Import the 'EdBuild' master dataset of school district finance,
student demographics, and community economic indicators for every school district in the United States.
The master dataset is built from the US Census, Annual Survey of School System Finances (F33)
and joins data from the National Center for Education Statistics, Common Core of Data;
the US Census, Small Area Income and Poverty Estimates; and the US Census,
Education Demographic and Geographic Estimates. We apply 'EdBuild' standard processing to the
dataset and provide the option to select from four different exclusion criteria - see the masterpull() help file for more details.
The master dataset is available for any school year from 2013 to 2019 or longitudinally for all years 2013-2019.
School year is identified by the end year. For example, the 2018-19 school year is 2019.
Additional functions in the package use 'EdBuild' master data to analyze the difference
between neighboring school districts and create formatted excel tables of school district data. For full details about
'EdBuild' data processing please see 'EdBuild' (2020) <http://data.edbuild.org>.

Weicheng Zhu <weicheng@dreamhunter.me>, Changsoon Park
<cspark@cau.ac.kr>
This package provide a unified approach for
Economic Design of Control Charts. The main purpose of
this package is to find out the optimal parameters to
minimize the cost per unit time.

This package provides a unified approach for
Economic Design of Control Charts. The main purpose of
this package is to find out the optimal parameters to
minimize the ECH (Expected Cost per Hour) of the process.

This package provides a unified approach for Economic
Design of Control Charts. The main purpose of this package is
to find out the optimal parameters to minimize the ECH
(Expected Cost per Hour) of the process.

Detection of edgepoints in images based on the difference
of two assymetric M kernel estimators. Linear and circular regression
clustering based on redescending M estimators. Detection of linear
edges in images.