vegan - Community Ecology Package

Ordination methods, diversity analysis and other functions for community and vegetation ecologists.

Last updated 6 days ago

ecological-modellingecologyordination

448 stars 19.15 score 7 dependencies 426 dependents

EDMAinR - Euclidean Distance Matrix Analysis in R

A coordinate-free approach for comparing biological shapes using landmark data based on Lele and Richtsmeier (1991) <doi:10.1002/ajpa.1330860307>.

Last updated 1 years ago

comparing-biological-shapescoordinate-freelandmark-datamorphometricsmultivariate-statistics

3 stars 3.78 score 52 dependencies

clickrup - Interacting with the ClickUp v2 API from R

Work with the ClickUp productivity app from R to manage tasks, goals, time tracking, and more.

Last updated 8 months ago

apiclickupclickup-apiproject-management

18 stars 3.26 score 9 dependencies

phacking - Sensitivity Analysis for p-Hacking in Meta-Analyses

Fits right-truncated meta-analysis (RTMA), a bias correction for the joint effects of p-hacking (i.e., manipulation of results within studies to obtain significant, positive estimates) and traditional publication bias (i.e., the selective publication of studies with significant, positive results) in meta-analyses [see Mathur MB (2022). "Sensitivity analysis for p-hacking in meta-analyses." <doi:10.31219/osf.io/ezjsx>.]. Unlike publication bias alone, p-hacking that favors significant, positive results (termed "affirmative") can distort the distribution of affirmative results. To bias-correct results from affirmative studies would require strong assumptions on the exact nature of p-hacking. In contrast, joint p-hacking and publication bias do not distort the distribution of published nonaffirmative results when there is stringent p-hacking (e.g., investigators who hack always eventually obtain an affirmative result) or when there is stringent publication bias (e.g., nonaffirmative results from hacked studies are never published). This means that any published nonaffirmative results are from unhacked studies. Under these assumptions, RTMA involves analyzing only the published nonaffirmative results to essentially impute the full underlying distribution of all results prior to selection due to p-hacking and/or publication bias. The package also provides diagnostic plots described in Mathur (2022).

Last updated 1 years ago

1 stars 3.00 score 64 dependencies

moosecounter - Adaptive Moose Surveys

Adaptive Moose surveys.

Last updated 8 months ago

shiny

3 stars 2.65 score 65 dependencies

QPAD - QPAD estimates

QPAD.

Last updated 7 years ago

2.31 score 0 dependencies

KnockKnockJokes - Knock-Knock Jokes

An S4 exercise for Knock-Knock Joke lovers.

Last updated 8 years ago

1 stars 1.70 score 0 dependencies