It provides functions to compute the values of different modifications of the Rand and Wallace indices. The indices are used to measure the stability or similarity of two partitions obtained on two different sets of units with a non-empty intercept. Splitting and merging of clusters can (depends on the selected index) have a different effect on the value of the indices.  The indices are proposed in Cugmas and Ferligoj (2018) <http://ibmi.mf.uni-lj.si/mz/2018/no-1/Cugmas2018.pdf>.

Various methods for MRI tissue classification.

Computes multiple correlation coefficient when the data matrix is given and tests its significance.

Andrew Gelman <gelman@stat.columbia.edu>, Michael Malecki
<malecki@wustl.edu>, Daniel Lee <bearlee@alum.mit.edu>, Yu-Sung
Su <ys463@columbia.edu>,
Michael Malecki <malecki@gmail.com>, Daniel Lee
<bearlee@alum.mit.edu>
R (>= 2.14), methods, Matrix, blme, arm, sp, lattice, plyr,
reshape2, mrpdata

Data for Multilevel Regression and Poststratification
Andrew Gelman <gelman@stat.columbia.edu>, Michael Malecki
<malecki@wustl.edu>, Daniel Lee <bearlee@alum.mit.edu>, Yu-Sung
Su <ys463@columbia.edu>, Jiqiang Guo <jg3263@columbia.edu>
Jiqiang Guo <jg3263@columbia.edu>, Michael Malecki
<malecki@gmail.com>, Daniel Lee <bearlee@alum.mit.edu>
Data for package Multilevel Regressions and
Poststratification (MRP) U.S. States and Territories map data
(spmap.states) Combined 2004-2005 poll data: support for
same-sex civil marriage (samesexmarriage) US Census Public Use
Microdata Sample (PUMS) for Poststratification (mrp.census)
States and Regions for MRP (mrp.region) CCES.complete

Regression Analysis for Very Large Data Sets via Merge and
Reduce
Esther Denecke [aut], Leo N. Geppert [aut, cre], Steffen Maletz [ctb],
R Core Team [ctb]
Frequentist and Bayesian linear regression for large data sets. Useful
when the data does not fit into memory (for both frequentist and Bayesian regression),
to make running time manageable (mainly for Bayesian regression), and to reduce
the total running time because of reduced or less severe memory-spillover into
the virtual memory. This is an implementation of Merge & Reduce for linear regression
as described in Geppert, L.N., Ickstadt, K., Munteanu, A., & Sohler, C. (2020).
'Streaming statistical models via Merge & Reduce'. International Journal of
Data Science and Analytics, 1-17, <doi:10.1007/s41060-020-00226-0>.