Package: cofad 0.3.9000

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]

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NEWS

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

Peer review:

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

Datasets:

On CRAN:

4.15 score 2 stars 8 scripts 224 downloads 9 exports 90 dependencies

Last updated 5 months agofrom:b3b90dbb1f. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 03 2024
R-4.5-winOKNov 03 2024
R-4.5-linuxOKNov 03 2024
R-4.4-winOKNov 03 2024
R-4.4-macOKNov 03 2024
R-4.3-winOKNov 03 2024
R-4.3-macOKNov 03 2024

Exports:%>%calc_contrastcalc_contrast_aggregatedcalc_r_alertingcalc_r_alerting_from_fcalc_r_contrastcalc_r_effectsizelambda_diffrun_app

Dependencies:backportsbase64encbitbit64bslibcachemcheckmateclicliprclustercolorspacecommonmarkcpp11crayondata.tabledigestdplyrevaluatefansifarverfastmapfontawesomeforeignFormulafsgenericsggplot2gluegridExtragtablehighrHmischmshtmlTablehtmltoolshtmlwidgetshttpuvisobandjquerylibjsonliteknitrlabelinglaterlatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmennetpillarpkgconfigprettyunitsprogresspromisesR6rappdirsRColorBrewerRcppreadrrhandsontablerlangrmarkdownrpartrstudioapisassscalesshinyshinydashboardshinyjssourcetoolsstringistringrtibbletidyselecttinytextzdbutf8vctrsviridisviridisLitevroomwithrxfunxtableyaml

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
Empathy data set by Furr (2004)furr_p4
Haans within data examplehaans_within1by4
Calculate lambdas for two competing hypotheseslambda_diff
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
Therapy data set by Rosenthal and Rosnow (2000)rosenthal_tbl59
Data set by Rosenthal and Rosnow (2000)rosenthal_tbl68
Starts the mimosa 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