Browse Items (12 total)

The dynamichazard package implements state space models that can provide a computationally efficient way to model time-varying parameters in survival analysis. I cover the
models and some of the estimation methods implemented in dynamichazard, apply…

Model selection in mixed models based on the conditional distribution is appropriate
for many practical applications and has been a focus of recent statistical research. In
this paper we introduce the R package cAIC4 that allows for the computation…

We provide a hands-on introduction to optimized textual sentiment indexation using
the R package sentometrics. Textual sentiment analysis is increasingly used to unlock
the potential information value of textual data. The sentometrics package…

Recently, there has been a growing interest in tensor data analysis, where tensor regression is the cornerstone of statistical modeling for tensor data. The R package TRES
provides the standard least squares estimators and the more efficient…

This paper describes the R package cold for the analysis of count longitudinal data. In
this package marginal and random effects models are considered. In both cases estimation
is via maximization of the exact likelihood and serial dependence among…

Dynamic time warping (DTW) is a popular distance measure for time series analysis
and has been applied in many research domains. This paper proposes the R package
IncDTW for the incremental calculation of DTW, and based on this principle…

Regularization techniques such as the lasso (Tibshirani 1996) and elastic net (Zou and
Hastie 2005) can be used to improve regression model coefficient estimation and prediction
accuracy, as well as to perform variable selection. Ordinal regression…

The aim of this paper is to describe the implementation and to provide a tutorial for
the R package ssmrob, which is developed for robust estimation and inference in sample
selection and endogenous treatment models. The sample selectivity issue…

Causal effect identification considers whether an interventional probability distribution
can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating system. While complete…

Statistical analyses of directional or angular data have applications in a variety of
fields, such as geology, meteorology and bioinformatics. There is substantial literature on
descriptive and inferential techniques for univariate angular data,…