Title: Make Static HTML Website for Predictive Models
Authors@R: c(person("Przemysław", "Biecek", email = "przemyslaw.biecek@gmail.com", role = c("aut")),
person("Magda", "Tatarynowicz", email = "magda.tatarynowicz@gmail.com", role = c("aut")),
person("Kamil", "Romaszko", email = "kamil.romaszko@gmail.com", role = c("aut", "cre")),
person("Mateusz", "Urbański", email = "matiszak@gmail.com", role = c("aut")))
Description: Website generator with HTML summaries for predictive models.
This package uses 'DALEX' explainers to describe global model behavior.
We can see how well models behave (tabs: Model Performance, Auditor),
how much each variable contributes to predictions (tabs: Variable Response)
and which variables are the most important for a given model (tabs: Variable Importance).
We can also compare Concept Drift for pairs of models (tabs: Drifter).
Additionally, data available on the website can be easily recreated in current R session.
Work on this package was financially supported by the NCN Opus grant 2017/27/B/ST6/01307
at Warsaw University of Technology, Faculty of Mathematics and Information Science.

Title: A Model Object Framework for Regression Analysis
Description: 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.

Description: 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
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.