Classification trees for ordinal responses
Giuliano Galimberti, Gabriele Soffritti, Matteo Di Maso
This package contains functions that allow to build
classification trees for ordinal responses within the CART
framework. The trees are grown using the Generalized Gini
impurity function, where the misclassification costs are given
by the absolute or squared differences in scores assigned to
the categories of the response. Pruning is based on the total
misclassification rate or on the total misclassification cost.

Classification Trees for Ordinal Responses
Recursive partitioning methods to build
classification trees for ordinal responses within the CART
framework. Trees are grown using the Generalized Gini
impurity function, where the misclassification costs are given
by the absolute or squared differences in scores assigned to
the categories of the response. Pruning is based on the total
misclassification rate or on the total misclassification cost.

Provides algorithims for randomly sampling a feasible set defined
by a given total and number of elements using integer partitioning.

Recognising Visual Charting Patterns in Time Series Data
person("Stephan", "Maier", email = "sm297@st-andrews.ac.uk", role =
c("aut", "cre"))
Generating visual charting patterns and noise,
smoothing to find a signal in noisy time series and enabling
users to apply their findings to real life data.

RobustPCA: Decompose a Matrix into Low-Rank and Sparse
Components
person("Maciek", "Sykulski", email = "macieksk@gmail.com", role = c("aut", "cre"))
Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Candes, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust principal component analysis?. Journal of the ACM (JACM), 58(3), 11. prove that we can recover each component individually under some suitable assumptions. It is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This package implements this decomposition algorithm resulting with Robust PCA approach.