Survey sampling using permanent random numbers (PRN's). A solution to the
    problem of unknown overlap between survey samples, which leads to a low
    precision in estimates when the survey is repeated or combined with other
    surveys. The PRN solution is to supply the U(0, 1) random numbers to the 
    sampling procedure, instead of having the sampling procedure generate them.
    In Lindblom (2014) <doi:10.2478/jos-2014-0047>, and therein cited articles,
    it is shown how this is carried out and how it improves the estimates. This
    package supports two common fixed-size sampling procedures (simple random 
    sampling and probability-proportional-to-size sampling) and includes a 
    function for transforming the PRN's in order to control the sample overlap.

Optogenetics is a new tool to study neuronal circuits that have been genetically modified to allow stimulation by flashes of light.  This package implements the methodological framework, Point-process Response model for Optogenetics (PRO), for analyzing data from these experiments.  This method provides explicit nonlinear transformations to link the flash point-process with the spiking point-process.  Such response functions can be used to provide important and interpretable scientific insights into the properties of the biophysical process that governs neural spiking in response to optogenetic stimulation.

A collection of tools to facilitate standardized analysis 
    and graphical procedures when using the National Cancer Institute’s 
    Patient-Reported Outcomes version of the Common Terminology Criteria for 
    Adverse Events (PRO-CTCAE) and other PRO measurements.

Models can be improved by post-processing class
    probabilities, by: recalibration, conversion to hard probabilities,
    assessment of equivocal zones, and other activities. 'probably'
    contains tools for conducting these operations as well as calibration
    tools and conformal inference techniques for regression models.

Functions and datasets  to accompany J. Albert and J. Hu, "Probability and Bayesian Modeling", CRC Press, (2019, ISBN: 1138492566).