papaja is an R package in the making including an R
Markdown template that can be used with
(or without) RStudio to produce documents
which conform to the American Psychological Association (APA) manuscript
guidelines (6th Edition). The package uses the LaTeX document class
apa6 and a .docx-reference file, so you
can create PDF documents, or Word documents if you have to. Moreover,
papaja supplies R-functions that facilitate reporting results of your
analyses in accordance with APA guidelines.
If you are looking for an in-depth introduction to papaja, check out
the current draft of the manual.
papaja is in active development and should be considered alpha. If
you experience any problems, ask a question on Stack Overflow using the
papaja tag or open an
issue on Github.
Take a look at the R
Markdown-file
of the example manuscript in the folder example and the resulting
PDF.
The example document also contains some basic instructions. For an
in-depth introduction to papaja, check out the current draft of the
manual.
To use papaja you need either an up-to-date version of
RStudio or
pandoc. If you want to create PDF-
in addition to DOCX-documents you additionally need a
TeX distribution. If you have no use
for TeX beyond rendering R Markdown documents, I recommend you use
TinyTex. TinyTex can be installed from
within R as
follows.
if(!"tinytex" %in% rownames(installed.packages())) install.packages("tinytex")
tinytex::install_tinytex()Otherwise consider MikTeX for Windows,
MacTeX for Mac, or TeX
Live for Linux. Please refer to the
papaja
manual
for detailed installation instructions.
papaja is not yet available on CRAN but you can install it from this
repository:
# Install devtools package if necessary
if(!"devtools" %in% rownames(installed.packages())) install.packages("devtools")
# Install the stable development verions from GitHub
devtools::install_github("crsh/papaja")
# Install the latest development snapshot from GitHub
devtools::install_github("crsh/papaja@devel")Once papaja is installed, you can select the APA template when
creating a new Markdown file through the RStudio menus.
If you want to add citations specify your BibTeX-file in the YAML front
matter of the document (bibliography: my.bib) and you can start
citing. If necessary, have a look at R Markdown’s overview of the
citation
syntax.
You may also be interested in citr, an R
Studio addin to swiftly insert Markdown citations.
The functions apa_print() and apa_table() facilitate reporting results of your analyses. Take a look at the R Markdown-file of the example manuscript in the folder example and the resulting PDF.
Drop a supported analysis result, such as an htest- or lm-object,
into apa_print() and receive a list of possible character strings that
you can use to report the results of your
analysis.
my_lm <- lm(Sepal.Width ~ Sepal.Length + Petal.Width + Petal.Length, data = iris)
apa_lm <- apa_print(my_lm)One element of this list is apa_lm$table that, in the case of an
lm-object, will contain a complete regression table. Pass
apa_lm$table to apa_table() to turn it into a proper table in your
PDF or Word document.
apa_table(apa_lm$table, caption = "Iris regression table.")Table. Iris regression table.
| Predictor | b | 95% CI | t(146) | p |
|---|---|---|---|---|
| Intercept | 1.04 | [0.51, 1.58] | 3.85 | < .001 |
| Sepal Length | 0.61 | [0.48, 0.73] | 9.77 | < .001 |
| Petal Width | 0.56 | [0.32, 0.80] | 4.55 | < .001 |
| Petal Length | -0.59 | [-0.71, -0.46] | -9.43 | < .001 |
papaja currently provides methods for the following object
classes:
| A-B | B-L | L-S | S-Z |
|---|---|---|---|
| afex_aov | BFBayesFactorTop* | lm | summary.glht* |
| anova | default | lsmobj* | summary.glm |
| Anova.mlm | emmGrid* | manova | summary.lm |
| aov | glht* | summary_emm* | summary.ref.grid* |
| aovlist | glm | summary.Anova.mlm | |
| BFBayesFactor* | htest | summary.aov | |
| BFBayesFactorList* | list | summary.aovlist |
* Not fully tested, don’t trust blindly!
Be sure to also check out apa_barplot(), apa_lineplot(), and
apa_beeplot() (or the general function apa_factorial_plot()) if you
work with factorial designs:
apa_factorial_plot(
data = npk
, id = "block"
, dv = "yield"
, factors = c("N", "P", "K")
, ylim = c(0, 80)
, level = .34
, las = 1
, ylab = "Yield"
, plot = c("swarms", "lines", "error_bars", "points")
)
If you prefer creating your plots with ggplot2 try theme_apa().
Don’t use RStudio? No problem. Use the rmarkdown::render function to
create articles:
# Create new R Markdown file
rmarkdown::draft(
"mymanuscript.Rmd"
, "apa6"
, package = "papaja"
, create_dir = FALSE
, edit = FALSE
)
# Render manuscript
rmarkdown::render("mymanuscript.Rmd")
Seth Gree has kindly prepared a minimal papaja example
capsule. If you want to use
papaja in your next CodeOcean project you can use this capsule as a
starting point.
For an in-depth introduction to papaja, check out the current draft of
the manual. If you have questions
related to the use of papaja that are not answered in the manual,
StackOverflow has a
papaja-tag and is
a great place to get answers. If you think you have found a bug, please
open issues and provide a
minimal complete verifiable
example.
Like papaja and want to contribute? Take a look at the open
issues if you need inspiration.
Other than that, there are many output objects from analysis methods
that we would like apa_print() to support. Any new S3/S4-methods for
this function are always appreciated (e.g., factanal, fa, lavaan,
lmer, or glmer).
Please cite papaja if you use it (citation('papaja') will provide
the reference). Below are some peer-reviewed publications that used
papaja. If you have published a paper that was written with papaja,
you can add the reference to the public Zotero
group yourself or send it
to me.
Aust, F., & Edwards, J. D. (2016). Incremental validity of Useful Field of View subtests for the prediction of instrumental activities of daily living. Journal of Clinical and Experimental Neuropsychology, 38(5), 497–515. https://doi.org/10.1080/13803395.2015.1125453
Aust, F., Haaf, J. M., & Stahl, C. (2018). A memory-based judgment account of expectancy-liking dissociations in evaluative conditioning. Journal of Experimental Psychology: Learning, Memory, and Cognition. https://doi.org/10/gdxv8n (R Markdown and data files: https://osf.io/vnmby/)
Aust, F., & Stahl, C. (2019). The enhancing effect of caffeine on mnemonic discrimination is at best small. PsyArXiv. https://doi.org/10/gf6jwz (R Markdown and data files: https://osf.io/p7f4m/)
Barrett, T. S., Borrie, S. A., & Yoho, S. E. (2019). Automating with Autoscore: Introducing an R package for automating the scoring of orthographic transcripts. PsyArXiv. https://doi.org/10/gf4cqp (R Markdown and data files: https://osf.io/htqvr/)
Barth, M., Stahl, C., & Haider, H. (2018). Assumptions of the process-dissociation procedure are violated in implicit sequence learning. Journal of Experimental Psychology: Learning, Memory, and Cognition. https://doi.org/10/gdxv8m (R Markdown and data files: https://github.com/methexp/pdl2)
Bartlett, J. E. (2020). No Difference in Trait-Level Attentional Bias Between Daily and Non-Daily Smokers. PsyArXiv. https://doi.org/10/gg2c8f (R Markdown and data files: osf.io/am9hd/)
Beaton, D., Sunderland, K. M., Levine, B., Mandzia, J., Masellis, M., Swartz, R. H., … Strother, S. C. (2018). Generalization of the minimum covariance determinant algorithm for categorical and mixed data types. bioRxiv. https://doi.org/10.1101/333005
Bergmann, C., Tsuji, S., Piccinini, P. E., Lewis, M. L., Braginsky, M., Frank, M. C., & Cristia, A. (2018). Promoting Replicability in Developmental Research Through Meta-analyses: Insights From Language Acquisition Research. Child Development. https://doi.org/10.1111/cdev.13079 (R Markdown and data files: https://osf.io/uhv3d/)
Bol, N., Dienlin, T., Kruikemeier, S., Sax, M., Boerman, S. C., Strycharz, J., … de Vreese, C. H. (2018). Understanding the Effects of Personalization as a Privacy Calculus: Analyzing Self-Disclosure Across Health, News, and Commerce Contexts†. Journal of Computer-Mediated Communication, 23(6), 370–388. https://doi.org/10/gftcm6
Buchanan, E., Foreman, R., Johnson, B., Pavlacic, J., Swadley, R., & Schulenberg, S. (2018). Does the Delivery Matter? Examining Randomization at the Item Level. PsyArXiv. https://doi.org/10.17605/osf.io/p93df (R Markdown and data files: https://osf.io/gvx7s/)
Buchanan, E., Johnson, B., Miller, A., Stockburger, D., & Beauchamp, M. (2018a). Perceived Grading and Student Evaluation of Instruction. PsyArXiv. https://doi.org/10.17605/osf.io/7x4uf (R Markdown and data files: https://osf.io/jdpfs/)
Buchanan, E. M., & Scofield, J. E. (2018). Methods to detect low quality data and its implication for psychological research. Behavior Research Methods. https://doi.org/10.3758/s13428-018-1035-6 (R Markdown and data files: https://osf.io/x6t8a/)
Buchanan, E., & Scofield, J. (2018). Bulletproof Bias? Considering the Type of Data in Common Proportion of Variance Effect Sizes. PsyArXiv. https://doi.org/10.17605/osf.io/cs4vy (R Markdown and data files: https://osf.io/urd8q/)
Buchanan, E., Scofield, J., & Nunley, N. (2018b). The N400’s 3 As: Association, Automaticity, Attenuation (and Some Semantics Too). PsyArXiv. https://doi.org/10.17605/osf.io/6w2se (R Markdown and data files: https://osf.io/h5sd6/)
Buchanan, E., & Valentine, K. (2018). An Extension of the QWERTY Effect: Not Just the Right Hand, Expertise and Typability Predict Valence Ratings of Words. PsyArXiv. https://doi.org/10.31219/osf.io/k7dx5 (R Markdown and data files: https://osf.io/zs2qj/)
Buchanan, E., Valentine, K., & Maxwell, N. (2018c). English Semantic Feature Production Norms: An Extended Database of 4,436 Concepts. PsyArXiv. https://doi.org/10.17605/osf.io/gxbf4 (R Markdown and data files: https://osf.io/cjyzw/)
Buchanan, E., Valentine, K., & Maxwell, N. (2018d). The LAB: Linguistic Annotated Bibliography. PsyArXiv. https://doi.org/10.17605/osf.io/h3bwx (R Markdown and data files: https://osf.io/9bcws/)
Chen, S.-C., de Koning, B., & Zwaan, R. A. (2019). Does Object Size Matter with Regard to the Mental Simulation of Object Orientation? Experimental Psychology. https://doi.org/10/ggfzxw
Conigrave, J. H., Lee, K. K., Zheng, C., Wilson, S., Perry, J., Chikritzhs, T., … others. (2020). Drinking risk varies within and between Australian Aboriginal and Torres Strait Islander samples: A meta-analysis to identify sources of heterogeneity. Addiction. https://doi.org/10/ggsk3n
Craddock, M., Klepousniotou, E., El-Deredy, W., Poliakoff, E., & Lloyd, D. M. (2018). Transcranial alternating current stimulation at 10 Hz modulates response bias in the Somatic Signal Detection Task. bioRxiv. https://doi.org/10.1101/330134
Derringer, J. (2018). A simple correction for non-independent tests. PsyArXiv. https://doi.org/10/gdrbxc (R Markdown and data files: https://osf.io/re5w2/)
Faulkenberry, T. J., Cruise, A., & Shaki, S. (2018). Task instructions modulate unit–decade binding in two-digit number representation. Psychological Research. https://doi.org/10/gdxv8k (R Markdown and data files: https://github.com/tomfaulkenberry/twodigittaskmanip)
Field, A. P., Lester, K. J., Cartwright-Hatton, S., Harold, G. T., Shaw, D. S., Natsuaki, M. N., … Leve, L. D. (2020). Maternal and paternal influences on childhood anxiety symptoms: A genetically sensitive comparison. Journal of Applied Developmental Psychology, 68, 101123. https://doi.org/10/ggq38c (R Markdown and data files: https://osf.io/zgcg2/)
Flygare, O., Andersson, E., Ringberg, H., Hellstadius, A.-C., Edbacken, J., Enander, J., … Rück, C. (2018). Adapted cognitive behavior therapy for obsessive compulsive disorder with co-occuring autism spectrum disorder: A clinical effectiveness study. PsyArXiv. https://doi.org/10/gffjrb (R Markdown and data files: https://osf.io/gj87z/)
Garrison, H., Baudet, G., Breitfeld, E., Aberman, A., & Bergelson, E. (2020). Familiarity plays a small role in noun comprehension at 12-18 months. Infancy. https://doi.org/10/ggsnm2 (R Markdown and data files: https://osf.io/pb2g6/)
Haaf, J. M., Klaassen, F., & Rouder, J. (2018). A Note on Using Systems of Orders to Capture Theoretical Constraint in Psychological Science. PsyArXiv. https://doi.org/10/gffjrf (R Markdown and data files: https://github.com/perceptionandcognitionlab/bf-order)
Haaf, J. M., & Rouder, J. (2018). Some do and some don’t? Accounting for variability of individual difference structures. PsyArXiv. https://doi.org/10.31234/osf.io/zwjtp (R Markdown and data files: https://github.com/perceptionandcognitionlab/ctx-mixture)
Haaf, J. M., & Rouder, J. N. (2017). Developing constraint in bayesian mixed models. Psychological Methods, 22(4), 779–798. https://doi.org/10.1037/met0000156 (R Markdown and data files: https://github.com/perceptionandcognitionlab/ctx-indiff)
Hardwicke, T. E., Mathur, M. B., MacDonald, K., Nilsonne, G., Banks, G. C., Kidwell, M. C., … Frank, M. C. (2018). Data availability, reusability, and analytic reproducibility: Evaluating the impact of a mandatory open data policy at the journal cognition. Royal Society Open Science, 5(8), 180448. https://doi.org/10/gdz63s (R Markdown and data files: https://osf.io/wn8fd/)
Hardwicke, T., & Ioannidis. (2018). Mapping the Universe of Registered Reports. PsyArXiv. https://doi.org/10.31222/osf.io/fzpcy (R Markdown and data files: https://osf.io/7dpwb/)
Harms, C., & Lakens, D. (2018). Making ’Null Effects’ Informative: Statistical Techniques and Inferential Frameworks. PsyArXiv. https://doi.org/10.17605/osf.io/48zca (R Markdown and data files: https://osf.io/wptju/)
Heino, M. T. J., Vuorre, M., & Hankonen, N. (2018). Bayesian evaluation of behavior change interventions: A brief introduction and a practical example. Health Psychology and Behavioral Medicine, 6(1), 49–78. https://doi.org/10.1080/21642850.2018.1428102 (R Markdown and data files: https://github.com/heinonmatti/baseline-visu)
Henderson, E. L., Vall’ee-Tourangeau, F., & Simons, D. J. (2019). The Effect of Concrete Wording on Truth Judgements: A Preregistered Replication and Extension of Hansen & Wänke (2010). Collabra: Psychology, 5. https://doi.org/10/gf9h3x
Heycke, T. (2018, July). Contingency Awareness in Evaluative Conditioning: Investigations Using Subliminal Stimulus Presentations (text.thesis.doctoral). Universität zu Köln. Retrieved from http://www.uni-koeln.de/
Heycke, T., Aust, F., & Stahl, C. (2017). Subliminal influence on preferences? A test of evaluative conditioning for brief visual conditioned stimuli using auditory unconditioned stimuli. Royal Society Open Science, 4(9), 160935. https://doi.org/10.1098/rsos.160935
Heycke, T., Gehrmann, S., Haaf, J. M., & Stahl, C. (2018). Of two minds or one? A registered replication of Rydell et al. (2006). Cognition and Emotion, 32(8), 1708–1727. https://doi.org/10.1080/02699931.2018.1429389
Heycke, T., & Spitzer, L. (2019). Screen Recordings as a Tool to Document Computer Assisted Data Collection Procedures. Psychologica Belgica, 59(1), 269–280. https://doi.org/10/gf5t5c
Heycke, T., & Stahl, C. (2018). No evaluative conditioning effects with briefly presented stimuli. Psychological Research. https://doi.org/10.1007/s00426-018-1109-1
Heyman, T., & Heyman, G. (2018). Can prediction-based distributional semantic models predict typicality? PsyArXiv. https://doi.org/10.17605/osf.io/59xtd (R Markdown and data files: https://osf.io/nkfjy/)
Jordan, K., Buchanan, E., & Padfield, W. (2018). Focus on the Target: The Role of Attentional Focus in Decisions about War. PsyArXiv. https://doi.org/10.17605/osf.io/9fgu8 (R Markdown and data files: https://osf.io/r8qp2/)
Kothe, E. J., & Ling, M. (2019). Retention of participants recruited to a one-year longitudinal study via Prolific. PsyArXiv. https://doi.org/10.31234/osf.io/5yv2u (R Markdown and data files: https://osf.io/yjstk/)
Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence Testing for Psychological Research: A Tutorial. Advances in Methods and Practices in Psychological Science, 1(2), 259–269. https://doi.org/10/gdj7s9 (R Markdown and data files: https://osf.io/qamc6/)
Lewis, M., Braginsky, M., Tsuji, S., Bergmann, C., Piccinini, P. E., Cristia, A., & Frank, M. C. (2017). A Quantitative Synthesis of Early Language Acquisition Using Meta-Analysis. PsyArXiv. https://doi.org/10.31234/osf.io/htsjm
Maxwell, N., & Buchanan, E. (2018a). Investigating the Interaction between Associative, Semantic, and Thematic Database Norms for Memory Judgments and Retrieval. PsyArXiv. https://doi.org/10.17605/osf.io/fcesn (R Markdown and data files: https://osf.io/y8h7v/)
Maxwell, N., & Buchanan, E. (2018b). Modeling Memory: Exploring the Relationship Between Word Overlap and Single Word Norms when Predicting Relatedness Judgments and Retrieval. PsyArXiv. https://doi.org/10.17605/osf.io/qekad (R Markdown and data files: https://osf.io/j7qtc/)
McHugh, C., McGann, M., Igou, E. R., & Kinsella, E. L. (2017). Searching for Moral Dumbfounding: Identifying Measurable Indicators of Moral Dumbfounding. Collabra: Psychology, 3(1). https://doi.org/10.1525/collabra.79 (R Markdown and data files: https://osf.io/wm6vc/)
McHugh, C., McGann, M., Igou, E. R., & Kinsella, E. L. (2020). Reasons or rationalizations: The role of principles in the moral dumbfounding paradigm. Journal of Behavioral Decision Making, bdm.2167. https://doi.org/10/ggf94x
Moors, P., & Hesselmann, G. (2019). Unconscious arithmetic: Assessing the robustness of the results reported by Karpinski, Briggs, and Yale (2018). Consciousness and Cognition, 68, 97–106. https://doi.org/10/gftmrj
Morin-Lessard, E., Poulin-Dubois, D., Segalowitz, N., & Byers-Heinlein, K. (2019). Selective attention to the mouth of talking faces in monolinguals and bilinguals aged 5 months to 5 years. PsyArXiv. https://doi.org/10.31234/osf.io/5pkne (R Markdown and data files: https://osf.io/ikvyr/)
Nalborczyk, L., Batailler, C., Lœvenbruck, H., Vilain, A., & Bürkner, P.-C. (2019). An Introduction to Bayesian Multilevel Models Using brms: A Case Study of Gender Effects on Vowel Variability in Standard Indonesian. Journal of Speech, Language, and Hearing Research, 62(5), 1225–1242. https://doi.org/10.1044/2018_JSLHR-S-18-0006 (R Markdown and data files: https://osf.io/dpzcb/)
Papenberg, M., Willing, S., & Musch, J. (2017). Sequentially presented response options prevent the use of testwiseness cues in multiple-choice testing. Psychological Test and Assessment Modeling, 59(2), 245–266. Retrieved from http://www.psychologie-aktuell.com/fileadmin/download/ptam/2-2017_20170627/06_Papenberg_.pdf
Pavlacic, J., Buchanan, E., Maxwell, N., Hopke, T., & Schulenberg, S. (2018). A Meta-Analysis of Expressive Writing on Positive Psychology Variables and Traumatic Stress. PsyArXiv. https://doi.org/10.17605/osf.io/u98cw (R Markdown and data files: https://osf.io/4mjqt/)
Peterka-Bonetta, J., Sindermann, C., Sha, P., Zhou, M., & Montag, C. (2019). The relationship between Internet Use Disorder, depression and burnout among Chinese and German college students. Addictive Behaviors, 89, 188–199. https://doi.org/10/gd4rcw
Pollet, T. V., & Saxton, T. (2018). How diverse are the samples used in the journals “Evolution & Human Behavior” and “Evolutionary Psychology”? PsyArXiv. https://doi.org/10.17605/osf.io/7h24p
Robison, M. K., & Unsworth, N. (2018). Pupillometry tracks fluctuations in working memory performance. PsyArXiv. https://doi.org/10/gdz63r (R Markdown and data files: osf.io/vuw9h/)
Rouder, J., & Haaf, J. M. (2018). A Psychometrics of Individual Differences in Experimental Tasks. PsyArXiv. https://doi.org/10/gfdbw2 (R Markdown and data files: https://github.com/perceptionandcognitionlab/ctx-reliability)
Rouder, J., & Haaf, J. M. (2019). Optional Stopping and the Interpretation of The Bayes Factor. PsyArXiv. https://doi.org/10.31234/osf.io/m6dhw (R Markdown and data files: https://osf.io/uv456/)
Rouder, J., Haaf, J. M., & Snyder, H. K. (2018a). Minimizing Mistakes In Psychological Science. PsyArXiv. https://doi.org/10/gfdb27 (R Markdown and data files: https://github.com/perceptionandcognitionlab/lab-transparent)
Rouder, J., Haaf, J. M., Stober, C., & Hilgard, J. (2017). Beyond Overall Effects: A Bayesian Approach to Finding Constraints Across A Collection Of Studies In Meta-Analysis. PsyArXiv. https://doi.org/10/gffjrd (R Markdown and data files: https://github.com/perceptionandcognitionlab/meta-planned)
Rouder, J. N., Haaf, J. M., & Aust, F. (2018b). From theories to models to predictions: A Bayesian model comparison approach. Communication Monographs, 85(1), 41–56. https://doi.org/10.1080/03637751.2017.1394581
Samaey, C., Wagemans, J., & Moors, P. (2020). Individual differences in processing orientation and proximity as emergent features. Vision Research, 169, 12–24. https://doi.org/10/ggnc6w (R Markdown and data files: https://osf.io/vgxja/)
Sauer, S. (2017). Observation oriented modeling revised from a statistical point of view. Behavior Research Methods. https://doi.org/10.3758/s13428-017-0949-8 (R Markdown and data files: https://osf.io/6vhja/)
Stahl, C., Barth, M., & Haider, H. (2015). Distorted estimates of implicit and explicit learning in applications of the process-dissociation procedure to the SRT task. Consciousness and Cognition, 37, 27–43. https://doi.org/10.1016/j.concog.2015.08.003
Stahl, C., & Corneille, O. (2020). Evaluative conditioning in the Surveillance paradigm is moderated by awareness exclusion criteria. PsyArXiv. https://doi.org/10.31234/osf.io/3xsbu (R Markdown and data files: https://osf.io/qs35v)
Stahl, C., Haaf, J., & Corneille, O. (2016a). Subliminal Evaluative Conditioning? Above-Chance CS Identification May Be Necessary and Insufficient for Attitude Learning. Journal of Experimental Psychology: General, 145, 1107–1131. https://doi.org/10.1037/xge0000191
Stahl, C., Henze, L., & Aust, F. (2016b). False memory for perceptually similar but conceptually distinct line drawings. PsyArXiv. https://doi.org/10.17605/osf.io/zr7m8 (R Markdown and data files: https://osf.io/jxm7z/)
Stahl, C., & Heycke, T. (2016). Evaluative Conditioning with Simultaneous and Sequential Pairings Under Incidental and Intentional Learning Conditions. Social Cognition, 34(5), 382–412. https://doi.org/10.1521/soco.2016.34.5.382
Stevens, J. R., & Soh, L.-K. (2018). Predicting similarity judgments in intertemporal choice with machine learning. Psychonomic Bulletin & Review, 25(2), 627–635. https://doi.org/10/gdfghk
Urry, H. L., Sifre, E., Song, J., Steinberg, H., Bornstein, M., Kim, J., … Andrews, M. (2018). Effect of Disgust on Judgments of Moral Wrongness: A Replication of Eskine, Kacinik, and Prinz (2011). *At Tufts University
- Spring, 2017*. Retrieved from https://osf.io/fu384/ (R Markdown and data files: https://osf.io/ddmkm)
Valentine, K., Buchanan, E., Scofield, J., & Beauchamp, M. (2018). Beyond p-values: Utilizing Multiple Estimates to Evaluate Evidence. PsyArXiv. https://doi.org/10.17605/osf.io/9hp7y (R Markdown and data files: https://osf.io/u9hf4/)
Vuorre, M., & Curley, J. P. (2018). Curating Research Assets: A Tutorial on the Git Version Control System. PsyArXiv. https://doi.org/10.31234/osf.io/6tzh8 (R Markdown and data files: https://github.com/mvuorre/reproguide-curate)
Xu, R., DeShon, R. P., & Dishop, C. R. (2019). Challenges and Opportunities in the Estimation of Dynamic Models. Organizational Research Methods, 109442811984263. https://doi.org/10/gf3vbj
Zhang, H., Miller, K. F., Sun, X., & Cortina, K. S. (2020). Wandering eyes: Eye movements during mind wandering in video lectures. Applied Cognitive Psychology, acp.3632. https://doi.org/10/ggjvfp
Zhang, H., Qu, C., Miller, K. F., & Cortina, K. S. (2019). Missing the joke: Reduced rereading of garden-path jokes during mind-wandering. Journal of Experimental Psychology: Learning, Memory, and Cognition. https://doi.org/10/gf68nd
Zhang, T., Hu, G., Yang, Y., Wang, J., & Zhou, Y. (2019). All-Atom Knowledge-Based Potential for RNA Structure Discrimination Based on the Distance-Scaled Finite Ideal-Gas Reference State. Journal of Computational Biology. https://doi.org/10/ggcp6w
By now, there are a couple of R packages that provide convenience functions to facilitate the reporting of statistics in accordance with APA guidelines.
- apa: Format output of statistical tests in R according to APA guidelines
- APAstats: R functions for formatting results in APA style and other stuff
- apaTables: Create American Psychological Association (APA) Style Tables
- pubprint: This package takes the output of several statistical tests, collects the characteristic values and transforms it in a publish-friendly pattern
- schoRsch: Tools for Analyzing Factorial Experiments
- sigr: Concise formatting of significances in R
Obviously, not all journals require manuscripts and articles to be
prepared according to APA guidelines. If you are looking for other
journal article templates, the following list of rmarkdown/pandoc
packages and templates may be helpful.
- rticles: LaTeX Journal Article Templates for R Markdown
- chi-proc-rmd-template: ACM CHI Proceedings R Markdown Template
- Michael Sachs’ pandoc journal templates: Pandoc templates for the major statistics and biostatistics journals
If you know of other packages and templates, drop us a note, so we can add them here.