Version 0.2.2.9002

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: serp
 Title: Smooth Effects on Response Penalty for 'CLM'
-Version: 0.2.2.9001
+Version: 0.2.2.9002
 Authors@R: 
     person(given = "Ejike R.",
            family = "Ugba",
@@ -22,7 +22,8 @@ Description: A regularization method for the cumulative link
     with the general model are to a large extent eliminated. Fitting is
     via a modified Newton's method. Several standard model performance and
     descriptive methods are also available. For more details on the penalty
-    implemented here, see, 'Ugba et al. (2021)' <doi:10.3390/stats4030037> and 
+    implemented here, see, Ugba (2021) <doi:10.21105/joss.03705>, 
+    'Ugba et al.' (2021) <doi:10.3390/stats4030037> and 
     Tutz and Gertheiss (2016) <doi:10.1177/1471082X16642560>.
 License: GPL-2 | file LICENSE
 URL: https://github.com/ejikeugba/serp, https://ejikeugba.github.io/serp

R/serp.R

@@ -150,23 +150,27 @@
 #' category-specific effects associated with the response turn towards a
 #' common global effect. SERP could also be applied to a semi-parallel model
 #' with only the category-specific part of the model penalized. See,
-#' Ugba et al. (2021) for a discussion and an application of SERP in an
-#' empirical study.
+#' Ugba (2021), Ugba et al. (2021) for further details and application in
+#' empirical studies.
 #'
 #' @references
-#' McCullagh, P. (1980). Regression Models for Ordinal Data.
-#'     \emph{Journal of the Royal Statistical Society. Series B
-#'     (Methodological)}, 42, pp. 109-142.
-#'     https://doi.org/10.1111/j.2517-6161.1980.tb01109.x
+#' Ugba, E. R. (2021). serp: An R package for smoothing in ordinal regression
+#'     \emph{Journal of Open Source Software}, 6(66), 3705.
+#'     https://doi.org/10.21105/joss.03705
+#'
+#' Ugba, E. R., Mörlein, D. and Gertheiss, J. (2021). Smoothing in Ordinal
+#'     Regression: An Application to Sensory Data. \emph{Stats}, 4, 616–633.
+#'     https://doi.org/10.3390/stats4030037
 #'
 #' Tutz, G. and Gertheiss, J. (2016). Regularized Regression
 #'     for Categorical Data (With Discussion and Rejoinder).
 #'     \emph{Statistical Modelling}, 16, pp. 161-260.
 #'     https://doi.org/10.1177/1471082X16642560
 #'
-#' Ugba, E. R., Mörlein, D. and Gertheiss, J. (2021). Smoothing in Ordinal
-#'     Regression: An Application to Sensory Data. \emph{Stats}, 4, 616–633.
-#'     https://doi.org/10.3390/stats4030037
+#' McCullagh, P. (1980). Regression Models for Ordinal Data.
+#'     \emph{Journal of the Royal Statistical Society. Series B
+#'     (Methodological)}, 42, pp. 109-142.
+#'     https://doi.org/10.1111/j.2517-6161.1980.tb01109.x
 #'
 #' @details An object of class \code{serp} with the components listed below,
 #' depending on the type of slope modeled. Other summary methods include:

README.Rmd

@@ -30,7 +30,7 @@ knitr::opts_chunk$set(
 
 
 #### Overview
-The R package `serp` fits cumulative link models (CLMs) with the `smooth-effect-on-response penalty (SERP)`. The `cumulative model` developed by McCullagh (1980) is probably the most frequently used ordinal model in empirical studies. However, the stochastic ordering property of the general form of the model poses a very serious challenge in most empirical applications of the model. For instance, unstable likelihoods with ill-conditioned parameter space are frequently encountered during the iterative process. `serp` implements a unique regularization method for CLMs that provides the means of smoothing the adjacent categories in the model. At extreme shrinkage, SERP causes all subject-specific effects associated with each variable in the model to shrink towards unique global effects. Fitting is done using a modified Newton's method. Several standard model performance and descriptive methods are also available. See [Ugba et al., 2021](https://doi.org/10.3390/stats4030037) and [Tutz and Gertheiss, 2016](https://doi.org/10.1177/1471082X16642560) for further details on the implemented penalty.
+The R package `serp` fits cumulative link models (CLMs) with the `smooth-effect-on-response penalty (SERP)`. The `cumulative model` developed by McCullagh (1980) is probably the most frequently used ordinal model in empirical studies. However, the stochastic ordering property of the general form of the model poses a very serious challenge in most empirical applications of the model. For instance, unstable likelihoods with ill-conditioned parameter space are frequently encountered during the iterative process. `serp` implements a unique regularization method for CLMs that provides the means of smoothing the adjacent categories in the model. At extreme shrinkage, SERP causes all subject-specific effects associated with each variable in the model to shrink towards unique global effects. Fitting is done using a modified Newton's method. Several standard model performance and descriptive methods are also available. See [Ugba, 2021](https://doi.org/10.21105/joss.03705), [Ugba et al., 2021](https://doi.org/10.3390/stats4030037) and [Tutz and Gertheiss, 2016](https://doi.org/10.1177/1471082X16642560) for further details on the implemented penalty.
 
 
 #### Example
@@ -117,3 +117,4 @@ Tutz, G. and Gertheiss, J. (2016). Regularized Regression for Categorical Data (
 
 Ugba, E. R., Mörlein, D. and Gertheiss, J. (2021). Smoothing in Ordinal Regression: An Application to Sensory Data. *Stats*, 4, 616–633. https://doi.org/10.3390/stats4030037
 
+Ugba, E. R. (2021). serp: An R package for smoothing in ordinal regression *Journal of Open Source Software*, 6(66), 3705. https://doi.org/10.21105/joss.03705

README.md

@@ -37,7 +37,8 @@ adjacent categories in the model. At extreme shrinkage, SERP causes all
 subject-specific effects associated with each variable in the model to
 shrink towards unique global effects. Fitting is done using a modified
 Newton’s method. Several standard model performance and descriptive
-methods are also available. See [Ugba et al.,
+methods are also available. See [Ugba,
+2021](https://doi.org/10.21105/joss.03705), [Ugba et al.,
 2021](https://doi.org/10.3390/stats4030037) and [Tutz and Gertheiss,
 2016](https://doi.org/10.1177/1471082X16642560) for further details on
 the implemented penalty.
@@ -150,3 +151,7 @@ Modelling*, 16, 161-260. <https://doi.org/10.1177/1471082X16642560>
 Ugba, E. R., Mörlein, D. and Gertheiss, J. (2021). Smoothing in Ordinal
 Regression: An Application to Sensory Data. *Stats*, 4, 616–633.
 <https://doi.org/10.3390/stats4030037>
+
+Ugba, E. R. (2021). serp: An R package for smoothing in ordinal
+regression *Journal of Open Source Software*, 6(66), 3705.
+<https://doi.org/10.21105/joss.03705>