Implementation of the Control Polygon Reduction and Control Net
    Reduction methods for finding parsimonious B-spline regression models.

Implements the three-step workflow for robust analysis of change in 
    two repeated measurements of continuous outcomes, described in Ning et al.
    (in press), "Robust estimation of the effect of an exposure on the change in
    a continuous outcome", BMC Medical Research Methodology.

Calculate date of birth, age, and gender, and generate anonymous
    sequence numbers from CPR numbers.
    <https://en.wikipedia.org/wiki/Personal_identification_number_(Denmark)>.

The primary function makeCPMSampler() generates a sampler function which performs the correlated pseudo-marginal method of Deligiannidis, Doucet and Pitt (2017) <arXiv:1511.04992>. If the 'rho=' argument of makeCPMSampler() is set to 0, then the generated sampler function performs the original pseudo-marginal method of Andrieu and Roberts (2009) <DOI:10.1214/07-AOS574>. The sampler function is constructed with the user's choice of prior, parameter proposal distribution, and the likelihood approximation scheme. Note that this algorithm is not automatically tuned--each one of these arguments must be carefully chosen.

Load Current Population Survey (CPS) microdata into R using the
    'Census Bureau Data' API
    (<https://www.census.gov/data/developers/data-sets.html>), including basic
    monthly CPS and CPS ASEC microdata.

Implements multiple change searching algorithms for a variety of 
    frequently considered parametric change-point models. In particular, it 
    integrates a criterion proposed by Zou, Wang and Li (2020) 
    <doi:10.1214/19-AOS1814> to select the number of change-points in a 
    data-driven fashion. Moreover, it also provides interfaces for 
    user-customized change-point models with one's own cost function and 
    parameter estimation routine. It is easy to get started with the 
    cpss.* set of functions by accessing their documentation pages 
    (e.g., ?cpss).