Another attempt to CRAN

Author: ikosmidis

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
 Version: 1.0.0
-Date: 2025-08-27 16:47:28 UTC
-SHA: abcf198d7ffe8c7db6457198e888943cf1d52be4
+Date: 2025-08-29 09:26:17 UTC
+SHA: bd559b0c220666327a829af4102976c32d87c43f

README.Rmd

@@ -302,14 +302,13 @@ abline(0, 1/summ_full_m$se_parameters[1], col = "grey")
 
 ## Estimation methods 
 
-The workhorse function in **brglm2** is
-[`brglm_fit()`](https://github.com/ikosmidis/brglm2/blob/master/R/brglmFit.R)
-(or equivalently `brglmFit()` if you like camel case), which, as we
-did in the example above, can be passed directly to the `method`
-argument of the `glm()` function. `brglm_fit()` implements a quasi
-[Fisher scoring](https://en.wikipedia.org/wiki/Scoring_algorithm)
-procedure, whose special cases result in a range of explicit and
-implicit bias reduction methods for generalized linear models for more
+The workhorse function in **brglm2** is `brglm_fit()` (or equivalently
+`brglmFit()` if you like camel case), which, as we did in the example
+above, can be passed directly to the `method` argument of the `glm()`
+function. `brglm_fit()` implements a quasi [Fisher
+scoring](https://en.wikipedia.org/wiki/Scoring_algorithm) procedure,
+whose special cases result in a range of explicit and implicit bias
+reduction methods for generalized linear models for more
 details). Bias reduction for multinomial logistic regression (nominal
 responses) can be performed using the function `brmultinom()`, and for
 adjacent category models (ordinal responses) using the function
@@ -319,8 +318,7 @@ The classification of bias reduction methods into explicit and
 implicit is as given in [Kosmidis
 (2014)](https://doi.org/10.1002/wics.1296).
 
-For logistic regression models, in particular, the
-[`mdypl_fit()`](https://github.com/ikosmidis/brglm2/blob/master/R/mdyplFit.R)
+For logistic regression models, in particular, the `mdypl_fit()`
 function provides maximum Diaconis-Ylvisaker prior penalized
 likelihood estimation, and can again be passed directly to the
 `method` argument of the `glm()` function. The `summary()` method for

README.md

@@ -115,6 +115,11 @@ R package
 
     # install.packages("detectseparation")
     library("detectseparation")
+    #> 
+    #> Attaching package: 'detectseparation'
+    #> The following objects are masked from 'package:brglm2':
+    #> 
+    #>     check_infinite_estimates, detect_separation
     update(modML, method = detect_separation)
     #> Implementation: ROI | Solver: lpsolve 
     #> Separation: TRUE 
@@ -455,11 +460,10 @@ impact of the correction is to inflate the MDYPL estimates.
 
 ## Estimation methods
 
-The workhorse function in **brglm2** is
-[`brglm_fit()`](https://github.com/ikosmidis/brglm2/blob/master/R/brglmFit.R)
-(or equivalently `brglmFit()` if you like camel case), which, as we did
-in the example above, can be passed directly to the `method` argument of
-the `glm()` function. `brglm_fit()` implements a quasi [Fisher
+The workhorse function in **brglm2** is `brglm_fit()` (or equivalently
+`brglmFit()` if you like camel case), which, as we did in the example
+above, can be passed directly to the `method` argument of the `glm()`
+function. `brglm_fit()` implements a quasi [Fisher
 scoring](https://en.wikipedia.org/wiki/Scoring_algorithm) procedure,
 whose special cases result in a range of explicit and implicit bias
 reduction methods for generalized linear models for more details). Bias
@@ -471,8 +475,7 @@ models (ordinal responses) using the function `bracl()`. Both
 The classification of bias reduction methods into explicit and implicit
 is as given in [Kosmidis (2014)](https://doi.org/10.1002/wics.1296).
 
-For logistic regression models, in particular, the
-[`mdypl_fit()`](https://github.com/ikosmidis/brglm2/blob/master/R/mdyplFit.R)
+For logistic regression models, in particular, the `mdypl_fit()`
 function provides maximum Diaconis-Ylvisaker prior penalized likelihood
 estimation, and can again be passed directly to the `method` argument of
 the `glm()` function. The `summary()` method for `mdyplFit` objects,

vignettes/iteration.Rmd

@@ -38,7 +38,7 @@ This vignette
 
 The bias-reducing quasi Fisher scoring iteration is also described in detail in the [bias vignette](https://cran.r-project.org/package=enrichwith/vignettes/bias.html) of the [**enrichwith**](https://cran.r-project.org/package=enrichwith) R package. @kosmidis:10 describe a parallel quasi [Newton-Raphson](https://en.wikipedia.org/wiki/Newton%27s_method) procedure.
 
-Most of the material in this vignette comes from a presentation by [Ioannis Kosmidis](https://www.ikosmidis.com) at the useR! 2016 international conference at University of Stanford on 16 June 2016. The presentation was titled "Reduced-bias inference in generalized linear models" and can be watched online at this [link](https://channel9.msdn.com/Events/useR-international-R-User-conference/useR2016/brglm-Reduced-bias-inference-in-generalized-linear-models).
+Most of the material in this vignette comes from a presentation by [Ioannis Kosmidis](https://www.ikosmidis.com) at the useR! 2016 international conference at University of Stanford on 16 June 2016. The presentation was titled "Reduced-bias inference in generalized linear models".
 
 # Generalized linear models