Maximum-Likelihood Cultural Consensus Analysis with Sub-Cultures
Mary C Meyer, Jeffrey G Snodgrass, Michael Lacy
The three functions in the package compute the maximum likelihood estimates of the informants' competence scores, tests for two answer keys with known groups, and finds "best" split of the informants into sub-culture groups.

Gene selection using Lasso for Microarray data with user-defined
genes fixed in model.
Fengfeng Zhou, Youxi Luo, Qinghan Meng, Ruiquan Ge, Guoqin Mai, Jikui Liu
The package implements a convenient procedure for microarray study, which is to do gene selection and classification simultaneously for binary outcomes. Users needn't to tune the parameters and can fix any genes that they desire to keep in the model. The K-folds cross validation results are returned.

Subgroup Specific Optimal Dose Assignment
Contains functions for choosing subgroup specific optimal doses in a phase I dose finding clinical trial and simulating a clinical trial under the subgroup specific time to event continual reassessment method.

Contains functions for choosing subgroup specific optimal doses in a phase I dose finding clinical trial allowing for subgroup combination and simulating a clinical trial under the subgroup specific time to event continual reassessment method.

Chooses subgroup specific optimal doses in a phase I dose finding clinical trial allowing for subgroup combination and simulates clinical trials under the subgroup specific time to event continual reassessment method.
Chapple, A.G., Thall, P.F. (2018). Subgroup-specific dose
finding in phase I clinical trials based on time to toxicity
allowing adaptive subgroup combination. Pharm Stat.
17(6):734–749. doi:10.1002/pst.1891.

Chooses subgroup specific optimal doses in a phase I dose finding clinical trial allowing for subgroup combination and simulates clinical trials under the subgroup specific time to event continual reassessment method.  Chapple, A.G., Thall, P.F. (2018) <doi:10.1002/pst.1891>.

Visual Exploration of Protein Alignments Resulting from Multiple
Substitution Matrices
Substitution matrices are important parameters in protein alignment algorithms. These matrices represent the likelihood that an amino acid will be substituted for another during mutation. This tool allows users to apply predefined and custom matrices and then explore the resulting alignments with interactive visualizations.