Sample sizes are often small due to hard to reach target populations,
    rare target events, time constraints, limited budgets, or ethical considerations.
    Two statistical methods with promising performance in small samples are the
    nonparametric bootstrap test with pooled resampling method, which is the
    focus of Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>,
    and informative hypothesis testing, which is implemented in the 'restriktor'
    package. The 'npboottprmFBar' package uses the nonparametric bootstrap test
    with pooled resampling method to implement informative hypothesis testing.
    The bootFbar() function can be used to analyze data with this method and the
    persimon() function can be used to conduct performance simulations on type-one
    error and statistical power.

A variety of functions for the best known and most innovative approaches to nonparametric boundary estimation. The selected methods are concerned with empirical, smoothed, unrestricted as well as constrained fits under both separate and multiple shape constraints. They cover robust approaches to outliers  as well as data envelopment techniques based on piecewise polynomials, splines, local linear fitting, extreme values and kernel smoothing. The package also seamlessly allows for Monte Carlo comparisons among these different estimation methods.  Its use is illustrated via a number of empirical applications and simulated examples.

An array of nonparametric and parametric estimation methods for cognitive diagnostic models, including nonparametric classification of examinee attribute profiles, joint maximum likelihood estimation (JMLE) of examinee attribute profiles and item parameters, and nonparametric refinement of the Q-matrix, as well as conditional maximum likelihood estimation (CMLE) of examinee attribute profiles given item parameters and CMLE of item parameters given examinee attribute profiles. Currently the nonparametric methods in the package support both conjunctive and disjunctive models, and the parametric methods in the package support the DINA model, the DINO model, the NIDA model, the G-NIDA model, and the R-RUM model.