# Package modEvA 3.0 is now on CRAN!

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This version of R package “**mod**el **Ev**aluation and **A**nalysis” includes some **bug fixes** (thanks to Huijie Qiao, Ying-Ju Tessa Chen, Oswald van Ginkel and Alba Estrada), some **new functions** (*predPlot*, *confusionLabel*, and *mod2obspred*, which is now used internally by several others), and it implements more classes (‘gam’, ‘gbm’, ‘randomForest’, ‘bart’) besides ‘glm’ for the ‘`model`

‘ argument in most functions. There are also a few **argument additions and improvements** — e.g.,* varPart *now has an option to plot the circles in colour (thanks to Oswald van Ginkel). You can read the package NEWS file for details.

You can now **install the newest version of modEvA from CRAN** and try out some of these new features:

`install.packages("modEvA")`

`library(modEvA)`

**# load some other packages to make different models:**```
library(gam)
library(gbm)
```

**# take a sample dataset and create a numeric binary response variable:**```
data(kyphosis)
head(kyphosis)
kyphosis$Kyphosis <- ifelse(kyphosis$Kyphosis == "present", 1, 0)
```

**# make different models with this response variable:**```
mod_glm <- glm(Kyphosis ~ Age + Number + Start, family = binomial, data = kyphosis)
mod_gam <- gam(Kyphosis ~ s(Age) + s(Number) + s(Start), family = binomial, data = kyphosis)
mod_gbm <- gbm(Kyphosis ~ Age + Number + Start, distribution = "bernoulli", data = kyphosis)
```

**# get different evaluation metrics/plots directly from the model objects:****# e.g., density of predictions for presences and absences:**```
predPlot(model = mod_glm, main = "GLM")
predPlot(model = mod_gam, main = "GAM")
predPlot(model = mod_gbm, main = "GBM")
predDensity(model = mod_glm, main = "GLM")
predDensity(model = mod_gam, main = "GAM")
predDensity(model = mod_gbm, main = "GBM")
```

# # (area under the) ROC and Precision-Recall curves:

```
AUC(model = mod_glm, main = "GLM")
AUC(model = mod_gam, main = "GAM")
AUC(model = mod_gbm, main = "GBM")
AUC(model = mod_glm, curve = "PR", main = "GLM")
AUC(model = mod_gam, curve = "PR", main = "GAM")
AUC(model = mod_gbm, curve = "PR", main = "GBM")
```

You can try also other functions such as ‘`threshMeasures`

‘, ‘`MillerCalib`

‘ or ‘`HLfit`

‘. And check out the colour version of ‘`varPart`

‘:

`varPart(A = 0.456, B = 0.315, AB = 0.852, A.name = "Spatial", B.name = "Climatic", col = TRUE)`

```
varPart(A = 0.456, B = 0.315, C = 0.281, AB = 0.051, BC = 0.444,
AC = 0.569, ABC = 0.624, A.name = "Spatial", B.name = "Human",
C.name = "Climatic", col = TRUE)
```

Feel free to send me any bug reports! Feature requests/suggestions are also welcome, though I can’t promise a timely response… And remember to look for the latest **package updates in the development page on R-Forge**!

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