Statistical Program R

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Doelia Lanoue

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Aug 3, 2024, 12:10:05 PM8/3/24

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I think it will be tough for Julia catch up with R and the like as a home for end users doing statistical analysis. But I think its a great environment for the core work of implementing numerical methods. Why code things separately for R, Python, etc., when we can just do it once, in Julia?

I tried this approach - writing R an python wrappers for one of my julia package (following example of DifferentialEquations.jl). But I feel this is currently not viable. For example, JuliaCall (which calls Julia from R) appears to be abandoned, and even basic installation issues will likely never get resolved. As a result, half the time my collaborators cannot successfully install these wrappers without my help. Also, I feel our community did not documented well how to write your Julia code to make wrapping in R/Python/whatever easy, and I actually had to re-structure a lot of my originally working Julia code to make the wrappers work.

To try to work in the other direction, starting an inferior Julia process from an R process and communicating with it from R, is much more difficult. You need to write the glue code in C/C++ and even with that I think the JuliaCall package for R still requires RCall.jl to be installed on the Julia side for some of the interfacing of internal types.

ProUCL version 5.2.00 (5.2) is the latest update of the USEPA ProUCL statistical software package for analysis of environmental data sets with and without nondetect (ND) observations. ProUCL version 5.2 is a comprehensive statistical software package with statistical methods and graphical tools to address many environmental sampling and statistical issues. Version 5.2 is the latest version of the software that has been updated to include improvements to the Technical Guide and the User Guide for clarity, code updates to correct for reported bugs, and several changes made to the decision logic for the recommendation of UCLs for this version. The Technical Guide and Users Guide due to size are now downloaded separately from the program.

Historically, ProUCL has placed emphasis on achieving adequate coverage, but not on achieving an accurate estimate of the mean, in the sense of an upper bound for the mean that is as close as possible to the true mean while maintaining the desired coverage. Depending on the data, there are some UCL estimators in ProUCL (particularly Chebyshev and H) that can generate gross overestimates of the mean so that adequate coverage will almost certainly be achieved in these cases, but accuracy suffers. Although this philosophy ensures that the likelihood of one decision error will be small (i.e., Type I error, concluding a site is not contaminated when it is), such an overestimate can result in a high likelihood of the opposite decision error (i.e., Type II error, concluding a site is contaminated when it is not). The objective should be to not only control for Type I error, but also to protect against large Type II errors. This requires balancing both objectives (coverage and accuracy) to select the most appropriate UCL method.

ProUCL 5.2 is compatible with Microsoft Office 10.0 and 11, and represents an upgrade of all previous versions of ProUCL. ProUCL 5.2 has been developed in Microsoft .NET Framework 4.7.2 using the C# programming language. To provide Excel-compatible Spreadsheet functionality, ProUCL uses GrapeCity Spread.NET 14; and the development software package, ChartFX 8 for graphics. With the exception of slight differences in graphical display, the look of ProUCL 5.2 is very similar to version 5.1. Any differences are addressed in the User Guide.

Changes to UCL recommendations were made based on three simulation studies performed by Neptune and Company, Inc (see Appendix D of the Technical Guide) with an aim to better align ProUCL with the DQO process, under which accuracy must be an important consideration. In ProUCL 5.2, the decision logic for goodness-of-fit has been modified so that more robust UCL estimators are selected more often. The Chebyshev UCL is no longer recommended, and the H UCL is only recommended in cases of moderate to large sample sizes when there is high confidence that the assumption of lognormality is met to a good approximation. It has been noted that in some cases, data may be too skewed or not numerous enough to determine an appropriate UCL. ProUCL 5.2 does not provide a recommendation in these cases but encourages the user to: 1) verify that the data were collected randomly (rather than through biased sampling, such as hot spot delineation sampling or best professional judgment sampling); 2) consider site knowledge that may explain why the data may be skewed (such as small areas of high concentrations); and 3) to contact a statistician if ProUCL cannot provide a recommendation. A detailed description of the justification for these changes is in Technical Guide Section 2.5, and new decision logic charts are available in Appendix A of the same guide.