The {oeli} package offers a collection of handy
functions that I found useful while developing R packages. Perhaps
you’ll find them helpful too!
The released package version can be installed from CRAN via:
install.packages("oeli")The package includes helpers for various tasks and objects. Some demos are shown below. Click the headings for reference pages with documentation on all available helpers in each category.
The package has density and sampling functions for distributions not in base R, such as Dirichlet, multivariate normal, truncated normal, and Wishart. For faster computation, an Rcpp implementation is also available.
ddirichlet(x = c(0.2, 0.3, 0.5), concentration = 1:3)
#> [1] 4.5rdirichlet(concentration = 1:3)
#> [1] 0.2032900 0.2310928 0.5656171Retrieving default arguments of a function:
f <- function(a, b = 1, c = "", ...) { }
function_defaults(f)
#> $b
#> [1] 1
#>
#> $c
#> [1] ""Create all possible permutations of vector elements:
permutations(LETTERS[1:3])
#> [[1]]
#> [1] "A" "B" "C"
#>
#> [[2]]
#> [1] "A" "C" "B"
#>
#> [[3]]
#> [1] "B" "A" "C"
#>
#> [[4]]
#> [1] "B" "C" "A"
#>
#> [[5]]
#> [1] "C" "A" "B"
#>
#> [[6]]
#> [1] "C" "B" "A"Quickly have a basic logo for your new package:
package_logo("my_package", brackets = TRUE, use_logo = FALSE)
How to print a matrix without filling up the entire console?
x <- matrix(rnorm(10000), ncol = 100, nrow = 100)
print_matrix(x, rowdots = 4, coldots = 4, digits = 2, label = "what a big matrix")
#> what a big matrix : 100 x 100 matrix of doubles
#> [,1] [,2] [,3] ... [,100]
#> [1,] 0.01 0.59 -1.02 ... 1.73
#> [2,] -0.11 -0.01 2.37 ... 0.22
#> [3,] -1.87 0.09 -1.24 ... -0.93
#> ... ... ... ... ... ...
#> [100,] 0.6 -0.99 -0.26 ... -0.9Let’s simulate a Markov chain:
Gamma <- sample_transition_probability_matrix(dim = 3)
simulate_markov_chain(Gamma = Gamma, T = 20)
#> [1] 2 2 3 2 1 2 2 2 2 1 2 2 1 1 2 3 3 1 1 1The group_data_frame() function groups a given
data.frame based on the values in a specified column:
df <- data.frame("label" = c("A", "B"), "number" = 1:10)
group_data_frame(df = df, by = "label")
#> $A
#> label number
#> 1 A 1
#> 3 A 3
#> 5 A 5
#> 7 A 7
#> 9 A 9
#>
#> $B
#> label number
#> 2 B 2
#> 4 B 4
#> 6 B 6
#> 8 B 8
#> 10 B 10Is my matrix a proper transition probability matrix?
matrix <- diag(4)
matrix[1, 2] <- 1
check_transition_probability_matrix(matrix)
#> [1] "Must have row sums equal to 1"