A tool to calculate the performance of a time series in a specific date or period. It is more intended for data analysis in the fields of finance, banking, telecommunications or operational marketing.

The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen from a set of optional candidates by cross-validation or alternatively generated from the input data.

A fast (C) implementation of the iterative proportional fitting
    procedure.

Performs iterative proportional updating given a seed table and
  an arbitrary number of marginal distributions. This is commonly used in
  population synthesis, survey raking, matrix rebalancing, and other
  applications. For example, a household survey may be weighted to match the
  known distribution of households by size from the census. An origin/
  destination trip matrix might be balanced to match traffic counts.
  The approach used by this package is based on a paper from
  Arizona State University (Ye, Xin, et. al. (2009)
  <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.537.723&rep=rep1&type=pdf>).
  Some enhancements have been made to their work including primary and 
  secondary target balance/importance, general marginal agreement, and weight 
  restriction.

Algorithms and utility functions for indoor positioning using fingerprinting techniques. 
    These functions are designed for manipulation of RSSI (Received Signal Strength Intensity) data 
    sets, estimation of positions,comparison of the performance of different models, and graphical 
    visualization of data. Machine learning algorithms and methods such as k-nearest neighbors or
    probabilistic fingerprinting are implemented in this package to perform analysis
    and estimations over RSSI data sets.