The main features of this package are: implementation of the **SVM-Maj** majorization algorithm for SVMs, handling of nonlinearity through splines and kernels, the ability to handle several error functions (among other the classic hinge, quadratic hinge and Huber hinge error).

The main functions of the package are `svmmaj`

, which estimates the SVM, and `svmmajcrossval`

, which performs a grid search of *k*-fold cross validations using **SVM-Maj** to find the combination of input values, (such as `lambda`

and `degree`

in the case of a polynomial kernel) giving the best prediction performance.

The former function requires the `n x k`

attribute matrix `X`

and the `n x 1`

vector `y`

with class labels. Apart from the data objects, other parameter input values can be given as input to tune the model:
- `lambda`

,
- `hinge`

,
- `weights.obs`

,
- `scale`

, and
- parameters for nonlinearities and settings of the algorithm itself.

For example,

```
svmmaj(X, y, lambda = 2, hinge = "quadratic", scale = "interval")
```

runs the SVM model with `lambda = 2`

, using a quadratic hinge and for each attribute, the values are scaled to the interval [0,1].
The function `svmmajcrossval`

uses the same parameter input values and additionally the parameters to be used as grid points of the *k*-fold cross validation. These parameters should be given in the list object `search.grid`

, e.g.,

```
svmmajcrossval(X, y, search.grid = list(lambda = c(1, 2, 4)))
```

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