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bioc
hopach:Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH)
The HOPACH clustering algorithm builds a hierarchical tree of clusters by recursively partitioning a data set, while ordering and possibly collapsing clusters at each level. The algorithm uses the Mean/Median Split Silhouette (MSS) criteria to identify the level of the tree with maximally homogeneous clusters. It also runs the tree down to produce a final ordered list of the elements. The non-parametric bootstrap allows one to estimate the probability that each element belongs to each cluster (fuzzy clustering).
Maintained by Katherine S. Pollard. Last updated 5 months ago.
6.05 score 54 scripts 5 dependents
erossiter
catSurv:Computerized Adaptive Testing for Survey Research
Provides methods of computerized adaptive testing for survey researchers. See Montgomery and Rossiter (2020) <doi:10.1093/jssam/smz027>. Includes functionality for data fit with the classic item response methods including the latent trait model, Birnbaum`s three parameter model, the graded response, and the generalized partial credit model. Additionally, includes several ability parameter estimation and item selection routines. During item selection, all calculations are done in compiled C++ code.
Maintained by Erin Rossiter. Last updated 11 months ago.
12 stars 4.68 score 3 scripts
malfly
AutoWMM:Perform the Weighted Multiplier Method on Trees
When many possible multiplier method estimates of a target population are available, a weighted sum of estimates from each back-calculated path can be achieved with this package. Variance-minimizing weights are used and with any admissible tree-structured data. The methodological basis used to create this package can be found in Flynn (2023) <http://hdl.handle.net/2429/86174>.
Maintained by Mallory J Flynn. Last updated 5 months ago.
4.32 score 3 scripts 1 dependents