Browse Items (15 total)

Even though adaptive two-stage designs with unblinded interim analyses are becoming
increasingly popular in clinical trial designs, there is a lack of statistical software to make
their application more straightforward. The package adoptr fills…

A plethora of disparate statistical methods have been proposed for subgroup identification to help tailor treatment decisions for patients. However a majority of them do not
have corresponding R packages and the few that do pertain to particular…

Gene expression data provide an abundant resource for inferring connections in gene
regulatory networks. While methodologies developed for this task have shown success, a
challenge remains in comparing the performance among methods. Gold-standard…

NScluster is an R package used for simulation and parameter estimation for NeymanScott cluster point process models and their extensions. For parameter estimation, NScluster uses the maximum Palm likelihood estimation procedure. As some estimation…

Monitoring data while it is processed and transformed can yield detailed insight into
the dynamics of a (running) production system. The lumberjack package is a lightweight
package allowing users to follow how an R object is transformed as it is…

The MixGHD package for R performs model-based clustering, classification, and discriminant analysis using the generalized hyperbolic distribution (GHD). This approach
is suitable for data that can be considered a realization of a (multivariate)…

We present mexhaz, an R package for fitting flexible hazard-based regression models
with the possibility to add time-dependent effects of covariates and to account for a twolevel hierarchical structure in the data through the inclusion of a normally…

An increasing number of time-consuming simulators exhibit a complex noise structure
that depends on the inputs. For conducting studies with limited budgets of evaluations,
new surrogate methods are required in order to simultaneously model the mean…

FRK is an R software package for spatial/spatio-temporal modeling and prediction
with large datasets. It facilitates optimal spatial prediction (kriging) on the most commonly used manifolds (in Euclidean space and on the surface of the sphere), for…

Multivariate time series observations are increasingly common in multiple fields of
science but the complex dependencies of such data often translate into intractable models
with large number of parameters. An alternative is given by first reducing…