SparseGrid, numDeriv, VineCopula (>= 2.0.5), methods, stats,
MASS, utils, yarrr, progress, crayon

Ostap Okhrin <ostap.okhrin@tu-dresden.de>,
Simon Trimborn <trimborn.econometrics@gmail.com>,
Martin Waltz <martin.waltz@tu-dresden.de>
foreach, parallel, doSNOW, R.utils, SparseGrid, numDeriv,
VineCopula (>= 2.0.5), methods, stats, MASS, utils, yarrr,
progress, crayon

Goodness of Fit Noise Analysis Using Monte Carlo Techniques
Joseph G Kreke, PhD; Harris, Inc.
Sangeet Khemlani, PhD; Naval Research Laboratory.
Greg Trafton, PhD; Naval Research Laboratory.
Goodness-of-fit metrics, such as R-Squared, RMSE, etc., share a sensitivity to noise, dependent on the degrees of freedom.  Some metrics, such as R-Squared, decrease with increasing dof and some, such as RMSE, increase with increasing dof.  This package calculates the noise baseline (ceiling) by random sampling, calculating the metric’s value for each sample and counting the number of samples below a desired level, 95% by default. If one’s measure is above (below) the calculation corresponding to the desired level, then the measurement is distinguishable from noise.  In addition, the ratio of the measurement to the calculated level provides a way to compare measurements of different degrees of freedom.
R (>= 3.3.1), ggplot2, grDevices, scales, stats

c(person(given = 'Aditya',
family = 'Mishra',
role = c('aut', 'cre'),
email = 'amishra@flatironinstitute.org'),
person(given = 'Kun',
family = 'Chen',
role = 'aut'))
Divide and conquer approach for estimating low-rank and sparse coefficient matrix in the generalized co-sparse factor regression. Please refer the manuscript 'Mishra, Aditya, Dipak K. Dey, Yong Chen, and Kun Chen. Generalized co-sparse factor regression. Computational Statistics & Data Analysis 157 (2021): 107127' for more details.
Rcpp (>= 0.12.9), MASS, magrittr, rrpack, glmnet
Aditya Mishra [aut, cre],
Kun Chen [aut]

Fast DocumentTermMatrix and TermDocumentMatrix Creation
c(person("Tyler", "Rinker", email =
"tyler.rinker@gmail.com", role = c("aut", "cre")))
Harness the power of 'quanteda', 'data.table' & 'stringi'
to quickly generate 'tm' DocumentTermMatrix and
TermDocumentMatrix data structures.
data.table (>= 1.9.5), quanteda, slam, SnowballC, stats, tm