Daniel J. Eck <eckxx049@umn.edu>, Charles J. Geyer
<charlie@stat.umn.edu>
Provides anova and summary tables for poisson and logistic
regression when the MLE does not exist in the usual sense.

Daniel Peña <daniel.pena@uc3m.es>,
Ezequiel Smucler <ezequiels.90@gmail.com>,
Victor Yohai <vyohai@dm.uba.ar>
Functions to compute the Generalized Dynamic Principal Components
introduced in Peña and Yohai (2016) <DOI:10.1080/01621459.2015.1072542>.
xts, methods, Rcpp (>= 0.12.7), parallel, doParallel, foreach

xts, zoo, methods, Rcpp (>= 0.12.7), parallel, doParallel,
foreach

Functions to compute the Generalized Dynamic Principal Components
introduced in Peña and Yohai (2016) <DOI:10.1080/01621459.2015.1072542>. The implementation
includes an automatic procedure proposed in Peña, Smucler and Yohai (2020) <DOI:10.18637/jss.v092.c02>
for the identification of both the number of lags to be used
in the generalized dynamic principal components as well as the number of components required
for a given reconstruction accuracy.

Contains a function called gds() which accepts three input
parameters like lower limits, upper limits and the frequencies of the
corresponding classes. The gds() function calculate and return the values
of mean ('gmean'), median ('gmedian'), mode ('gmode'), variance ('gvar'), standard
deviation ('gstdev'), coefficient of variance ('gcv'), quartiles ('gq1', 'gq2', 'gq3'),
inter-quartile range ('gIQR'), skewness ('g1'), and kurtosis ('g2') which facilitate
effective data analysis. For skewness and kurtosis calculations we use moments.

CoreArray Generic Data Structurs (GDS) R Interface
R interface of CoreArray GDS is based on the CoreArray
project initiated and developed by Xiuwen Zheng from 2007.

CoreArray Genomic Data Structure (GDS) R Interface
CoreArray project is to develop portable and scalable
storage technologies for bioinformatics data, allowing parallel
computing at the multicore and cluster levels.

CoreArray project is to develop portable and scalable storage technologies for bioinformatics data, allowing parallel computing at the multicore and cluster levels.