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Least-Squares Estimator under k-Monotony Constraint for Discrete
Functions
We implement two least-squares estimators under k-monotony constraint using a method based on the Support Reduction Algorithm from Groeneboom et al (2008) <DOI:10.1111/j.1467-9469.2007.00588.x>. The first one is a projection estimator on the set of k-monotone discrete functions. The second one is a projection on the set of k-monotone discrete probabilities. This package provides functions to generate samples from the spline basis from Lefevre and Loisel (2013) <DOI:10.1239/jap/1378401239>, and from mixtures of splines.

R (>= 2.0.0), foreign, binr, forestplot, rtf

Conduct a noncompartmental analysis as closely as possible to the most widely used commercial software.
Some features are
1) CDISC SDTM terms
2) Automatic slope selection with the same criterion of WinNonlin(R)
3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method
4) Interval(partial) AUCs with 'linear' or 'log' interpolation method
* Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107).

Florian Wickelmaier [cre, aut], Juergen Heller [aut], Pasquale
Anselmi [ctb]
Fitting and testing probabilistic knowledge structures,
especially the basic local independence model (BLIM, Doignon &
Flamagne, 1999), using the minimum discrepancy maximum
likelihood (MDML) method.

Florian Wickelmaier [cre, aut], Juergen Heller [aut],
Pasquale Anselmi [ctb]
Fitting and testing probabilistic knowledge structures,
especially the basic local independence model (BLIM, Doignon & Flamagne,
1999), using the minimum discrepancy maximum likelihood (MDML) method.