• Fix for issue introduced by version 0.7.0 of the broom package related to degrees of freedom in linear regression
• Fix for NoLD issue identified by CRAN

• Minor adjustments in anticipation of dplyr 1.0.0

• Fix for cv.rforest when the max of mtry exceeds the number of explanatory variables
• Fix to write.coeff when one or more coefficients have a missing value
• Use weighted mean and sd in write.coeff function when needed
• Added flexibility in using constants while defining the spec for other randomly generated variables

• Adding OR% change as a columns in output for Model > Logistic regression and the write.coeff function
• Restrict max number of levels in a “groupable” variable used in Model > Evaluate classification and Model > Multinomial logistic regression to no more than 50
• Avoid rounding the profit measures in Model > Evaluate classificiation

• Improvements to cv.gbt to allow previously setup evaluation functions to be used in cross validation for hyper parameter tuning
• Random Forest module using the ranger package. Includes a cv.rforest function for tuning using cross-validation
• Gradient Boosted Trees module using the xgboost package. Includes a cv.gbt function for tuning using cross-validation. For convenience, all data.frame-to-matrix-conversion is handled by radiant
• Partial Dependence Plots for all trees-based estimation modules and for neural networks
• onehot function to make converting a data.frame with categorical variables to a matrix a bit easier

• Allow specification of multiple summary functions in Model > Simulate > Repeat
• Use patchwork for grouping multiple plots together
• Allow formula input for logistic and regress functions
• Adjust correlation plot for NB to accommodate changes in Basics > Correlation
• Fix for repeated simulation (Model > Simulate > Repeat) where “Variables to re-simulate” and “Output variables” were not always updated correctly when the set of available variables changed

• Fix prediction issue when using I(x^2) in a stepwise estimation process and x is removed
• Fix issue finding .as_int and .as_num when use radiant through shiny server

• Option to drop the intercept for Model > Multinomial Logistic Regression
• Provide access to the variables in a dataset during simulation and repeated simulation.

• Various fixes related to stepwise estimation of Multinomial, Logistic, and Linear regression model (e.g., VIF calculation, models with only an intercept, perfect multicollinearity, etc.).

• Fix to ensure environment is not attached as an attribute to data frames generated in the Model > Simulate tool

• Update action buttons that initiate calculations when one or more relevant inputs are changed. When, for example, a model should be re-estimated, a spinning “refresh” icon will be shown
• Add option to use a formula for the regress function
• Improved description of standardization process used. Added link to Gelman 2008
• Added an influence plot that shows standardized residuals and cooks-distance

• Fix for nobs in Model > Multinomial logistic regression.
• Fix for write.coeff for use with Model > Multinomial logistic regression
• Fix for decision trees that reference sub-trees. Environment to evaluate the tree is now explicitly provided. This will now also work with (sub) trees loaded from .yaml files
• Decision analysis now allows basic formulas in all parts of the tree
• Added confusion matrix and misclassification error for Model > Multinomial Logistic regression (MNL)
• Fix for saving multiple residual series for MNL
• Added a module for Multinomial Logistic regression (MNL) in the Model > Estimate menu
• Fix for confusion matrix which couldn’t find find the selected dataset in the web-interface
• Improved checks for variables that show no variation
• Numerous small code changes to support enhanced auto-completion, tooltips, and annotations in shinyAce 0.4.1
• Automatically fix faulty spacing in user input in Model > Decision Analysis

• Keyboard shortcut (Enter) when defining variable in Model > Simulate
• Allow series of type ts and date in models and prediction
• Autocompletion for functions in Model > Simulate
• Require shinyAce 0.4.0

• Don’t use simulation variables when their type is not selected
• Provide auto-completion for variables and relevant functions in the Simulate > Functions input
• Keyboard shortcuts for add a defined variable (i.e., press enter after adding the last input value)

• Fix for variable definition in Model > Simulate where names of discrete random variables were not properly ‘fixed’
• Fix for variable selection in Model > Decision analysis > Sensitivity

• Allow any variable in the prediction dataset to be used to customize a prediction when using Predict > Data & Command
• Fix for write.coeff when interactions, quadratic, and/or cubic terms are included in a linear or logistic regression
• Rescale predictions in cv.nn so RMSE and MAE are in the original scale even if the data were standardized for estimation
• Rename scaledf to scale_df for consistency
• Fix for plot sizing and printing of missing values in collaborative filtering
• Fix for cv.nn when weights are used in estimation
• Improve documentation for cross-validation of nn and crtree models (i.e., cv.nn and cv.crtree)
• Fixes for breaking changes in dplyr 0.8.0
• Use an expandable shinyAce input for the formula and function inputs in Model > Simulate
• Fixes for repeated simulation with grid-search
• Use test instead of validation

• Option to add user defined function to simulations. This dramatically increases the flexibility of the simulation tool
• Ensure variable and dataset names are valid for R (i.e., no spaces or symbols), “fixing” the input as needed
• Cross validation functions for decision trees (cv.crtree) and neural networks(cv.nn) that can use various performance metrics for during evaluation e.g., auc or profit
• Option to add square and cube terms in Model > Linear regression and Model > Logistic regression.
• Option to pass additional arguments to shiny::runApp when starting radiant such as the port to use. For example, radiant.model::radiant.model(“https://github.com/radiant-rstats/docs/raw/gh-pages/examples/demo-dvd-rnd.state.rda”, port = 8080)
• Avoid empty string showing up in auto-generated code for model prediction (i.e., pred_data or pred_cmd)
• Fix for VIF based on car for regress and logistic
• Don’t live-update the active tree input to make it easier to save edits to a new tree without adding edits to the existing tree (Model > Decision analysis)
• Fix for NA error when last line of a decision analysis input is a node without a payoff or probability
• Load input (CMD + O) and Save input (CMD + S) keyboard shortcuts for decision analysis

## Minor changes

• Fix for simulations that use a data set as part of the analysis
• Replace non-ASCII characters in example datasets
• Remove rstudioapi as a direct import
• Revert from svg to png for plots in _Report > Rmd_ and _Report > R_.svg scatter plots with many point get to big for practical use on servers that have to transfer images to a local browser
• Removed dependency on methods package

## Major changes

• Various changes to the code to accommodate the use of shiny::makeReactiveBinding. The advantage is that the code generated for Report > Rmd and Report > R will no longer have to use r_data to store and access data. This means that code generated and used in the Radiant browser interface will be directly usable without the browser interface as well.
• Improved documentation and examples

## Major changes

• Show the interval used in prediction for Model > Regression and Model > logistic (e.g., “prediction” or “confidence” for linear regression)
• Auto complete in Model > Decision analysis now provides hints based on the current tree input and any others defined in the app. It also provides suggestions for the basic element of the tree (e.g., type: decision, type: chance, payoff, etc.)
• Updated user messages for Model > Decision analysis when input has errors

## Major changes

• Default interval for predictions from a linear regression is now “confidence” rather than “prediction”
• Estimate model button indicates when the output has been invalidated and the model should be re-estimated
• Combined Evaluate classification Summary and Plot into Evaluate tab
• Upload and download data using the Rstudio file browser. Allows using relative paths to files (e.g., data or images inside an Rstudio project)

## Minor changes

• Require shinyAce 0.3.0 in radiant.data and useSoftTabs for Model > Decision Analysis

## Major changes

• Add Poisson as an option for Model > Simulate

## Bug fixes

• Fix for #43 where scatter plot was not shown for a dataset with less than 1,000 rows
• Fixed example for logistic regression prediction plot
• Fix for case weights when minimum response value is 0

## Minor changes

• Allow character variables in estimation and prediction
• Depend on DiagrammeR 1.0.0

## Major changes

• Residual diagnostic plot for Neural Network regression
• Improved handling of case weights for logistic regression and neural networks

## Minor changes

• Show number of observations used in training and validation in Model > Evaluate classification
• Use Elkan’s formula to adjust probabilities when using priors in crtree (rpart)
• Added options to customize tree generated using crtree (based on rpart)
• Better control of tree plot size in plot.crtree
• Cleanup of crtree code
• Improved printing of NN weights
• Option to change font size in NN plots
• Keyboard shortcut: Press return when cursor is in textInput to store residuals or predictions

## Bug fixes

• Fix for tree labels when (negative) integers are used

## Minor changes

• Cleanup of lists returned by evalbin and confusion
• Add intercept in coefficient tables that can be downloaded for linear and logistic regression or using write.coeff
• Convert logicals to factors in crtree to avoid labels < 0.5 and >= 0.5
• Improved labeling of decision tree splits in crtree. The tooltip (aka hover-over) will contain all levels used, but the tree label may be truncated as needed

## Bug fixes

• Fix input reset when screen size or zoom level is changed

• Renamed ann to nn. The ann function is now deprecated

## Minor changes

• Left align tooltip in DiagrammeR plots (i.e., Model >Decision Analysis and Model > Classification and regression trees)
• Add information about levels in tree splits to tooltips (Model > Classification and regression trees)

## Bug fixes

• Fix to ensure DiagrammeR plots are shown in Rmarkdown report generate in Report > Rmd or Report > R

## Major changes

• Added option to generate normally distributed correlated data in Model > Simulate
• Added option to generate normally distributed simulated data with exact mean and standard deviation in Model > Simulate
• Long lines of code generated for Report > Rmd will be wrapped to enhance readability

## Minor changes

• Default names when saving Decision Analysis input and output are now based on tree name
• Allow browser zoom for tree plots in Model > Decision Analysis and Model > Classification and Regression Trees
• Enhanced keyboard shortcuts for estimation and reporting
• Applied styler to code

## Bug fixes

• Grid search specs ignored when Model > Simulate > Repeat is set to Simulate
• The number of repetitions in Model > Simulate was NA when grid search was used
• Fix for large weights that may cause an integer overflow
• Minor fix for coefficient plot in plot.logistic
• Fixed state setting for decision analysis sensitivity input
• Fixed for special characters (e.g., curly quote) in input for Model > Decision Analysis
• Check that costs are not assigned to terminal nodes in Decision Analysis Trees. Specifying a cost is only useful if it applies to multiple nodes in a branch. If the cost only applies to a terminal node adjust the payoff instead
• Ensure : are followed by a space in the YAML input to Model > Decision Analysis

## Minor change

• Upgraded dplyr dependency to 0.7.1
• Upgraded tidyr dependency to 0.7

## Bug fix

• Fix in crs when a tibble is passed

## Major change

• Added option to use robust standard errors in Linear regression and Logistic regression. The HC1` covariance matrix is used to produce results consistent with Stata

## Minor changes

• Moved coefficient formatting from summary.regress and summary.logistic to make result\$coeff more easily accessible
• Added F-score to Model > Evaluate classification > Confusion

## Bug fixes

• Fixed RSME typo
• Don’t calculate VIFs when stepwise regression selects only one explanatory variable

## Major changes

• Added Model > Naive Bayes based on e1071
• Added Model > Classification and regression trees based on rpart
• Added Model > Collaborative Filtering and example dataset (data/cf.rda)
• Various enhancements to evaluate (binary) classification models
• Added Garson plot and moved all plots to the ANN > Plot tab

## Minor changes

• Improved plot sizing for Model > Decision Analysis
• Show progress indicators if variable acquisition takes some time
• Expanded coefficient csv file for linear and logistic regression
• Show dataset name in output if dataframe passed directly to analysis function
• As an alternative to using the Estimate button to run a model you can now also use CTRL-enter (CMD-enter on mac)
• Use ALT-enter as a keyboard short-cut to generate code and sent to Report > Rmd or Report > R
• Improved documentation on how to customize plots in Report > Rmd or Report > R

## Bug fixes

• Multiple tooltips in sequence in Decision Analysis
• Decision Analysis plot size in PDF was too small
• Replace histogram by distribution in regression plots
• Fix bug in regex for overlapping labels in variables section of Model > Decision Analysis
• Fixes for model with only an intercept (e.g., after stepwise regression)
• Update Predict settings when dataset is changed
• Fix for predict when using center or standardize with a command to generate the predictions
• Show full confusion matrix even if some elements are missing
• Fix for warnings when creating profit and gains charts
• Product dropdown for Model > Collaborative filtering did not list all variables

## Deprecated

• Use of *_each is deprecated