Package: dglars 2.1.7
dglars: Differential Geometric Least Angle Regression
Differential geometric least angle regression method for fitting sparse generalized linear models. In this version of the package, the user can fit models specifying Gaussian, Poisson, Binomial, Gamma and Inverse Gaussian family. Furthermore, several link functions can be used to model the relationship between the conditional expected value of the response variable and the linear predictor. The solution curve can be computed using an efficient predictor-corrector or a cyclic coordinate descent algorithm, as described in the paper linked to via the URL below.
Authors:
dglars_2.1.7.tar.gz
dglars_2.1.7.zip(r-4.7)dglars_2.1.7.zip(r-4.6)dglars_2.1.7.zip(r-4.5)
dglars_2.1.7.tgz(r-4.6-x86_64)dglars_2.1.7.tgz(r-4.6-arm64)dglars_2.1.7.tgz(r-4.5-x86_64)dglars_2.1.7.tgz(r-4.5-arm64)
dglars_2.1.7.tar.gz(r-4.7-arm64)dglars_2.1.7.tar.gz(r-4.7-x86_64)dglars_2.1.7.tar.gz(r-4.6-arm64)dglars_2.1.7.tar.gz(r-4.6-x86_64)
dglars_2.1.7.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
dglars/json (API)
| # Install 'dglars' in R: |
| install.packages('dglars', repos = c('https://luigiaugugliaro.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:048808111f. Checks:11 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | NOTE | 129 | ||
| linux-devel-x86_64 | NOTE | 128 | ||
| source / vignettes | OK | 174 | ||
| linux-release-arm64 | NOTE | 119 | ||
| linux-release-x86_64 | NOTE | 131 | ||
| macos-release-arm64 | NOTE | 152 | ||
| macos-release-x86_64 | NOTE | 178 | ||
| macos-oldrel-arm64 | NOTE | 97 | ||
| macos-oldrel-x86_64 | NOTE | 209 | ||
| windows-devel | NOTE | 270 | ||
| windows-release | NOTE | 115 | ||
| windows-oldrel | NOTE | 140 | ||
| wasm-release | OK | 99 |
Exports:AIC.dglarsBIC.dglarscoef.cvdglarscoef.dglarscvdglarscvdglars_ccdcvdglars_pccvdglars.fitd2mu_de2_mkd2th_dmu2_mkdglarsdglars_ccddglars_pcdglars.fitgdfgrcvlogLik.dglarsmake_actionmake_coefmake_cvdglarsmake_dglarsmake_rumake_summary_tablephihatplot.cvdglarsplot.dglarspredict.dglarsprint.cvdglarsprint.dglarsprint.gof_dglarsprint.loglik_dglarssetDiffsummary.dglars
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Differential Geometric Least Angle Regression | dglars-package |
| Akaike's An Information Criterion | AIC AIC.dglars BIC BIC.dglars |
| Data from the microarray experiment done by Alon et al. (1999) | alon |
| Breast Cancer microarray experiment | breast |
| Extract the Coefficients Estimated by 'cvdglars' | coef.cvdglars |
| Extract the dgLARS Coefficient Path | coef.dglars |
| Cross-Validation Method for dgLARS | cvdglars cvdglars.fit |
| dgLARS Solution Curve for GLM | dglars dglars.fit |
| Duke breast cancer microarray experiment | duke |
| Estimate the Generalized Degrees-of-Freedom | gdf print.gof_dglars |
| General Refitted Cross-Validation Estimator | grcv |
| Extract Log-Likelihood | logLik logLik.dglars print.loglik_dglars |
| Estimate the Dispersion Parameter | phihat |
| Plot from a cvdglars Object | plot.cvdglars |
| Plot from a dglars Object | plot.dglars |
| Predict Method for dgLARS Fits. | predict predict.dglars |
| Print a cvdglars Object | print.cvdglars |
| Printing a dgLARS Object | print.dglars |
| Summaryzing dgLARS Fits | summary summary.dglars |
