Package: miceFast 0.8.2
miceFast: Fast Imputations Using 'Rcpp' and 'Armadillo'
Fast imputations under the object-oriented programming paradigm. Moreover there are offered a few functions built to work with popular R packages such as 'data.table' or 'dplyr'. The biggest improvement in time performance could be achieve for a calculation where a grouping variable have to be used. A single evaluation of a quantitative model for the multiple imputations is another major enhancement. A new major improvement is one of the fastest predictive mean matching in the R world because of presorting and binary search.
Authors:
miceFast_0.8.2.tar.gz
miceFast_0.8.2.zip(r-4.5)miceFast_0.8.2.zip(r-4.4)miceFast_0.8.2.zip(r-4.3)
miceFast_0.8.2.tgz(r-4.4-x86_64)miceFast_0.8.2.tgz(r-4.4-arm64)miceFast_0.8.2.tgz(r-4.3-x86_64)miceFast_0.8.2.tgz(r-4.3-arm64)
miceFast_0.8.2.tar.gz(r-4.5-noble)miceFast_0.8.2.tar.gz(r-4.4-noble)
miceFast_0.8.2.tgz(r-4.4-emscripten)miceFast_0.8.2.tgz(r-4.3-emscripten)
miceFast.pdf |miceFast.html✨
miceFast/json (API)
NEWS
| # Install 'miceFast' in R: |
| install.packages('miceFast', repos = c('https://polkas.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/polkas/micefast/issues
- air_miss - Airquality dataset with additional variables
cppfastfast-imputationsgroupingimputationimputationsmatrixmromultiple-imputationrcpprcpparmadillovifweighting
Last updated 2 years agofrom:425710b91a. Checks:OK: 1 NOTE: 8. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 01 2024 |
| R-4.5-win-x86_64 | NOTE | Nov 01 2024 |
| R-4.5-linux-x86_64 | NOTE | Nov 01 2024 |
| R-4.4-win-x86_64 | NOTE | Nov 01 2024 |
| R-4.4-mac-x86_64 | NOTE | Nov 01 2024 |
| R-4.4-mac-aarch64 | NOTE | Nov 01 2024 |
| R-4.3-win-x86_64 | NOTE | Nov 01 2024 |
| R-4.3-mac-x86_64 | NOTE | Nov 01 2024 |
| R-4.3-mac-aarch64 | NOTE | Nov 01 2024 |
Exports:compare_impcorrDatafill_NAfill_NA_NmiceFastnaive_fill_NAneiboupset_NAVIF
Dependencies:data.tableRcppRcppArmadillo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| miceFast package for fast multiple imputations. | miceFast-package |
| airquality dataset with additional variables | air_miss |
| Comparing imputations and original data distributions | compare_imp |
| 'fill_NA' function for the imputations purpose. | fill_NA fill_NA.data.frame fill_NA.data.table fill_NA.matrix |
| 'fill_NA_N' function for the multiple imputations purpose | fill_NA_N fill_NA_N.data.frame fill_NA_N.data.table fill_NA_N.matrix |
| 'naive_fill_NA' function for the simple and automatic imputation | naive_fill_NA naive_fill_NA.data.frame naive_fill_NA.data.table naive_fill_NA.matrix |
| Finding in random manner one of the k closets points in a certain vector for each value in a second vector | neibo |
| Class '"Rcpp_corrData"' | corrData Rcpp_corrData-class |
| Class '"Rcpp_miceFast"' | miceFast Rcpp_miceFast-class |
| upset plot for NA values | upset_NA |
| 'VIF' function for assessing VIF. | VIF VIF.data.frame VIF.data.table VIF.matrix |
