N>=3 methods are used to measure each of n items. The data are used to estimate simultaneously systematic error (bias) and random error (imprecision). Observed measurements for each method or device are assumed to be linear functions of the unknown true values and the errors are assumed normally distributed. Pairwise calibration curves and plots can be easily generated. Unlike the 'ncb.od' function, the 'omx' function builds a one-factor measurement error model using 'OpenMx' and allows missing values, uses full information maximum likelihood to estimate parameters, and provides both likelihood-based and bootstrapped confidence intervals for all parameters, in addition to Wald-type intervals.
1. Put any C/C++/Fortran code in 'src' 2. If you have compiled code, add a .First.lib() function in 'R' to load the shared library 3. Edit the help file skeletons in 'man' 4. Run R CMD build to create the index files 5. Run R CMD check to check the package 6. Run R CMD build to make the package file Read "Writing R Extensions" for more information.