--- /tmp/rebuilderdaGSW8w/inputs/r-cran-riskregression_2023.12.21+ds-1_riscv64.deb +++ /tmp/rebuilderdaGSW8w/out/r-cran-riskregression_2023.12.21+ds-1_riscv64.deb ├── file list │ @@ -1,3 +1,3 @@ │ -rw-r--r-- 0 0 0 4 2024-01-09 07:53:11.000000 debian-binary │ --rw-r--r-- 0 0 0 2616 2024-01-09 07:53:11.000000 control.tar.xz │ +-rw-r--r-- 0 0 0 2572 2024-01-09 07:53:11.000000 control.tar.xz │ -rw-r--r-- 0 0 0 1610908 2024-01-09 07:53:11.000000 data.tar.xz ├── control.tar.xz │ ├── control.tar │ │ ├── file list │ │ │ @@ -1,3 +1,3 @@ │ │ │ drwxr-xr-x 0 root (0) root (0) 0 2024-01-09 07:53:11.000000 ./ │ │ │ --rw-r--r-- 0 root (0) root (0) 2056 2024-01-09 07:53:11.000000 ./control │ │ │ +-rw-r--r-- 0 root (0) root (0) 1910 2024-01-09 07:53:11.000000 ./control │ │ │ -rw-r--r-- 0 root (0) root (0) 4493 2024-01-09 07:53:11.000000 ./md5sums │ │ ├── ./control │ │ │ @@ -1,14 +1,14 @@ │ │ │ Package: r-cran-riskregression │ │ │ Version: 2023.12.21+ds-1 │ │ │ Architecture: riscv64 │ │ │ Maintainer: Debian R Packages Maintainers │ │ │ Installed-Size: 2047 │ │ │ Depends: r-api-4.0, r-cran-cmprsk, r-cran-data.table (>= 1.12.2), r-cran-doparallel, r-cran-foreach, r-cran-ggplot2 (>= 3.1.0), r-cran-lattice, r-cran-lava (>= 1.6.5), r-cran-mets, r-cran-mvtnorm, r-cran-plotrix, r-cran-prodlim (>= 2018.4.18), r-cran-publish, r-cran-ranger, r-cran-rcpp, r-cran-rms (>= 5.1.3), r-cran-survival (>= 2.44.1), r-cran-timereg (>= 1.9.3), r-cran-rcpparmadillo, libblas3 | libblas.so.3, libc6 (>= 2.27), libgcc-s1 (>= 3.4), libstdc++6 (>= 13.1) │ │ │ -Recommends: r-cran-boot, r-cran-smcfcs, r-cran-glmnet, r-cran-gbm, r-cran-mgcv, r-cran-nnls, r-cran-numderiv, r-cran-party, r-cran-pec, r-cran-proc, r-cran-randomforest, r-cran-rpart, r-cran-testthat, r-cran-r.rsp │ │ │ +Recommends: r-cran-mgcv, r-cran-numderiv, r-cran-proc, r-cran-rpart │ │ │ Section: gnu-r │ │ │ Priority: optional │ │ │ Homepage: https://cran.r-project.org/package=riskRegression │ │ │ Description: GNU R Risk Regression Models and Prediction Scores for Survival │ │ │ Analysis with Competing Risks Implementation of the following methods │ │ │ for event history analysis. Risk regression models for survival │ │ │ endpoints also in the presence of competing risks are fitted using