Package: cofad 0.4.0

Johannes Titz

cofad: Contrast Analyses for Factorial Designs

Contrast analysis for factorial designs provides an alternative to the traditional ANOVA approach, offering the distinct advantage of testing targeted hypotheses. The foundation of this package is primarily rooted in the works of Rosenthal, Rosnow, and Rubin (2000, ISBN: 978-0521659802) as well as Sedlmeier and Renkewitz (2018, ISBN: 978-3868943214).

Authors:Johannes Titz [aut, cre], Markus Burkhardt [aut], Mirka Henninger [ctb], Simone Malejka [ctb]

cofad_0.4.0.tar.gz
cofad_0.4.0.zip(r-4.7-any)cofad_0.4.0.zip(r-4.6-any)cofad_0.4.0.zip(r-4.5-any)
cofad_0.4.0.tgz(r-4.6-any)cofad_0.4.0.tgz(r-4.5-any)
cofad_0.4.0.tar.gz(r-4.7-any)cofad_0.4.0.tar.gz(r-4.6-any)
cofad_0.4.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
cofad/json (API)

# Install 'cofad' in R:
install.packages('cofad', repos = c('https://johannes-titz.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/johannes-titz/cofad/issues

Datasets:

On CRAN:

Conda:

3.68 score 2 stars 12 scripts 418 downloads 10 exports 73 dependencies

Last updated from:4de29ef3fb. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK158
source / vignettesOK201
linux-releaseOK178
macos-releaseOK146
macos-oldrelOK115
windows-develOK134
windows-releaseOK86
windows-oldrelOK109
wasm-releaseOK123

Exports:%>%calc_contrastcalc_contrast_aggregatedcalc_r_alertingcalc_r_alerting_from_fcalc_r_contrastcalc_r_effectsizedetect_designlambda_diffrun_app

Dependencies:askpassbase64encbslibcachemclicommonmarkcpp11crosstalkcurldata.tabledigestdplyrevaluatefarverfastmapfontawesomeforeignfsgenericsggplot2gluegtablehighrhtmltoolshtmlwidgetshttpuvhttrisobandjquerylibjsonliteknitrlabelinglaterlazyevallifecyclemagrittrmemoisemimeopensslotelpillarpkgconfigplotlypromisespurrrR6rappdirsRColorBrewerRcpprhandsontablerlangrmarkdownS7sassscalesshinyshinydashboardshinyjssourcetoolsstringistringrsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunxtableyaml

Readme and manuals

Help Manual

Help pageTopics
Data from Akan et al. (2018), experiment 2Bakan
Calculate contrast analysis for factorial designscalc_contrast
Calculate between contrast analysis from aggregated data (means, sds and ns)calc_contrast_aggregated
Calculate r_alerting from r_contrast and r_effectsizecalc_r_alerting
Calculate r_alerting from F-valuescalc_r_alerting_from_f
Calculate r_contrast from r_alerting and r_effectsizecalc_r_contrast
Calculate r_effectsize from r_contrast and r_alertingcalc_r_effectsize
Suggest roles for a contrast-analysis data setdetect_design
Empathy data set by Furr (2004)furr_p4
Haans within data examplehaans_within1by4
Calculate lambdas for two competing hypotheseslambda_diff
Data from Maraver et al. (2021)maraver
Output of between-subject design contrast analysisprint.cofad_bw
Output of a mixed design contrast analysisprint.cofad_mx
Output of a within subject design contrast analysisprint.cofad_wi
Complexity data set by Rosenthal and Rosnow (2000)rosenthal_chap5_q2
Data set by Rosenthal and Rosnow (2000)rosenthal_p141
Data set by Rosenthal and Rosnow (2000)rosenthal_tbl31
Children data set by Rosenthal and Rosnow (2000)rosenthal_tbl53
Response-profile data by Rosenthal et al. (2000)rosenthal_tbl54
Therapy data set by Rosenthal and Rosnow (2000)rosenthal_tbl59
Data set by Rosenthal and Rosnow (2000)rosenthal_tbl68
Corrected mixed-design Table 6.8 data by Rosenthal et al. (2000)rosenthal_tbl68_mixed
Start the cofad Shiny apprun_app
Data from Schwoebel et al. (2018)schwoebel
Problem solving data set by Sedlmeier & Renkewitz (2018)sedlmeier_p525
Music data set by Sedlmeier & Renkewitz (2018)sedlmeier_p537
Summary of between subject design contrast analysissummary.cofad_bw
Summary of a mixed design contrast analysissummary.cofad_mx
Summary of within subject design contrast analysissummary.cofad_wi
Testing Effect datatesting_effect