A Model Object Framework for Regression Analysis
A utility library to facilitate the generalization of statistical methods built on a regression framework. Package developers can use 'modelObj' methods to initiate a regression analysis without concern for the details of the regression model and the method to be used to obtain parameter estimates. The specifics of the regression step are left to the user to define when calling the function. The user of a function developed within the 'modelObj' framework creates as input a 'modelObj' that contains the model and the R methods to be used to obtain parameter estimates and to obtain predictions.  In this way, a user can easily go from linear to non-linear models within the same package.

'methodObj.R' 'methodObjPredict.R' 'methodObjSolver.R'
'methodObjSolverFormula.R' 'modelObj.R' 'modelObjFormula.R'
'methodObjSolverXY.R' 'modelObjXY.R' 'buildModelObj.R'
'modelObjFit.R' 'warnMsg.R'

The ROC curve method is one of the most important and commonly used
methods for model accuracy assessment, which is one of the most important elements
of model evaluation.
The 'modelROC' package is a model-based ROC assessment tool, which directly works
for ROC analysis of regression results for logistic regression of binary variables,
including the glm() and lrm() commands, and COX regression for survival analysis,
including the cph() and coxph() commands.
The most important feature of 'modelROC' is that both the model and the independent
variables can be analysed simultaneously, and for survival analysis
multiple time points and area under the curve analysis are supported.
Still, flexible visualisation is possible with the 'ggplot2' package.
Reference are Kelly H. Zou (1998) <doi:10.1002/(sici)1097-0258(19971015)16:19%3C2143::aid-sim655%3E3.0.co;2-3> and
P J Heagerty (2000) <doi:10.1111/j.0006-341x.2000.00337.x>.

Interactive Studio with Explanations for ML Predictive Models
c(person("Hubert", "Baniecki", role = c("aut", "cre"), email = "hbaniecki@gmail.com"),
person("Przemyslaw", "Biecek", role = c("aut"),
comment = c(ORCID = "0000-0001-8423-1823")))
Automate explanation of machine learning predictive models.
This package generates advanced interactive and animated model explanations in the form
of serverless HTML site. It combines 'R' with 'D3.js' to produce plots and descriptions
for local and global explanations. The whole is greater than the sum of its parts,
so it also supports EDA (Exploratory Data Analysis) on top of that. 'modelStudio' is
a fast and condensed way to get all the answers without much effort.
Break down your model and look into its ingredients with only a few lines of code.
iBreakDown (>= 0.9.9), ingredients (>= 0.3.9), r2d3, jsonlite
DALEX, parallelMap, randomForest, knitr, rmarkdown, testthat
Hubert Baniecki [aut, cre],
Przemyslaw Biecek [aut] (<https://orcid.org/0000-0001-8423-1823>)