Software:

• Web applet DoseEstimateH2AX: Radiation dose estimation from the gamma-H2AX assay [14]
• R package LPCM : Local principal curve methods [1,2,3]; with L. Evers.
• Records of changes and updates.
• A C++ version of this package ('LACE'; Back et al, 2014) has been produced by the Elementary Particle Physics Group, University of Warwick, and is available on github; see also Documentation and [11].
• R package npmlreg: Nonparametric maximum likelihood estimation for random effect models [5,6]; with R. Darnell and J. Hinde.
• Records of changes and updates .
• R package UEM: Estimating and updating Gaussian and Poisson mixtures via the EM algorithm [12,13] .tar.gz version ; ZIP version; Manual; with D. Bonetti and N. Qarmalah
(under construction; updated 01/07/2020)
• Function UEM : Implementation of the (update) EM algorithm for Gaussian and Poisson mixtures [12] (Type ?EM or ?UEM for examples)
• Function kboxplot: Implementation of ``k-Boxplots for Mixture Data" [13] (Type ?kboxplot for examples).
• R package boxcoxmix: Response Transformations for Random Effect and Variance Component Models (first author: A. Almohaimeed)
  • On CRAN since 29/07/2017
  • Vignette

S-Plus/R Source Code for individual functions:

• Function modalreg (Multivalued non-parametric regression [7]) in R package hdrcde. Package maintained by R. Hyndman.
• Function wrm.smooth (Weighted Repeated Median Smoothing [8]) in R package robfilter. Package maintained by R. Fried.
• (Outdated) R source code for Local Principal Curves [1,2]
• Online-Monitoring [9]

Data sets

• Pennsylvania Floodplain Data (Source: Office of Remote Sensing for Earth Resources, Pennsylvania State University).
• European Coastal resorts (Source: Diercke Weltatlas, 1984).
• Speed-flow data for [10].

References

[1] Local Principal Curves (Einbeck, Tutz & Evers, 2005). Statistics and Computing 15, 301-313.
[2] Exploring Multivariate Data Structures with Local Principal Curves (Einbeck, Tutz & Evers, 2005). Proceedings of the GfKl 2004.
[3] Data compression and regression based on local principal curves (Einbeck, Evers & Hinchliff, 2010). Proceedings of the GfKl 2008.
[4] Data compression and regression through local principal curves and surfaces (Einbeck, Evers, & Powell, 2010). International Journal of Neural Systems 20, 177–192.
[5] A General Maximum Likelihood Analysis of Overdispersion in Generalized Linear Models (Aitkin, 1996). Statistics and Computing 6 , 251-262.
[6] A note on NPML estimation for exponential family regression models with unspecified dispersion parameter (Einbeck & Hinde, 2006). Austrian Journal of Statistics 35 , 233-243, 2006.
[7] Modelling beyond Regression Functions: an Application of Multimodal Regression to Speed-Flow Data (Einbeck & Tutz, 2006). Journal of the Royal Statistical Society, Series C (Applied Statistics) 55, 461-475.
[8] Weighted Repeated Median Smoothing and Filtering (Fried, Einbeck & Gather, 2007). JASA , 102, 1300--1308.
[9] Online Monitoring with Local Smoothing Methods and Adaptive Ridging (Einbeck & Kauermann, 2003). Journal of Statistical Computation and Simulation 73, 913-929.
[10] Using principal curves to analyze traffic patterns on freeways (Einbeck & Dwyer, 2011). Transportmetrica, 7, 229-246.
[11] Implementation of a local principal curves alghorithm for neutrino interaction reconstruction in a liquid argon volume (Back et al, 2014). European Physical Journal C 74 pp. 2832
[12] A study of online and blockwise updating of the EM algorithm for Gaussian mixtures (Einbeck and Bonetti, 2014): In: T. Kneib et al (Eds.). Proceedings of the 29th International Workshop on Statistical Modelling. Göttingen, Germany, 14-18 July 2014, pages 35-38.
[13] k-Boxplots for Mixture Data (Qarmalah, Einbeck, and Coolen, 2016): Statistical Papers, doi 10.1007/s00362-016-0774-7.
[14] A statistical framework for radiation dose estimation with uncertainty quantification from the γ-H2AX assay (Einbeck et al, 2018). PLOS ONE 13(11): e0207464.