)
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## @title TG curves dataset
## @description , 105 TG curves and 90 calorimetric curves were obtained from 
## calcium oxalate monohydrate. The obtained curves correspond to
## seven simulated laboratories, 15 curves per laboratory 
## @name curves
## @docType data
## @format An array of (15 x 1000 x 7).
## \describe{ 
## \item{m}{curves}
## \item{p}{the points observed in each curve}
## }
## @keywords datasets
## @examples
##'
##' data(curves)
##' str(curves)
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