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dlnm: Distributed Lag Non-Linear Models

The package dlnm contains functions to specify and interpret distributed lag linear (DLMs) and non-linear (DLNMs) models. The DLM/DLNM methodology is illustrated in detail in a series of articles referenced at the end of this document.

Info on the dlnm package

The package dlnm is available on the Comprehensive R Archive Network (CRAN), with info at the related web page (https://cran.r-project.org/package=dlnm). A development website is available on GitHub (https://github.com/gasparrini/dlnm).

For a short summary of the functionalities of this package, refer to the main help page by typing:

help(dlnm)

in R after installation (see below). For a more comprehensive overview, refer to the main vignette of the package that can be opened with:

vignette("dlnmOverview")

Installation

The last version officially released on CRAN can be installed directly within R by typing:

install.packages("dlnm")

R code in published articles

Several peer-reviewed articles and documents provide R code illustrating methodological developments of dlnm or replicating substantive results using this package. An updated version of the code can be found at the GitHub (httpsgithub.com/gasparrini) or personal web page (http://www.ag-myresearch.com) of the package maintainer.

References:

Gasparrini A. Distributed lag linear and non-linear models in R: the package dlnm. Journal of Statistical Software. 2011; 43(8):1-20. [freely available here]

Gasparrini A, Scheipl F, Armstrong B, Kenward MG. A penalized framework for distributed lag non-linear models. Biometrics. 2017;73(3):938-948. [freely available here]://

Gasparrini A. Modelling lagged associations in environmental time series data: a simulation study. Epidemiology. 2016; 27(6):835-842. [freely available here]

Gasparrini A. Modeling exposure-lag-response associations with distributed lag non-linear models. Statistics in Medicine. 2014; 33(5):881-899. [freely available here].

Gasparrini A., Armstrong, B.,Kenward M. G. Distributed lag non-linear models. Statistics in Medicine. 2010; 29(21):2224-2234. [freely available here].

Gasparrini A., Armstrong, B., Kenward M. G. Reducing and meta-analyzing estimates from distributed lag non-linear models. BMC Medical Research Methodology. 2013; 13(1):1. [freely available here].

Armstrong, B. Models for the relationship between ambient temperature and daily mortality. Epidemiology. 2006, 17(6):624-31. [available here].

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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