Enables automated visualization of variable
distribution and changes over time for predictive model building.
It efficiently computes summary statistics aggregated by time for
large datasets, and create plots for variable level monitoring.

Ornstein-Uhlenbeck models for phylogenetic comparative hypotheses
Fit and compare Ornstein-Uhlenbeck models for evolution
along a phylogenetic tree.

Aaron A. King <king@tiem.utk.edu> and Marguerite A. Butler <mabutler@utk.edu>

Aaron A. King <kingaa@umich.edu> and Marguerite A. Butler <mabutler@utk.edu>

Ornstein-Uhlenbeck Models for Phylogenetic Comparative
Hypotheses
Aaron A. King <kingaa@umich.edu> and Marguerite A. Butler <mbutler@hawaii.edu>
Fit and compare Ornstein-Uhlenbeck models for evolution along a phylogenetic tree.
R(>= 3.0.0), methods, stats, graphics, subplex
ouchtree.R ape2ouch.R glssoln.R rmvnorm.R brown.R hansen.R
methods.R paint.R plot.R

R(>= 3.6), methods, stats, graphics, subplex

methods, stats, graphics, grDevices, utils, subplex
'anolis.R' 'package.R' 'ouchtree.R' 'ape2ouch.R' 'bimac.R'
'rmvnorm.R' 'glssoln.R' 'brown.R' 'simulate.R' 'hansen.R'
'bootstrap.R' 'coef.R' 'loglik.R' 'paint.R' 'plot.R' 'print.R'
'summary.R' 'update.R'
Aaron A. King [aut, cre],
Marguerite A. Butler [ctb]

'anolis.R' 'print.R' 'package.R' 'ouchtree.R' 'ape2ouch.R'
'bimac.R' 'bootstrap.R' 'update.R' 'simulate.R' 'summary.R'
'logLik.R' 'coef.R' 'rmvnorm.R' 'glssoln.R' 'brown.R' 'plot.R'
'hansen.R' 'paint.R'

'anolis.R' 'print.R' 'package.R' 'ouchtree.R' 'ape2ouch.R'
'bimac.R' 'bootstrap.R' 'update.R' 'simulate.R' 'summary.R'
'logLik.R' 'coef.R' 'rmvnorm.R' 'glssoln.R' 'brown.R'
'geospiza.R' 'plot.R' 'hansen.R' 'paint.R'

R(>= 2.6.1), methods, stats, graphics, subplex

Ornstein-Uhlenbeck models for phylogenetic comparative
hypotheses
Aaron A. King <kingaa@umich.edu> and Marguerite A. Butler
<mbutler@hawaii.edu>

R(>= 2.9.1), methods, stats, graphics, subplex

ouchtree.R ape2ouch.R glssoln.R rmvnorm.R brown.R hansen.R
methods.R paint.R plot.R deprecated.R

Multivariate Outlier Detection and Replacement
Provides a random forest based implementation of
the method described in Chapter 7.1.2 (Regression model based anomaly
detection) of Chandola et al. (2009)
<doi.acm.org/10.1145/1541880.1541882>. It works as follows: Each
numeric variable is regressed onto all other variables by a random
forest. If the scaled absolute difference between observed value and
out-of-bag prediction of the corresponding random forest is
suspiciously large, then a value is considered an outlier. The package
offers different options to replace such outliers, e.g. by realistic
values found via predictive mean matching. Once the method is trained
on a reference data, it can be applied to new data.
stats, graphics, FNN, ranger, missRanger (>= 2.1.0)