Allows for nonparametric regression where one assumes that the signal is given by the sum of a piecewise constant function and a smooth function. More precisely, it implements the estimator PCpluS (piecewise constant plus smooth regression estimator) from Pein and Shah (2025) <doi:10.48550/arXiv.2112.03878>.

Implementation of the pattern recognition technique Principal
    Component Pursuit tailored to environmental health data, as described
    in Gibson et al (2022) <doi:10.1289/EHP10479>.

Set of functions for analysis of Principal Coordinates of Phylogenetic Structure (PCPS).

Calculates the amplification efficiency and curves from real-time 
  quantitative PCR (Polymerase Chain Reaction) data. Estimates the relative 
  expression from PCR data using the double delta CT and the standard curve 
  methods Livak & Schmittgen (2001) <doi:10.1006/meth.2001.1262>. Tests for 
  statistical significance using two-group tests and linear regression 
  Yuan et al. (2006) <doi: 10.1186/1471-2105-7-85>.

A collection of functions and data sets that support teaching
  a quantitative finance MS level course on Portfolio Construction and Risk
  Analysis, and the writing of a textbook for such a course.  The package is
  unique in providing several real-world data sets that may be used for problem
  assignments and student projects.  The data sets include cross-sections of
  stock data from the Center for Research on Security Prices, LLC (CRSP),
  corresponding factor exposures data from S&P Global, and several SP500 data
  sets.

Extracts features from amplification curve data of quantitative 
    Polymerase Chain Reactions (qPCR) according to Pabinger et al. 2014 
    <doi:10.1016/j.bdq.2014.08.002> for machine learning purposes. Helper 
    functions prepare the amplification curve data for processing as functional 
    data (e.g., Hausdorff distance) or enable the plotting of amplification 
    curve classes (negative, ambiguous, positive). The hookreg() and hookregNL() 
    functions of Burdukiewicz et al. (2018) <doi:10.1016/j.bdq.2018.08.001> 
    can be used to predict amplification curves with an hook effect-like 
    curvature. The pcrfit_single() function can be used to extract features 
    from an amplification curve.