* A Better Alternative to the Hodrick-Prescott Filter* (

**neverhpfilter**)

In the working paper titled ‘Why You Should Never Use the Hodrick-Prescott Filter’, James D. Hamilton proposes an interesting new alternative to economic time series filtering. The neverhpfilter package provides functions for implementing his solution. Hamilton (2017) <doi:10.3386/w23429>.

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**Tests for Same-Source of Toolmarks****toolmaRk**)

Implements two tests for same-source of toolmarks. The chumbley_non_random() test follows the paper ‘An Improved Version of a Tool Mark Comparison Algorithm’ by Hadler and Morris (2017) <doi:10.1111/1556-4029.13640>. This is an extension of the Chumbley score as previously described in ‘Validation of Tool Mark Comparisons Obtained Using a Quantitative, Comparative, Statistical Algorithm’ by Chumbley et al (2010) <doi:10.1111/j.1556-4029.2010.01424.x>. fixed_width_no_modeling() is based on correlation measures in a diamond shaped area of the toolmark as described in Hadler (2017).

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**Survival Analysis of Time Varying Coefficients Using a Tree-Based Approach****TimeVTree**)

Estimates time varying regression effects under Cox type models in survival data using classification and regression tree. The codes in this package were originally written in S-Plus for the paper ‘Survival Analysis with Time-Varying Regression Effects Using a Tree-Based Approach,’ by Xu, R. and Adak, S. (2002) <doi:10.1111/j.0006-341X.2002.00305.x>, Biometrics, 58: 305-315. Development of this package was supported by NIH grants AG053983 and AG057707, and by the UCSD Altman Translational Research Institute, NIH grant UL1TR001442. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The example data are from the Honolulu Heart Program/Honolulu Asia Aging Study (HHP/HAAS).

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**Factor Adjusted Robust Model Selection****FarmSelect**)

Implements a consistent model selection strategy for high dimensional sparse regression when the covariate dependence can be reduced through factor models. By separating the latent factors from idiosyncratic components, the problem is transformed from model selection with highly correlated covariates to that with weakly correlated variables. It is appropriate for cases where we have many variables compared to the number of samples. Moreover, it implements a robust procedure to estimate distribution parameters wherever possible, hence being suitable for cases when the underlying distribution deviates from Gaussianity. See the paper on the ‘FarmSelect’ method, Fan et al.(2017) <arXiv:1612.08490>, for detailed description of methods and further references.

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**Robust and Sparse Methods for High Dimensional Linear and**

Logistic RegressionLogistic Regression

**enetLTS**)

Fully robust versions of the elastic net estimator are introduced for linear and logistic regression, in particular high dimensional data by Kurnaz, Hoffmann and Filzmoser (2017) <DOI:10.1016/j.chemolab.2017.11.017>. The algorithm searches for outlier free subsets on which the classical elastic net estimators can be applied.