Keng is the abbreviation of “Knock Errors off Nice Guesses.” Hope the functions and data gathered in the Keng package help to ease your life.

You can install the development version of Keng from GitHub with:

# install devtools if it is not installed
if (!requireNamespace("devtools", quietly = TRUE)) {
  install.packages("devtools")
}

# install the developing version of Keng from GitHub
devtools::install_github("qyaozh/Keng", dependencies = TRUE, build_vignettes = TRUE)

Before using the Keng package, load it using the library() function.

library(Keng)

Here is a list of the data and functions gathered in the Keng package. Their usages are detailed in the documentation.

depress is a subset of data from a research about depression and coping.

Scale() could change the origin of a numeric vector x (including mean-centering it), or standardize the mean and standard deviation of x (including transforming it to its z-score).

cut_r() gives you the cut-off values of Pearson’s r at the significance levels of p = 0.1, 0.05, 0.01, and 0.001 with known sample size n.

test_r() tests the significance and compute the post-hoc power of r with known sample size n.

powered_r() conducts post-hoc power analysis with known sample size n. 

power_r() conducts a priori power analysis and plan the sample size for r.

compare_lm() compares lm()’s fitted outputs using PRE, R², f², and post-hoc power.

calc_PRE() calculates PRE from partial correlation, Cohen’s f, or f_squared.

powered_lm() conducts post-hoc power analysis with known sample size n.

power_lm() conducts a priori power analysis and plans the sample size for one or a set of predictors in regression analysis.

power_r() and power_lm() return the Keng_power class, which has print() and plot() methods.

print() prints primary but not all contents of the Keng_power class.

plot() plots the power against sample size for the Keng_power class.

pick_sl() and pick_dcb() have been added to randomly pick numbers for Chinese Super Lotto and Double Color Balls.

assess_coa() calculates course objective achievement based on students’ grades per session, weights of each session, and weights of course objectives within each session.