search for: rpruim

...myself the weekend to think of other things, but I can?t figure it out. Perhaps someone on the list can lend a hand. Here?s one example situation, where I build the index.html file myself and put it in inst/doc/index.html $ cat fastR.Rcheck/00check.log | grep -v OK * using log directory ?/Users/rpruim/projects/github/fastR/fastR.Rcheck? * using R Under development (unstable) (2014-08-21 r66456) * using platform: x86_64-apple-darwin13.1.0 (64-bit) * using session charset: UTF-8 * checking extension type ... Package * this is package ?fastR? version ?0.8-0? * checking CRAN incoming feasibility ......

...me functions that add extra features to familiar functions (e.g., xchisq.test(), xhistogram(), xpnorm(), ...) * some data sets If you are using mosaic and discover bugs, or have suggestions for future development, consider submitting an issue on our github development site: http://github.com/rpruim/mosaic/issues/ You can also look there to see what's already on our to-do list. ---rjp (on behalf of the development team that includes Danny Kaplan and Nick Horton) ======================================================================== Randall Pruim phon...

...me functions that add extra features to familiar functions (e.g., xchisq.test(), xhistogram(), xpnorm(), ...) * some data sets If you are using mosaic and discover bugs, or have suggestions for future development, consider submitting an issue on our github development site: http://github.com/rpruim/mosaic/issues/ You can also look there to see what's already on our to-do list. ---rjp (on behalf of the development team that includes Danny Kaplan and Nick Horton) ======================================================================== Randall Pruim phon...

I see that the sigma() function has recently been introduced into R 3.3. The help for sigma() says: Extract the estimated standard deviation of the errors, the ?residual standard deviation? (misnomed also ?residual standard error?, e.g., in summary.lm()'s output, from a fitted model. Is there any reason not to fix the mis-naming of residual standard error now too? Both functions are in the

...h y, the last 1 - min(ratio, 1/ratio) fraction of the spoke rendered differently. This is similar to adding error bars to a plot in xYplot -- only in polar coordinates. ============================================== Randall Pruim Dept. of Biostatistics, University of Michigan email: rpruim at umich.edu phone: 734.615.9825

>>>>> Randall Pruim <rpruim at calvin.edu> >>>>> on Sun, 17 Apr 2016 13:54:28 +0000 writes: > I see that the sigma() function has recently been introduced into R 3.3. The help for sigma() says: > Extract the estimated standard deviation of the errors, the ?residual standard deviation? (mi...

Dear all, On the CRAN website http://cran.r-project.org/ it is currently not possible to get to the packages. Clicking on "Packages" or on "Contributed extension packages" results in Error 404: Object not found!, see: http://cran.r-project.org/web/packages/ Best regards, Christian _._._._._._._._._._._._._._._._._._ C.h.r.i.s.t.i.a.n S.t.r.a.t.o.w.a V.i.e.n.n.a

I?m wondering if there is any way for me to create a generic function in a package that merges the generics and methods from two or more packages that each define a generic with the same name, but have non intersecting instances. Here is an example. dplyr and MASS both define an S3 generic select() but define no colliding methods. Just to make life extra interesting, there is also

Thanks. I am fully aware of what aggregate() returnes, and I can post-process this into the form I want ? if the names are available. But for foo, the returned object is both different in structure and loses the name altogether: foo <- function(x) { c(mean = base::mean(x)) } str(aggregate(iris$Sepal.Length, by = list(iris$Species), FUN = foo)) ## 'data.frame': 3 obs. of 2 variables:

In the examples below, the first loses the name attached by foo(), the second retains names attached by bar(). Is this an intentional difference? I?d prefer that the names be retained in both cases. foo <- function(x) { c(mean = base::mean(x)) } bar <- function(x) { c(mean = base::mean(x), sd = stats::sd(x))} aggregate(iris$Sepal.Length, by = list(iris$Species), FUN = foo) #>