Function reference • anthrocheckr

Description
`anthrocheckr`	Ensuring the precision and accuracy of measurements is critical when collecting anthropometric data. Anthropometrists are usually tested for precision and accuracy of measurement through standardisation tests performed prior to anthropometric data collection. This package provides functions to calculate inter- and intra-observer technical error of measurement (TEM) to assess precision of measurements.
Calculate
`summary_measure()`	Get the mean, standard deviation and maximum value of the observations/ measurements made by a single observer across multiple subjects
`calculate_coeff_r()`	Calculate the coefficient of reliability (R).
`calculate_relative_tem()`	Calculate relative TEM of measurements by multiple observers.
`calculate_team_tem()`	Function to calculate technical error of measurement (TEM) when there are more than two observers involved.
`calculate_tem()`	Function to calculate technical error of measurement (TEM) based on formula created by Ulijaszek and Kerr (1999) as an indicator of measurement precision as described by Mueller and Mortorell (1998). This function is specific for intra-observer TEM for two measurements, and for inter-observer TEM involving two measurers.
`calculate_tem_cohort()`	Function to calculate intra-observer TEM for each observer using input dataset containing multiple types of multiple measurements from multiple observers.
`calculate_total_tem()`	Calculate the Total TEM given individual intra-observer TEM.
`estimate_bias()`	Calculate the bias of a measurement against a gold standard. Two gold standards are used in this function: 1) measurements made by a supervisor or a known expert; and, 2) median of all measurements made by the observers.
Shiny app
`run_anthrocheckr()`	Run the anthrocheckr Shiny app
Datasets
`smartStd`	Example dataset from the Standardized Monitoring and Assessment of Relief and Transitions (SMART) capacity building toolbox found at <http://smartmethodology.org/survey-planning-tools/smart-capacity-building-toolbox/>. The dataset has been restructured into a less wide format compared to the original wide format.
`smartStdLong`	Example dataset from the Standardized Monitoring and Assessment of Relief and Transitions (SMART) capacity building toolbox found at <http://smartmethodology.org/survey-planning-tools/smart-capacity-building-toolbox/>. This dataset is the same as the `smartStd` dataset but has been restructured into a long format (as per tidyverse specifications) compared to original format.
`stature`	Example dataset from Ulijaszek and Kerr (1999) containing repeat measurements of stature `m` carried out by four observers on ten subjects.
`liberiaStdData`	Dataset from a standardisation exercise done in Liberia in preparation for a coverage survey.