R/invGauss.R:

CRAN
invGauss: Threshold Regression that Fits the (Randomized Drift) Inverse Gaussian Distribution to Survival Data

if(.test)
cat("\nNote: negative values in density\n") #
if(no.censor){

tests/testthat/test_compute_mean_pcv.R:

GITHUB
jnguyen92/jn.general: Generic functions I use

), "response_10", "response_11"))
o.b <- data.frame(x = c('a', 'a', 'b', 'b'), y = c(1, 2, 1, 3), o.a)
rownames(o.b

tests/testthat/test_compute_mean_pcv.R:

GITHUB
jennguyen1/jn.general: Generic functions I use

), "response_10", "response_11"))
o.b <- data.frame(x = c('a', 'a', 'b', 'b'), y = c(1, 2, 1, 3), o.a)
rownames(o.b

tests/testthat/test04-dtjoin_anti:

CRAN
fjoin: Data Frame Joins Leveraging 'data.table'

(DF_A, DF_B, on=c("id_A == id_B"))
# compare <-
# dtjoin(DF_B, DF_A, on=c("id_B == id_A"), indicate = TRUE, i.main

tests/testthat/test10-other-args.R:

CRAN
fjoin: Data Frame Joins Leveraging 'data.table'

with on.first", {
result <- dtjoin(DF_B, DF_A, on="v_B==v_A", on.first=TRUE)
expect_named(result, c("v_B","id_B

tests/testthat/test06-basic-classes-DF:

CRAN
fjoin: Data Frame Joins Leveraging 'data.table'

fjoin_left with do=FALSE, data.frames", {
expect_null(fjoin_left(DF_A, DF_B, on=c("id_A == id_B"), do=FALSE

R/OuterJoinMerge.R:

CRAN
Trading: Trade Objects, Advanced Correlation & Beta Estimates, Betting Strategies

#' @param df_a The first dataframe
#' @param df_b The second dataframe
#' @return A dataframe with all combinations

full_range_comparison: Intersection, differences and union of two dataframes

GITHUB
antonio-mora/vennRanges: Venn Diagrams of Genomic Ranges

the GenomicRanges package.
Usage
full_range_comparison(df_a, df_b)

R/pd_matchRows.R:

CRAN
dataCompareR: Compare Two Data Frames and Summarise the Difference

A dataframe
#' @param df_b A dataframe
#' @param indices The indices to match rows between \code{df_a} and \code{df_b}. Can

R/full_range_comparison.R:

GITHUB
antonio-mora/vennRanges: Venn Diagrams of Genomic Ranges

full_range_comparison <- function(df_a, df_b) {
cat("Intersection, differences and union of two dataframes of genomic

R/full_range_comparison.R:

GITHUB
antonio-mora/chrombrowseR: Chromatin Browser

full_range_comparison <- function(df_a, df_b) {
cat("Intersection, differences and union of two dataframes of genomic

tests/testthat/test-smk-expt-nextDS.R:

GITHUB
patRyserWelch8/dsShareServer: Provide the server-side functions for privacy-preserving sharing of parameters and encrypted datasets

("dsShareServer::nextDS::smk")
test_that("nothing has been set",
expect_error(nextDS())

tests/testthat/test12-checks:

CRAN
fjoin: Data Frame Joins Leveraging 'data.table'

", {
fjoin_left(DF_A, DF_B, on="id_A==id_B", order="left") |>
expect_no_error()

tests/testthat/test03-dtjoin_semi:

CRAN
fjoin: Data Frame Joins Leveraging 'data.table'

)"
if (PRINT_TEST_NAME) cat("\nTest: ", desc, "\n")
test_that(desc, {

tests/testthat/test02-dtjoin-outcomes.R:

CRAN
fjoin: Data Frame Joins Leveraging 'data.table'

(DF_B, DF_A, on=c("id_B == id_A", "t_B < t_A"), nomatch = NULL)
compare <-
dplyr::inner_join(DF_A, DF_B, by=dplyr

tests/testthat/test-smk-expt-nextDS.R:

GITHUB
patRyserWelch8/dsShareServerNewVersion: Provide the server-side functions for privacy-preserving sharing of parameters and encrypted datasets

("dsShareServer::nextDS::smk")
test_that("nothing has been set",
expect_error(nextDS())

tests/testthat/test05-dtjoin_cross:

CRAN
fjoin: Data Frame Joins Leveraging 'data.table'

DF_A <- DF_A[1:3,]
DF_B <- DF_B[1:2,]
desc <- "dtjoin_cross"

R/OuterJoinMerge.R:

RFORGE
Trading: CCR, Advanced Correlation & Beta Estimates, Betting Strategies

#' @param df_a The first dataframe
#' @param df_b The second dataframe
#' @return A dataframe with all combinations

full_range_comparison: Intersection, differences and union of two dataframes

GITHUB
antonio-mora/chrombrowseR: Chromatin Browser

the GenomicRanges package.
Usage
full_range_comparison(df_a, df_b)

tests/testthat:

GITHUB
patRyserWelch8/dsShareServerNewVersion: Provide the server-side functions for privacy-preserving sharing of parameters and encrypted datasets

= df_a, encoded = df_b, limit = 1000))
expect_false(idds.are.values.in.limit(server = df_a, encoded = df_c, limit = 1000