Computes solutions for linear and logistic regression models with potentially high-dimensional categorical predictors. This is done by applying a nonconvex penalty (SCOPE) and computing solutions in an efficient path-wise fashion. The scaling of the solution paths is selected automatically. Includes functionality for selecting tuning parameter lambda by k-fold cross-validation and early termination based on information criteria. Solutions are computed by cyclical block-coordinate descent, iterating an innovative dynamic programming algorithm to compute exact solutions for each block.

Several functions for working with mixed effects regression models for limited dependent variables. The functions facilitate post-estimation of model predictions or margins, and comparisons between model predictions for assessing or probing moderation. Additional helper functions facilitate model comparisons and implements simulation-based inference for model predictions of alternative-specific outcome models. See also, Melamed and Doan (2024, ISBN: 978-1032509518).

Cohort plAtform Trial Simulation whereby every cohort consists of two arms, 
             control and experimental treatment. Endpoints are co-primary binary endpoints and
             decisions are made using either Bayesian or frequentist decision rules. 
             Realistic trial trajectories are simulated and the operating characteristics of the 
             designs are calculated.

Provides the tools to produce catseye plots, principally
    by catseyesplot() function which calls R's standard plot() function internally, or alternatively
    by the catseyes() function to overlay the catseye plot onto an existing
    R plot window. Catseye plots illustrate the normal distribution of the mean (picture a 
    normal bell curve reflected over its base and rotated 90 degrees), with a shaded confidence
    interval; they are an intuitive way of illustrating and comparing normally distributed estimates,
    and are arguably a superior alternative to standard confidence intervals, since they show the full
    distribution rather than fixed quantile bounds. The catseyesplot and catseyes functions require
    pre-calculated means and standard errors (or standard deviations), provided as numeric vectors;
    this allows the flexibility of obtaining this information from a variety of sources, such as
    direct calculation or prediction from a model.  Catseye plots, as illustrations of the
    normal distribution of the means, are described in Cumming (2013 & 2014).
    Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 27, 7-29. <doi:10.1177/0956797613504966> pmid:24220629.