Goodness-of-fit tests for the family of skew-normal models
GOFSN is a package that implements a method for checking
if a skew-normal model fits the observed dataset, when all
parameters are unknown. While location and scale parameters are
estimated by moment estimators, the shape parameter is
integrated with respect to the prior predictive distribution,
as proposed in (BOX, 1980). A default and proper prior on
skewness parameter is used to obtain the prior predictive
distribution, as proposed in (CABRAS, CASTELLANOS, 2008).
Goodness-of-fit tests, here proposed, depend only on sample
size and exhibit full agreement between nominal and actual
size. This package implements EDF statistics
Kolmogorov-Smirnov(D), Cram\'er-von Mises(W2) and proposes some
simple algorithms (SimulD,SimulW2) to approximate their
respective marginal predictive distributions. It also has
functions (ks.sn,W2.sn) that calculate the p-value on observed
data.

Interactive Document for Working with Goodness of Fit Analysis
An interactive document on  the topic of goodness of fit analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://predanalyticssessions1.shinyapps.io/ChiSquareGOF/>.

Accounting for genes' functional-non-equivalence within pathways in classical Gene-set-analysis.

Fit Generalized Odds Rate Mixture Cure Model with Interval
Censored Data
Generalized Odds Rate Mixture Cure (GORMC) model is a flexible model of fitting survival data with a cure fraction, including the Proportional Hazards Mixture Cure (PHMC) model and the Proportional Odds Mixture Cure Model as special cases. This package fit the GORMC model with interval censored data.

Computation of functional similarities between GO terms and gene products
Holger Froehlich <h.froehlich@dkfz.de> contributions by Tim Beissbarth <beissbarth@wehi.edu.au>
This package implements several functions useful for
computing similarities between GO terms and gene products based on their GO annotation