How to cite the R package mcp

mcp is a popular R package that is available at https://cran.r-project.org/web/packages/mcp/index.html. By citing R packages in your paper you lay the grounds for others to be able to reproduce your analysis and secondly you are acknowledging the time and work people have spent creating the package.

APA citation

Formatted according to the APA Publication Manual 7^th edition. Simply copy it to the References page as is.

APA

The minimal requirement is to cite the R package in text along with the version number. Additionally, you can include the reference list entry the authors of the mcp package have suggested.

Example of an in-text citation

Analysis of the data was done using the mcp package (v0.3.0; Lindeløv, 2020).

Reference list entry

Lindeløv, J. K. (2020). Mcp: An R package for regression with multiple change points. https://doi.org/10.31219/osf.io/fzqxv

Vancouver citation

Formatted according to Vancouver style. Simply copy it to the references section as is.

Vancouver

Example of an in-text citation

Analysis of the data was done using the mcp package v0.3.0 (1).

Reference list entry

1.
Lindeløv JK. Mcp: An R package for regression with multiple change points [Internet]. 2020. Available from: http://dx.doi.org/10.31219/osf.io/fzqxv

BibTeX

Reference entry in BibTeX format. Simply copy it to your favorite citation manager.

BibTeX

@UNPUBLISHED{Lindelov2020-fg,
  title    = "Mcp: An {R} package for regression with multiple change points",
  author   = "Lindel{\o}v, Jonas Kristoffer",
  abstract = "The R package mcp does flexible and informed Bayesian regression
              with change points. mcp can infer the location of changes between
              regression models on means, variances, autocorrelation structure,
              and any combination of these. Prior and posterior samples and
              summaries are returned for all parameters and a rich set of
              plotting options is available. Bayes Factors can be computed via
              Savage-Dickey density ratio and posterior contrasts.
              Cross-validation can be used for more general model comparison.
              mcp ships with sensible defaults, including priors, but the user
              can override them to get finer control of the models and outputs.
              The strengths and limitations of mcp are discussed in relation to
              existing change point packages in R.",
  month    =  jan,
  year     =  2020,
  url      = "http://dx.doi.org/10.31219/osf.io/fzqxv",
  doi      = "10.31219/osf.io/fzqxv"
}

RIS

Reference entry in RIS format. Simply copy it to your favorite citation manager.

RIS

TY  - INPR
AU  - Lindeløv, Jonas Kristoffer
TI  - Mcp: An R package for regression with multiple change points
PY  - 2020
DA  - 2020/1/5
AB  - The R package mcp does flexible and informed Bayesian regression with
      change points. mcp can infer the location of changes between regression
      models on means, variances, autocorrelation structure, and any combination
      of these. Prior and posterior samples and summaries are returned for all
      parameters and a rich set of plotting options is available. Bayes Factors
      can be computed via Savage-Dickey density ratio and posterior contrasts.
      Cross-validation can be used for more general model comparison. mcp ships
      with sensible defaults, including priors, but the user can override them
      to get finer control of the models and outputs. The strengths and
      limitations of mcp are discussed in relation to existing change point
      packages in R.
DO  - 10.31219/osf.io/fzqxv
UR  - http://dx.doi.org/10.31219/osf.io/fzqxv
ER  -

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mcp R package release history

Version	Release date
0.2.0	2020-01-09