Obtain optimum block from Non-overlapping Block Bootstrap method.

Three-dimensional rendering for 'grid' and 'ggplot2' graphics using cubes and cuboids drawn with an oblique projection.  As a special case also supports primary view orthographic projections.  Can be viewed as an extension to the 'isocubes' package <https://github.com/coolbutuseless/isocubes>.

Oblique random survival forests incorporate linear combinations of input variables into random survival forests (Ishwaran, 2008 <DOI:10.1214/08-AOAS169>). Regularized Cox proportional hazard models (Simon, 2016 <DOI:10.18637/jss.v039.i05>) are used to identify optimal linear combinations of input variables.

Applies an objective Bayesian method to the Mb capture-recapture model to estimate the population size N.  The Mb model is a class of capture-recapture methods used to account for variations in capture probability due to animal behavior.  Under the Mb formulation,  the initial capture of an animal may effect the probability of subsequent captures due to their becoming "trap happy" or "trap shy."

An implementation for computing Optimal B-Robust Estimators of two-parameter 
    distribution. The procedure is composed of some equations
    that are evaluated alternatively until the solution is reached. Some tools
    for analyzing the estimates are included. The most relevant is
    covariance matrix computation using a closed formula.

Two-part system for first collecting then managing direct
    observation data, as described by Hibbing PR, Ellingson LD,
    Dixon PM, & Welk GJ (2018) <doi:10.1249/MSS.0000000000001486>.

Checks that a given dataset passes user-specified 
    rules. The main functions are observe_if() and inspect().

Implements the objective Bayesian methodology proposed in Consonni and Deldossi in order to choose the optimal experiment that better discriminate between competing models, see Deldossi and Nai Ruscone (2020) <doi:10.18637/jss.v094.i02>.

Observational studies are limited in that there could be an unmeasured variable related to both the response variable and the primary predictor.  If this unmeasured variable were included in the analysis it would change the relationship (possibly changing the conclusions).  Sensitivity analysis is a way to see how much of a relationship needs to exist with the unmeasured variable before the conclusions change.  This package provides tools for doing a sensitivity analysis for regression (linear, logistic, and cox) style models.