path.singboost: Coefficient paths for SingBoost
In gfboost: Gradient-Free Gradient Boosting

Runs SingBoost but saves the coefficients paths. If no coefficient path plot is needed, just use singboost.

path.singboost(
  D,
  M = 10,
  m_iter = 100,
  kap = 0.1,
  singfamily = Gaussian(),
  best = 1,
  LS = FALSE
)

`D`	Data matrix. Has to be an n \times (p+1)-dimensional data frame in the format (X,Y). The X-part must not contain an intercept column containing only ones since this column will be added automatically.
`M`	An integer between 2 and `m_iter`. Indicates that in every M-th iteration, a singular iteration will be performed. Default is 10.
`m_iter`	Number of SingBoost iterations. Default is 100.
`kap`	Learning rate (step size). Must be a real number in ]0,1]. Default is 0.1 It is recommended to use a value smaller than 0.5.
`singfamily`	A Boosting family corresponding to the target loss function. See .`mboost` for families corresponding to standard loss functions. May also use the loss functions for ranking losses provided in this package. Default is `Gaussian()` for which SingBoost is just standard L_2-Boosting.
`best`	Needed in the case of localized ranking. The parameter `K` of the localized ranking loss will be computed by best \cdot n (rounded to the next larger integer). Warning: If a parameter `K` is inserted into the `LocRank` family, it will be ignored when executing SingBoost.
`LS`	If a `singfamily` object that is already provided by `mboost` is used, the respective Boosting algorithm will be performed in the singular iterations if `Ls` is set to `TRUE`. Default is `FALSE`.

`Selected variables`	Names of the selected variables.
`Coefficients`	The selected coefficients as an (p+1)-dimensional vector (i.e., including the zeroes).
`Freqs`	Selection frequencies and a matrix for intercept and coefficient paths, respectively.
`Intercept path`	The intercept path as an m_{iter}-dimensional vector.
`Coefficient path`	The coefficient paths as a 2 \cdot m_{iter} \times 2-dimensional matrix.