zoo, stats, plyr, coin, Rdpack, exactRankTests (>= 0.8-29),
ggplot2 (>= 1.0.1)
Builds the
coincident profile proposed by Martinez, W and Nieto, Fabio H and Poncela, P (2016)
<doi:10.1016/j.spl.2015.11.008>.
This methodology studies the relationship between a couple of
time series based on the the set of turning points of each
time series. The coincident profile establishes if two time
series are coincident, or one of them leads the second.

R.utils, RSQLite, biganalytics, bigmemory, collUtils, dplyr,
ggplot2, methods, stringr, stats, haplo.stats
Implements a generalized version of the CDH test (<DOI:10.1371/journal.pone.0028145> and <DOI:10.1186/s12859-016-1006-9>)
for detecting compound heterozygosity on a
genome-wide level, due to usage of generalized linear models it allows flexible
analysis of binary and continuous traits with covariates.

R.utils, RSQLite, biganalytics, bigmemory, collUtils, dplyr,
ggplot2, methods, stringr, stats
Implements a generalized version of the CDH test
<DOI:10.1371/journal.pone.0028145> for detecting compound heterozygosity on a
genome-wide level, due to usage of generalized linear models it allows flexible
analysis of binary and continuous traits with covariates.

Collapses Levels, Computes Information Value and WoE
Provides functions to collapse levels of an attribute based on response rates.
It also provides functions to compute and display information value, and weight of evidence (WoE) for the attributes, and to convert numeric variables to categorical ones by binning.
These functions only work for binary classification problems.

An update to the previous version ( 0.1.0 ) of the package.
Functions in the previous version required the binary outcome variable of the data set to be strictly a factor.In version 0.2.0  the binary outcome variable may be both a factor (with two levels) or an integer (or numeric ).
All functions of the previous version have been retained.No new functions have been added.
Like the previous version,provides functions to collapse the levels of an attribute based on response rates.
It also provides functions to compute and display information value, and weight of evidence (WoE) for the attributes, and to convert numeric variables to categorical ones by binning.
These functions only work for binary classification problems.