version 1.0.1

DESCRIPTION

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+Package: latenetwork
+Type: Package
+Title: Inference on LATEs under Network Interference of Unknown Form
+Version: 1.0.1
+Authors@R: c(
+    person("Tadao", "Hoshino", email = "thoshino@waseda.jp", role = c("aut", "cph"), comment = c(ORCID = "0000-0001-9484-2943")),
+    person("Takahide", "Yanagi", email = "yanagi@econ.kyoto-u.ac.jp", role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0003-3975-5518"))
+    )
+Description: Estimating causal parameters in the presence of treatment spillover is of great interest in statistics.
+    This package provides tools for instrumental variables estimation of average causal effects under network interference of unknown form.
+    The target parameters are the local average direct effect, the local average indirect effect, the local average overall effect, and the local average spillover effect.
+    The methods are developed by Hoshino and Yanagi (2023) <doi:10.48550/arXiv.2108.07455>.
+License: MIT + file LICENSE
+Encoding: UTF-8
+RoxygenNote: 7.2.3
+Imports: igraph, methods, statip, stats
+Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
+VignetteBuilder: knitr
+Config/testthat/edition: 3
+URL: https://tkhdyanagi.github.io/latenetwork/
+NeedsCompilation: no
+Packaged: 2023-08-07 23:58:25 UTC; Takahide Yanagi
+Author: Tadao Hoshino [aut, cph] (<https://orcid.org/0000-0001-9484-2943>),
+  Takahide Yanagi [aut, cre, cph]
+    (<https://orcid.org/0000-0003-3975-5518>)
+Maintainer: Takahide Yanagi <yanagi@econ.kyoto-u.ac.jp>
+Repository: CRAN
+Date/Publication: 2023-08-08 16:10:02 UTC

LICENSE

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+YEAR: 2023
+COPYRIGHT HOLDER: latenetwork authors

MD5

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+16650403f4bcc611f62b50e40f5b1dcc *DESCRIPTION
+c82e8e2a7bbc0691c4b7ef9fe1df9069 *LICENSE
+9e14a4eda7a2172dc6e9171e6f0b49ce *NAMESPACE
+e8df72ce2d85abff5f3877901ec8be3f *NEWS.md
+4824ed349bf17f058a3405a4a90d2bb0 *R/datageneration.R
+c9920eb0c8b2335776dc815804150fe2 *R/direct.R
+4143bfc8206913e2b4b492688c8e8f02 *R/errorhandling.R
+b94ba97e39a4adf60eda6aa567f9aec5 *R/indirect.R
+bce951678803112f701e44e3b6f95c50 *R/overall.R
+7bec1ab1b555bddac7d0865a7c0cc85d *R/spillover.R
+6981d40e28b30bdd5e72de3ff6bdd451 *README.md
+bb8817ad6d2796ea70acb4bddee57e52 *build/vignette.rds
+4c47b4bf5fb34eba001c4770b73a667f *inst/doc/latenetwork.R
+6c649d17532f6449388ce2bc6aa968ef *inst/doc/latenetwork.Rmd
+d0e64ee37044b0981708afa6a536a5ac *inst/doc/latenetwork.html
+b1d958e670b178ffbe6292d72cd1a227 *inst/doc/review.R
+bc85c33df34310663bf8efc52f4c5386 *inst/doc/review.Rmd
+fe6c674cff9571bb51b2810932e6aae0 *inst/doc/review.html
+7dd6da675305de80e58029970f3e8b5d *man/datageneration.Rd
+29e7467e73659674de283d9be6829ea8 *man/direct.Rd
+36c9f0a14024b58ad16d8f7326953ca8 *man/indirect.Rd
+736a8636017d5e71b8bed73bab0f25fd *man/overall.Rd
+77ea34d5f035b068ea4617461fcba4ef *man/spillover.Rd
+a8a8b849110ed65b729f9efdaadfd6fe *tests/testthat.R
+8358034463198650a58ba31c1aeeb80d *tests/testthat/test-direct.R
+bdaf59a3e6997810b257ca4e96e8a6ff *vignettes/file1450181067a1.html
+6c649d17532f6449388ce2bc6aa968ef *vignettes/latenetwork.Rmd
+bc85c33df34310663bf8efc52f4c5386 *vignettes/review.Rmd

NAMESPACE

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+# Generated by roxygen2: do not edit by hand
+
+export(datageneration)
+export(direct)
+export(indirect)
+export(overall)
+export(spillover)

NEWS.md

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+# latenetwork 1.0.1
+
+* Second CRAN submission.
+
+# latenetwork 1.0.0
+
+* First CRAN submission.
+
+# latenetwork 0.1.2
+
+* Updated DESCRIPTION, README, and vignettes.
+
+# latenetwork 0.1.1
+
+* Added GitHub Pages.
+
+# latenetwork 0.1.0
+
+* First version.

R/datageneration.R

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+#' Generate Artificial Data by Simulation
+#'
+#' The `datageneration()` function generates artificial ring-network data
+#' by simulation.
+#' The function is used in the package vignette.
+#'
+#' @param n The sample size
+#'
+#' @returns A list containing the outcome vector, the treatment vector,
+#' the instrumental vector, and the true instrumental exposure vector, and
+#' the symmetric binary adjacency matrix.
+#'
+#' @examples
+#' latenetwork::datageneration(n = 2000)
+#'
+#' @export
+#'
+datageneration <- function(n) {
+
+  # Adjacency matrix of ring network
+  ring <- igraph::make_ring(n,
+                            directed = FALSE,
+                            mutual = FALSE,
+                            circular = TRUE)
+  A <- igraph::as_adjacency_matrix(ring)
+  A <- as.matrix(A)
+
+  # Instrumental vector
+  Z <- sample(x = 0:1,
+              size = n,
+              replace = TRUE,
+              prob = c(0.5, 0.5))
+
+  # Instrumental exposure
+  IEM <- ifelse(A %*% Z > 0, 1, 0)
+
+  # Treatment vector
+  D <- ifelse(stats::rnorm(n, mean = -1.5, sd = 1) + Z + IEM >= 0, 1, 0)
+
+  # Outcome vector
+  Y <- stats::rnorm(n, mean = 1, sd = 1) + stats::runif(n, min = 1, max = 2) * D
+
+  # Return
+  return(list(Y = Y,
+              D = D,
+              Z = Z,
+              IEM = IEM,
+              A = A))
+
+}