* Estimating Local False Discovery Rates Using Empirical Bayes Methods* (

**LFDREmpiricalBayes**)

New empirical Bayes methods aiming at analyzing the association of single nucleotide polymorphisms (SNPs) to some particular disease are implemented in this package. The package uses local false discovery rate (LFDR) estimates of SNPs within a sample population defined as a ‘reference class’ and discovers if SNPs are associated with the corresponding disease. Although SNPs are used throughout this document, other biological data such as protein data and other gene data can be used. Karimnezhad, Ali and Bickel, D. R. (2016) <http://…/34889>.

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**Nonlinear Quantile Regression Coefficients Modeling****qrcmNL**)

Nonlinear parametric modeling of quantile regression coefficient functions. Frumento P and Bottai M (2016) <doi:10.1111/biom.12410>.

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**Null Model Analysis for Ecological Networks****econullnetr**)

Tools for using null models to analyse ecological networks (e.g. food webs, flower-visitation networks, seed-dispersal networks) and detect resource preferences or non-random interactions among network nodes. Tools are provided to run null models, test for and plot preferences, plot and analyse bipartite networks, and export null model results in a form compatible with other network analysis packages. The underlying null model was developed by Agusti et al. (2003) <doi:10.1046/j.1365-294X.2003.02014.x> and the full application to ecological networks by Vaughan et al. (2017) econullnetr: an R package using null models to analyse the structure of ecological networks and identify resource selection. Methods in Ecology & Evolution, in press.

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**The Multiple Filter Test for Change Point Detection****MFT**)

Provides statistical tests and algorithms for the detection of change points in time series and point processes – particularly for changes in the mean in time series and for changes in the rate and in the variance in point processes. References – Michael Messer, Marietta Kirchner, Julia Schiemann, Jochen Roeper, Ralph Neininger and Gaby Schneider (2014) <doi:10.1214/14-AOAS782>, Stefan Albert, Michael Messer, Julia Schiemann, Jochen Roeper, Gaby Schneider (2017) <doi:10.1111/jtsa.12254>, Michael Messer, Kaue M. Costa, Jochen Roeper and Gaby Schneider (2017) <doi:10.1007/s10827-016-0635-3>.

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**A Lightweight Template for Data Analysis Projects****tinyProject**)

Creates useful files and folders for data analysis projects and provides functions to manage data, scripts and output files. Also provides a project template for ‘Rstudio’.

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**OGA+HDIC+Trim and High-Dimensional Linear Regression Models****Ohit**)

Ing and Lai (2011) <doi:10.5705/ss.2010.081> proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. The first two steps, OGA and HDIC, are used to sequentially select input variables and determine stopping rules, respectively. The third step, Trim, is used to delete irrelevant variables remaining in the second step. This package aims at fitting a high-dimensional linear regression model via OGA+HDIC+Trim.