* Recursive Partitioning of Longitudinal Data* (

**longRPart2**)

Performs recursive partitioning of linear and nonlinear mixed effects models, specifically for longitudinal data. The package is an extension of the original ‘longRPart’ package by Stewart and Abdolell (2013) <https://…/package=longRPart>.

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**Heteroskedastic Gaussian Process Modeling and Design under Replication****hetGP**)

Performs Gaussian process regression with heteroskedastic noise following Binois, M., Gramacy, R., Ludkovski, M. (2016) <arXiv:1611.05902>. The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.

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**Kernel Adaptive Density Estimation and Regression****kader**)

Implementation of various kernel adaptive methods in nonparametric curve estimation like density estimation as introduced in Stute and Srihera (2011) <doi:10.1016/j.spl.2011.01.013> and Eichner and Stute (2013) <doi:10.1016/j.jspi.2012.03.011> for pointwise estimation, and like regression as described in Eichner and Stute (2012) <doi:10.1080/10485252.2012.760737>.

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**Convert Tables to PDF****latexpdf**)

Converts table-like objects to stand-alone PDF. Can be used to embed tables and arbitrary content in PDF documents. Provides a low-level R interface for creating ‘LaTeX’ code, e.g. command() and a high-level interface for creating PDF documents, e.g. as.pdf.data.frame(). Extensive customization is available via mid-level functions, e.g. as.tabular(). See also ‘package?latexpdf’. Adapted from ‘metrumrg’ <http://…/?group_id=1215>. Requires a compatible installation of ‘pdflatex’, e.g. <https://…/>.

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**Inference of Parameters of Normal Distributions from a Mixture of Normals****DPP**)

This MCMC method takes a data numeric vector (Y) and assigns the elements of Y to a (potentially infinite) number of normal distributions. The individual normal distributions from a mixture of normals can be inferred. Following the method described in Escobar (1994) <doi:10.2307/2291223> we use a Dirichlet Process Prior (DPP) to describe stochastically our prior assumptions about the dimensionality of the data.

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**Filter Methods for Parameter Estimation in Linear Regression Models****LMfilteR**)

We present a method based on filtering algorithms to estimate the parameters of linear regressions, i.e. the coefficients and the variance of the error term. The proposed algorithm makes use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods.