Package: MGLM 0.2.1
MGLM: Multivariate Response Generalized Linear Models
Provides functions that (1) fit multivariate discrete distributions, (2) generate random numbers from multivariate discrete distributions, and (3) run regression and penalized regression on the multivariate categorical response data. Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported.
Authors:
MGLM_0.2.1.tar.gz
MGLM_0.2.1.zip(r-4.5)MGLM_0.2.1.zip(r-4.4)MGLM_0.2.1.zip(r-4.3)
MGLM_0.2.1.tgz(r-4.4-any)MGLM_0.2.1.tgz(r-4.3-any)
MGLM_0.2.1.tar.gz(r-4.5-noble)MGLM_0.2.1.tar.gz(r-4.4-noble)
MGLM_0.2.1.tgz(r-4.4-emscripten)MGLM_0.2.1.tgz(r-4.3-emscripten)
MGLM.pdf |MGLM.html✨
MGLM/json (API)
| # Install 'MGLM' in R: |
| install.packages('MGLM', repos = c('https://juhkim111.r-universe.dev', 'https://cloud.r-project.org')) |
- rnaseq - RNA-seq count data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:e0b8d5d6de. Checks:OK: 3 NOTE: 4. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 09 2024 |
| R-4.5-win | NOTE | Oct 09 2024 |
| R-4.5-linux | NOTE | Oct 09 2024 |
| R-4.4-win | NOTE | Oct 09 2024 |
| R-4.4-mac | NOTE | Oct 09 2024 |
| R-4.3-win | OK | Oct 09 2024 |
| R-4.3-mac | OK | Oct 09 2024 |
Exports:AICBICcoefddirmddirmndgdirmdgdirmndmndmultndnegdnegmndofkrlogLikmaxlambdaMGLMfitMGLMregMGLMreg.fitMGLMsparseregMGLMsparsereg.fitMGLMtunepathpredictrdirmrdirmnrgdirmrgdirmnrmnrnegmnshow
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| MGLM: A package for multivariate response generalized linear models | MGLM-package |
| Akaike's Information Criterion (AIC) | AIC AIC,MGLMfit-method AIC,MGLMreg-method AIC,MGLMsparsereg-method AIC,MGLMtune-method |
| Bayesian information criterion (BIC) | BIC BIC,MGLMfit-method BIC,MGLMreg-method BIC,MGLMsparsereg-method BIC,MGLMtune-method |
| Extract Model Coefficients | coef coef,MGLMfit-method coef,MGLMreg-method coef,MGLMsparsereg-method coef,MGLMtune-method |
| Details of the distributions | dist |
| Fit multivariate discrete distributions | DMD.DM.fit DMD.GDM.fit DMD.NegMN.fit MGLMfit |
| Extract degrees of freedom | dof dof,MGLMsparsereg-method |
| Khatri-Rao product of two matrices | kr |
| Extract log-likelihood | logLik logLik,MGLMfit-method logLik,MGLMreg-method logLik,MGLMsparsereg-method |
| Extract maximum lambda | maxlambda maxlambda,MGLMsparsereg-method |
| Deprecated function(s) in the MGLM package | ddirm dgdirm dneg MGLM-deprecated rdirm rgdirm |
| Class '"MGLMfit"' | MGLMfit-class |
| Fit multivariate response GLM regression | MGLMreg MGLMreg.fit |
| Class '"MGLMreg"' | MGLMreg-class |
| Fit multivariate GLM sparse regression | MGLMsparsereg MGLMsparsereg.fit |
| Class '"MGLMsparsereg"' | MGLMsparsereg-class |
| Choose the tuning parameter value in sparse regression | MGLMtune |
| Class '"MGLMtune"' | MGLMtune-class |
| Extract path | path path,MGLMtune-method |
| Predict method for MGLM Fits | predict predict,MGLMreg predict,MGLMreg,ANY-method predict,MGLMreg-method |
| The Dirichlet Multinomial Distribution | ddirmn dirmn rdirmn |
| The Generalized Dirichlet Multinomial Distribution | dgdirmn gdirmn rgdirmn |
| The Multinomial Distribution | dmn mn rmn |
| RNA-seq count data | rnaseq |
| The Negative Multinomial Distribution | dnegmn negmn rnegmn |
| Show an object | show show,MGLMfit-method show,MGLMreg-method show,MGLMsparsereg-method show,MGLMtune-method |
