BiostatR Blog

 Introduction to Google Earth Engine with R language https://www.youtube.com/watch?v=SHXuIpjU3YE Deep Learning for Remote Sensing images with R language https://www.youtube.com/watch?v=N3CHgRlRqOA

Alternative proposed by Varin Sacha using the package weibulltools  with delta method (a little bit modified to present only the median and the CI): library(weibulltools) x <- rweibull(100000, 2, 2) data <- reliability_data(x = x, status = 1)  ml <- ml_estimation(x = data, distribution = "weibull") conf <- confint_fisher(x = ml, b_lives = 0.5, direction = "x")  The results are: > as.numeric(conf[conf$prob == 0.5, c(4, 1, 5)]) [1] 1.657227 1.663275 1.669346 The true median was : > 2*log(2)^(1/2) [1] 1.665109 Let try if the average estimate by Fisher method for 1,000 samples is biased or not: library(weibulltools) library(pbmcapply) k <- pbmclapply(1:1000, FUN=function(i) { x <- rweibull(100000, 2, 2) data <- reliability_data(x = x, status = 1)  ml <- ml_estimation(x = data, distribution = "weibull") conf <- confint_fisher(x = ml, b_lives = 0.5, direction = "x")  return(as.numeric(conf[conf$prob == 0.5, 1])) }, mc.c...

From a discussion in   I am attempting to build my package using devtools::build I have verified that both Rcpp and RcppArmadillo are working properly. However, when I build, I get the following error message  Error: package or namespace load failed for ‘HACSim’ in dyn.load(file, DLLpath = DLLpath, ...):     unable to load shared object '/private/var/folders/wv/4_z4h7ns57g7qvd600qgd__w0000gn/T/RtmpGe2Cqt/Rinst118e1e9f13a0/00LOCK-HACSim/00new/HACSim/libs/HACSim.so':  dlopen(/private/var/folders/wv/4_z4h7ns57g7qvd600qgd__w0000gn/T/RtmpGe2Cqt/Rinst118e1e9f13a0/00LOCK-HACSim/00new/HACSim/libs/HACSim.so, 6): Symbol not found: ___addtf3      Referenced from: /usr/local/lib/libquadmath.0.dylib      Expected in: /usr/local/lib/libgcc_s_x86_64.1.dylib     in /usr/local/lib/libquadmath.0.dylib    Error: loading failed    Execution halted    ERROR: loading failed ─  removing ‘/private/var/folde...

 sudo vi /etc/rstudio/rserver.conf # Server Configuration File #  www-port=8080 rsession-which-r=usr/bin/R auth-timeout-minutes=0 auth-stay-signed-in-days=3000 Note that the option session-timeout-minutes=0 Does not work. Perhaps it is only for the pro version

I'm having trouble with : install.packages("broom", type = "source") and install.packages("spdep", type = "source") in R 4.12 in MacOSX 12.2 The error message is: Erreur dans dyn.load(file, DLLpath = DLLpath, ...) :    impossible de charger l'objet partagé '/Library/Frameworks/R.framework/Versions/4.1/Resources/library/glue/libs/glue.so':   dlopen(/Library/Frameworks/R.framework/Versions/4.1/Resources/library/glue/libs/glue.so, 0x0006): Library not loaded: /usr/local/opt/gsl/lib/libgsl.25.dylib   Referenced from: /Library/Frameworks/R.framework/Versions/4.1/Resources/library/glue/libs/glue.so   Reason: tried: '/usr/local/opt/gsl/lib/libgsl.25.dylib' (no such file), '/Library/Frameworks/R.framework/Resources/lib/libgsl.25.dylib' (no such file), '/Library/Java/JavaVirtualMachines/jdk1.8.0_241.jdk/Contents/Home/jre/lib/server/libgsl.25.dylib' (no such file), '/usr/local/Cellar/gsl/2.7.1/lib/libgsl.25.dylib...

 In ubuntu 20.04: sudo apt-get install libgsl0-dev install.packages("mixcat") In MacOSX: brew install gsl   Create a directory ~/.R/ if it does not exist and a file Makervars with PKG_LIBS=-L/usr/local/opt/gettext/lib CFLAGS=-I/usr/local/opt/gsl/include LDFLAGS=-L/usr/local/opt/gsl/lib -lgsl -lgslcblas Then in R: install.packages("mixcat") After the installation, remove the Makervars file or it will create interference with other installation, for example nloptr. Thanks to aymeric.stamm at math.cnrs.fr to have found this solution to install the nloptr package !

I had problem to install new version of spdep from source. I get this answer from maintainer of the package: This is not an spdep issue. See r-spatial/sf#1848, and the resolution of never installing from source unless you know how to do it and need to do it to access the development versions of packages, or more recent versions of external software. I don't agree at all with this answer. When you build a package from source, it is exactly fitted for your configuration. Here is the solution: In terminal brew install gdal brew install proj brew install geos brew install sqlite3 or brew reinstall *** in R with intel processor install.packages("sf",configure.args = "--with-gdal-config=/usr/local/bin/gdal-config", type="source") install.packages("spdep",configure.args = "--with-gdal-config=/usr/local/bin/gdal-config", type="source") And it works In R with ARM64 processor install.packages("sf", type = "source...