Constrained quantile regression is performed. One constraint is that all beta coefficients (including the constant) cannot be negative, they can be either 0 or strictly positive. Another constraint is that the beta coefficients lie within an interval. References: Koenker R. (2005) Quantile Regression, Cambridge University Press. <doi:10.1017/CBO9780511754098>.

CODATA internationally recommended values of the fundamental
    physical constants, provided as symbols for direct use within the R language.
    Optionally, the values with uncertainties and/or units are also provided if
    the 'errors', 'units' and/or 'quantities' packages are installed.
    The Committee on Data for Science and Technology (CODATA) is an
    interdisciplinary committee of the International Council for Science which
    periodically provides the internationally accepted set of values of the
    fundamental physical constants. This package contains the "2022 CODATA"
    version, published on May 2024:
    Eite Tiesinga, Peter J. Mohr, David B. Newell, and Barry N. Taylor (2024)
    <https://physics.nist.gov/cuu/Constants/>.

Examine any number of time series data frames to identify 
    instances in which various criteria are met within specified time
    frames. In clinical medicine, these types of events are often
    called "constellations of signs and symptoms", because a single 
    condition depends on a series of events occurring within a certain 
    amount of time of each other. This package was written to work with
    any number of time series data frames and is optimized for speed 
    to work well with data frames with millions of rows.

Provides functions for efficient computation of non-linear spatial predictions with local change of support (Hofer, C. and Papritz, A. (2011) "constrainedKriging: An R-package for customary, constrained and covariance-matching constrained point or block kriging" <doi:10.1016/j.cageo.2011.02.009>).  This package supplies functions for two-dimensional spatial interpolation by constrained (Cressie, N. (1993) "Aggregation in geostatistical problems" <doi:10.1007/978-94-011-1739-5_3>), covariance-matching constrained (Aldworth, J. and Cressie, N. (2003) "Prediction of nonlinear spatial functionals" <doi:10.1016/S0378-3758(02)00321-X>) and universal (external drift) Kriging for points or blocks of any shape from data with a non-stationary mean function and an isotropic weakly stationary covariance function.  The linear spatial interpolation methods, constrained and covariance-matching constrained Kriging, provide approximately unbiased prediction for non-linear target values under change of support.  This package extends the range of tools for spatial predictions available in R and provides an alternative to conditional simulation for non-linear spatial prediction problems with local change of support.