Showing 4 of total 4 results (show query)
psychbruce
bruceR:Broadly Useful Convenient and Efficient R Functions
Broadly useful convenient and efficient R functions that bring users concise and elegant R data analyses. This package includes easy-to-use functions for (1) basic R programming (e.g., set working directory to the path of currently opened file; import/export data from/to files in any format; print tables to Microsoft Word); (2) multivariate computation (e.g., compute scale sums/means/... with reverse scoring); (3) reliability analyses and factor analyses; (4) descriptive statistics and correlation analyses; (5) t-test, multi-factor analysis of variance (ANOVA), simple-effect analysis, and post-hoc multiple comparison; (6) tidy report of statistical models (to R Console and Microsoft Word); (7) mediation and moderation analyses (PROCESS); and (8) additional toolbox for statistics and graphics.
Maintained by Han-Wu-Shuang Bao. Last updated 4 days ago.
anovadata-analysisdata-sciencelinear-modelslinear-regressionmultilevel-modelsstatisticstoolbox
176 stars 8.16 score 316 scripts 3 dependents
jtimonen
lgpr:Longitudinal Gaussian Process Regression
Interpretable nonparametric modeling of longitudinal data using additive Gaussian process regression. Contains functionality for inferring covariate effects and assessing covariate relevances. Models are specified using a convenient formula syntax, and can include shared, group-specific, non-stationary, heterogeneous and temporally uncertain effects. Bayesian inference for model parameters is performed using 'Stan'. The modeling approach and methods are described in detail in Timonen et al. (2021) <doi:10.1093/bioinformatics/btab021>.
Maintained by Juho Timonen. Last updated 7 months ago.
bayesian-inferencegaussian-processeslongitudinal-datastancpp
25 stars 5.94 score 69 scripts
jasonmoy28
psycModel:Integrated Toolkit for Psychological Analysis and Modeling in R
A beginner-friendly R package for modeling in psychology or related field. It allows fitting models, plotting, checking goodness of fit, and model assumption violations all in one place. It also produces beautiful and easy-to-read output.
Maintained by Jason Moy. Last updated 7 months ago.
4 stars 5.59 score 14 scripts
