Packaged: 2015-05-10 16:34:32 UTC; michelballings

Title: Binary Classifier and Regression Model Interpretation Functions
Description: Compute permutation- based performance measures and create partial
dependence plots for (cross-validated) 'randomForest' and 'ada' models.
Packaged: 2015-06-23 14:03:27 UTC; michelballings

Packaged: 2016-03-19 20:36:41 UTC; michelballings

Title: Flexible Tools for Estimating Interactions
person("Matthew", "Blackwell", email = "mblackwell@gov.harvard.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-3689-9527")),
person("Olson", "Michael", email = " michael.p.olson@wustl.edu", role = c("aut")))
Suggests: knitr, ggplot2, lmtest, rmarkdown
Description: A set of functions to estimate interactions flexibly in the face of possibly many controls. Implements the procedures described in Blackwell and Olson (2022) <doi:10.1093/restud/rdt044>.
Packaged: 2022-04-20 14:31:39 UTC; mblackwell

Title: Weighted Logrank Tests and NPMLE for interval censored data
Depends: R (>= 2.2.1), stats, survival, perm
Description: Functions to fit nonparametric survival curves, plot them,
and perform logrank or Wilcoxon type tests.

Depends: R (>= 2.2.1), stats, survival, perm, Icens, MLEcens

Depends: R (>= 2.2.1), stats, survival, perm (>= 1.0), Icens, MLEcens

Description: Functions to fit nonparametric survival curves, plot them, and perform logrank or Wilcoxon type tests.

Title: Weighted Logrank Tests and NPMLE for Interval Censored Data

Description: Functions to fit nonparametric survival curves, plot them, and perform logrank or Wilcoxon type tests [see Fay and Shaw <doi:10.18637/jss.v036.i02>].

Title: Time-Weighted Averaging for Interval Data
Description: Perform fast and memory efficient time-weighted averaging of values
measured over intervals into new arbitrary intervals.
This package is useful in the context of data measured or represented
as constant values over intervals on a one-dimensional discrete axis
(e.g. time-integrated averages of a curve over defined periods).
This package was written specifically to deal with air
pollution data recorded or predicted as averages over sampling periods.
Data in this format often needs to be shifted to non-aligned periods
or averaged up to periods of longer duration (e.g. averaging data measured over
sequential non-overlapping periods to calendar years).