BMisc

BMisc includes miscellaneous functions useful for applied econometrics, with a focus on panel data and distributional analysis. Utilities cover balancing panels, working with distribution functions, computing weighted statistics, manipulating formulas, and supporting staggered treatment adoption settings (e.g., identifying treatment groups and recovering pre-treatment outcomes).

Installation

You can install BMisc from CRAN with:

install.packages("BMisc")

Or install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("bcallaway11/BMisc")

Example 1: Working with distribution functions

make_dist creates a distribution function from a vector of values and their corresponding CDF values.

library(BMisc)
y <- rnorm(100)
y <- y[order(y)]
u <- runif(100)
u <- u[order(u)]
F <- make_dist(y, u)
class(F)
#> [1] "ecdf"     "stepfun"  "function"
# plot(F)

Example 2: Working with panel data

make_balanced_panel drops observations from a panel dataset that are not available in all time periods.

id <- rep(seq(1, 100, 1), 2) ## individual ids for setting up a two period panel
t <- rep(seq(1, 2), 100) ## time periods
y <- rnorm(200) ## outcomes
dta <- data.frame(id = id, t = t, y = y) ## make into data frame
dta <- dta[-7, ] ## drop the 7th row from the dataset (which creates an unbalanced panel)
nrow(dta)
#> [1] 199
dta <- make_balanced_panel(dta, idname = "id", tname = "t")
nrow(dta) ## now all the observations with missing data in any period are dropped
#> [1] 198

Example 3: Staggered treatment adoption

get_group identifies the time period in which each unit first becomes treated. check_staggered verifies that treatment is absorbing (no de-treatments).

n <- 100
id <- rep(seq_len(n), each = 4)
t <- rep(1:4, n)
## assign units to groups: 0 = never treated, 2 = treated from period 2, 3 = from period 3
g <- rep(sample(c(0, 2, 3), n, replace = TRUE), each = 4)
treat <- as.integer(t >= g & g > 0)
dta <- data.frame(id = id, t = t, treat = treat)

dta$group <- get_group(dta, idname = "id", tname = "t", treatname = "treat")
head(unique(dta[, c("id", "group")]))
#>    id group
#> 1   1     2
#> 5   2     3
#> 9   3     2
#> 13  4     0
#> 17  5     0
#> 21  6     2
check_staggered(dta, idname = "id", treatname = "treat")
#> [1] TRUE

Example 4: Formula utilities

rhs extracts the right-hand side of a formula. toformula builds a formula from variable name strings — useful when constructing formulas programmatically.

ff <- y ~ x1 + x2 + x3
rhs(ff)
#> ~x1 + x2 + x3
#> <environment: 0x651fca30df70>
toformula("y", c("x1", "x2", "x3"))
#> y ~ x1 + x2 + x3
#> <environment: 0x651fca396268>

Example 5: Working with lists

get_list_element extracts the same element from every entry in a list — a common pattern when results are stored as a list of lists.

results <- lapply(1:5, function(i) list(est = i * 1.5, se = i * 0.1))
get_list_element(results, "est")
#> [[1]]
#> [1] 1.5
#> 
#> [[2]]
#> [1] 3
#> 
#> [[3]]
#> [1] 4.5
#> 
#> [[4]]
#> [1] 6
#> 
#> [[5]]
#> [1] 7.5
get_list_element(results, "se")
#> [[1]]
#> [1] 0.1
#> 
#> [[2]]
#> [1] 0.2
#> 
#> [[3]]
#> [1] 0.3
#> 
#> [[4]]
#> [1] 0.4
#> 
#> [[5]]
#> [1] 0.5