Abstract:

For survival data with a large number of explanatory variables, lasso penalized Cox regression is a popular regularization strategy. However, a penalized Cox model may not always provide the best fit to data and can be difficult to estimate in high dimension because of its intrinsic nonlinearity. The semiparametric additive hazards model is a flexible alternative which is a natural survival analogue of the standard linear regression model. Building on this analogy, we develop a cyclic coordinate descent algorithm for fitting the lasso and elastic net penalized additive hazards model. The algorithm requires no nonlinear optimization steps and offers excellent performance and stability. An implementation is available in the R package ahaz. We demonstrate this implementation in a small timing study and in an application to real data.

Abstract:

Aligment of mass spectrometry (MS) chromatograms is sometimes required prior to sample comparison and data analysis. Without alignment, direct comparison of chromatograms would lead to inaccurate results. We demonstrate a new method for computing a high quality alignment of full length MS chromatograms using variable penalty dynamic time warping. This method aligns signals using local linear shifts without excessive warping that can alter the shape (and area) of chromatogram peaks. The software is available as the R package VPdtw on the Comprehensive R Archive Network and we highlight how one can use this package here.

Abstract:

Manufacturing processes are often based on more than one quality characteristic. When these variables are correlated the process capability analysis should be performed using multivariate statistical methodologies. Although there is a growing interest in methods for evaluating the capability of multivariate processes, little attention has been given to developing user friendly software for supporting multivariate capability analysis. In this work we introduce the package MPCI for R, which allows to compute multivariate process capability indices. MPCI aims to provide a useful tool for dealing with multivariate capability assessment problems. We illustrate the use of MPCI package through both simulated and real examples.

Abstract:

We consider the problem of nonparametric density estimation where estimates are constrained to be unimodal. Though several methods have been proposed to achieve this end, each of them has its own drawbacks and none of them have readily-available computer codes. The approach of Braun and Hall (2001), where a kernel density estimator is modified by data sharpening, is one of the most promising options, but optimization difficulties make it hard to use in practice. This paper presents a new algorithm and MATLAB code for finding good unimodal density estimates under the Braun and Hall scheme. The algorithm uses a greedy, feasibility-preserving strategy to ensure that it always returns a unimodal solution. Compared to the incumbent method of optimization, the greedy method is easier to use, runs faster, and produces solutions of comparable quality. It can also be extended to the bivariate case.

Abstract:

The R package bclust is useful for clustering high-dimensional continuous data. The package uses a parametric spike-and-slab Bayesian model to downweight the effect of noise variables and to quantify the importance of each variable in agglomerative clustering. We take advantage of the existence of closed-form marginal distributions to estimate the model hyper-parameters using empirical Bayes, thereby yielding a fully automatic method. We discuss computational problems arising in implementation of the procedure and illustrate the usefulness of the package through examples.

mathStatica 2.5, version 2.5
mathStatica

Modern Fortran: Style and Usage
Norman S. Clerman and Walter Spector
Cambridge University Press, New York, NY, 2012
ISBN: 978-0-521-73052-5

Abstract:

Frailty models are very useful for analysing correlated survival data, when observations are clustered into groups or for recurrent events. The aim of this article is to present the new version of an R package called frailtypack. This package allows to fit Cox models and four types of frailty models (shared, nested, joint, additive) that could be useful for several issues within biomedical research. It is well adapted to the analysis of recurrent events such as cancer relapses and/or terminal events (death or lost to follow-up). The approach uses maximum penalized likelihood estimation. Right-censored or left-truncated data are considered. It also allows stratification and time-dependent covariates during analysis.

Abstract:

Classical supervised learning enjoys the luxury of accessing the true known labels for the observations in a modeled dataset. Real life, however, poses an abundance of problems, where the labels are only partially defined, i.e., are uncertain and given only for a subset of observations. Such partial labels can occur regardless of the knowledge source. For example, an experimental assessment of labels may have limited capacity and is prone to measurement errors. Also expert knowledge is often restricted to a specialized area and is thus unlikely to provide trustworthy labels for all observations in the dataset. Partially supervised mixture modeling is able to process such sparse and imprecise input. Here, we present an R package called bgmm, which implements two partially supervised mixture modeling methods: soft-label and belief-based modeling. For completeness, we equipped the package also with the functionality of unsupervised, semi- and fully supervised mixture modeling. On real data we present the usage of bgmm for basic model-fitting in all modeling variants. The package can be applied also to selection of the best-fitting from a set of models with different component numbers or constraints on their structures. This functionality is presented on an artificial dataset, which can be simulated in bgmm from a distribution defined by a given model.

Abstract:

We present GeoXp, an R package implementing interactive graphics for exploratory spatial data analysis. We use a data set concerning public schools of the French Midi- Pyr ́en ́ees region to illustrate the use of these exploratory techniques based on the coupling between a statistical graph and a map. Besides elementary plots like boxplots, histograms or simple scatterplots, GeoXp also couples maps with Moran scatterplots, variogram clouds, Lorenz curves and other graphical tools. In order to make the most of the multidimensionality of the data, GeoXp includes dimension reduction techniques such as principal components analysis and cluster analysis whose results are also linked to the map.

Abstract:

splm is an R package for the estimation and testing of various spatial panel data specifications. We consider the implementation of both maximum likelihood and generalized moments estimators in the context of fixed as well as random effects spatial panel data models. This paper is a general description of splm and all functionalities are illustrated using a well-known example taken from Munnell (1990) with productivity data on 48 US states observed over 17 years. We perform comparisons with other available software; and, when this is not possible, Monte Carlo results support our original implementation.

Abstract:

This article describes the benchden package which implements a set of 28 example densities for nonparametric density estimation in R. In addition to the usual functions that evaluate the density, distribution and quantile functions or generate random variates, a function designed to be specifically useful for larger simulation studies has been added. After describing the set of densities and the usage of the package, a small toy example of a simulation study conducted using the benchden package is given.

Abstract:

In many medical studies, patients can experience several events. The times between consecutive events (gap times) are often of interest and lead to problems that have received much attention recently. In this work we consider the estimation of the bivariate distribution function for censored gap times, using survivalBIV a software application for R. Some related problems such as the estimation of the marginal distribution of the second gap time is also discussed. It describes the capabilities of the program for estimating these quantities using four different approaches, all using the Kaplan-Meier estimator of survival. One of these estimators is based on Bayes’ theorem and Kaplan-Meier survival function. Two estimators were recently proposed using the Kaplan-Meier estimator pertaining to the distribution of the total time to weight the bivariate data (de Un ̃a-A ́lvarez and Meira-Machado 2008 and de Un ̃a-A ́lvarez and Amorim 2011). The software can also be used to implement the estimator proposed in Lin, Sun, and Ying (1999), which is based on inverse probability of censoring weighted. The software is illustrated using data from a bladder cancer study.