version 0.2.0

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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: spINAR
 Type: Package
 Title: (Semi)Parametric Estimation and Bootstrapping of INAR Models
-Version: 0.1.0
-Date: 2023-03-22
+Version: 0.2.0
+Date: 2024-04-08
 Authors@R: c(person(given = "Maxime",
            family = "Faymonville",
            role = c("aut", "cre"),
@@ -22,7 +22,12 @@ Authors@R: c(person(given = "Maxime",
            family = "Jentsch",
            role = "aut",
            email = "jentsch@statistik.tu-dortmund.de",
-           comment = c(ORCID = "0000-0001-7824-1697")))
+           comment = c(ORCID = "0000-0001-7824-1697")),
+    person(given = "Christian H.",
+           family = "Weiß",
+           role = "ctb",
+           email = "weissc@hsu-hh.de",
+           comment = c(ORCID = "0000-0001-8739-6631")))
 Maintainer: Maxime Faymonville <faymonville@statistik.tu-dortmund.de>
 Description: Semiparametric and parametric estimation of INAR models including a finite sample refinement (Faymonville et al. (2022) <doi:10.1007/s10260-022-00655-0>) for the semiparametric setting introduced in Drost et al. (2009) <doi:10.1111/j.1467-9868.2008.00687.x>, different procedures to bootstrap INAR data (Jentsch, C. and Weiß, C.H. (2017) <doi:10.3150/18-BEJ1057>) and flexible simulation of INAR data.
 License: GPL (>= 3)
@@ -34,11 +39,13 @@ URL: https://github.com/MFaymon/spINAR
 BugReports: https://github.com/MFaymon/spINAR/issues
 Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
 Config/testthat/edition: 3
+VignetteBuilder: knitr
 NeedsCompilation: no
-Packaged: 2023-03-24 15:08:04 UTC; faymonville
+Packaged: 2024-04-08 13:32:13 UTC; faymonville
 Author: Maxime Faymonville [aut, cre] (<https://orcid.org/0000-0001-5565-0711>),
   Javiera Riffo [aut] (<https://orcid.org/0000-0002-5372-7196>),
   Jonas Rieger [aut] (<https://orcid.org/0000-0002-0007-4478>),
-  Carsten Jentsch [aut] (<https://orcid.org/0000-0001-7824-1697>)
+  Carsten Jentsch [aut] (<https://orcid.org/0000-0001-7824-1697>),
+  Christian H. Weiß [ctb] (<https://orcid.org/0000-0001-8739-6631>)
 Repository: CRAN
-Date/Publication: 2023-03-26 19:10:02 UTC
+Date/Publication: 2024-04-08 14:00:02 UTC
diff --git a/MD5 b/MD5
@@ -1,4 +1,4 @@
-625a9223fddbc36dcd5b838cf2189b2b *DESCRIPTION
+7802f73bde797f15a357818157732535 *DESCRIPTION
 521e416b3c8d71439de3ecdfa46b9e34 *NAMESPACE
 147bb4886da91830fc5e26d3aa5bb796 *R/helper.R
 6e4a0dbb9bbf0df63af78bd320f52d0e *R/helper_geo.R
@@ -9,18 +9,22 @@ f53a3bf3e86e8280886565c990ef9728 *R/llpinar_geo.R
 e5825266f0e4110552950a52a70fa1b3 *R/llpinar_poi.R
 79440ccc744b95b296e02f623914404c *R/llspinar.R
 9d1b7e4a45c6487947432424b7b71a1a *R/llspinar_penal.R
-d6b30310b37b6db52464e6cf62bf0307 *R/spINAR-package.R
-ab1f9bdf4ce3b4f6cae5d4a998aeae8c *R/spinar_boot.R
-8de9726017b52a90637fb6bc8c895b66 *R/spinar_est.R
-d0795d6e955fb6fd638acf5dd0cc8ea5 *R/spinar_est_param.R
-ba863da4c0caced1f67ad121b24b9e7e *R/spinar_penal.R
-fc12844891d5072c790f309514c9cecb *R/spinar_penal_val.R
-7b52a2751f5b7ee4d01df5e6e6b9ce54 *R/spinar_sim.R
-6fb2b28c486d8210aa8b835626be00a7 *README.md
+50bdb30e13a175471b36ac172b006773 *R/spINAR-package.R
+15f0ca2bbecadcd0319c522e9c59f057 *R/spinar_boot.R
+0b3379cc828c474a8ef608e759ef3f6d *R/spinar_est.R
+ec56c4bc6b28934c8505d5bc3c58c74f *R/spinar_est_param.R
+7d4493378930ac44db443ea8864e9338 *R/spinar_penal.R
+256be8d4a26a85e1cd898d9503f83423 *R/spinar_penal_val.R
+bb983129f6cc7c348ba6631ec156195c *R/spinar_sim.R
+63ac0a7e8c6a48b24efce2d4be5682e0 *README.md
 cb4924f3573383fca290a0a8e2ca5294 *build/partial.rdb
-63acb560a7149aef28f7e4d3d2ca1742 *inst/CITATION
-37b47ffc27856fcd97613b6bf93487b4 *man/spINAR-package.Rd
-1ff74f70dbc05168413e55436af25803 *man/spinar_boot.Rd
+bd47dd25c18d0c924cacea0cc0babcd9 *build/vignette.rds
+5b32945dc26158813b56cdbc4d96bb0c *inst/CITATION
+37255b1f270e64c5510e31d63a235069 *inst/doc/Replicate_literature_results_using_spINAR.R
+8f94b9fb65a5e7f892ffb06064979913 *inst/doc/Replicate_literature_results_using_spINAR.Rmd
+784bcd69254d8a4964b8fd7a4fb22ab4 *inst/doc/Replicate_literature_results_using_spINAR.html
+9957b6ea142468bfb813dae9c010f006 *man/spINAR-package.Rd
+71ea23785b5e121d6532120f541997b6 *man/spinar_boot.Rd
 7e141f281c3fb7090f0fcb608684eb2e *man/spinar_est.Rd
 4b3b6108902efae7bc152e30ba1c811c *man/spinar_est_param.Rd
 f9d11e5295c1854c3924f35bd40b3efb *man/spinar_penal.Rd
@@ -32,4 +36,6 @@ efb41e8ff1ea02800c8d26eefd38e750 *tests/testthat/test_spinar_boot.R
 f006cddbc473f6a5bdf98125cb79bd0e *tests/testthat/test_spinar_est_param.R
 98786eb5d0f252fc910fcc2d4dd3c5dd *tests/testthat/test_spinar_penal.R
 f70d321411693251e958aa65c17cf923 *tests/testthat/test_spinar_penal_val.R
-2e0653b0e66c8bfd6229206f73826218 *tests/testthat/test_spinar_sim.R
+0233ad6f957d6c1fd1c28ac0ba00166d *tests/testthat/test_spinar_sim.R
+8f94b9fb65a5e7f892ffb06064979913 *vignettes/Replicate_literature_results_using_spINAR.Rmd
+e1b42d3b16765f810aacc0ed80194e46 *vignettes/barplots.png
diff --git a/R/spINAR-package.R b/R/spINAR-package.R
@@ -20,7 +20,7 @@
 #'
 #' @references
 #' Faymonville, M., Jentsch, C., Weiß, C.H. and Aleksandrov, B. (2022).
-#' Semiparametric Estimation of INAR Models using Roughness Penalization.
+#' "Semiparametric Estimation of INAR Models using Roughness Penalization".
 #' Statistical Methods & Applications. \doi{10.1007/s10260-022-00655-0}.
 #'
 #' Jentsch, C. and Weiß, C. H. (2017), “Bootstrapping INAR Models”.

diff --git a/R/spinar_boot.R b/R/spinar_boot.R
@@ -13,10 +13,10 @@
 #' estimation setting \eqn{\in \code{\{"sp", "p"\}}}, where \code{"sp"} defines a semiparametric setting and
 #' \code{"p"} a parametric setting.
 #' @param type [\code{string(1)}]\cr
-#' type of estimation \eqn{\in \code{\{"mom", "ml"\}}}, where \code{"mom"} performs moment-based estimation and
+#' type of estimation \eqn{\in \code{\{"mom", "ml"\}}}, where \code{"mom"} (default) performs moment-based estimation and
 #' \code{"ml"} maximum likelihood-based estimation.
 #' @param distr [\code{string(1)}]\cr
-#' parametric family of innovation distribution \eqn{\in  \code{\{"poi", "geo", "nb"\}}}, where \code{"poi"} denotes
+#' parametric family of innovation distribution \eqn{\in  \code{\{"poi", "geo", "nb"\}}}, where \code{"poi"} (default) denotes
 #' Poi(\code{lambda}), \code{"geo"} Geo(\code{prob}) and \code{"nb"} NB(\code{r}, \code{prob}) distributions.
 #' @param M [\code{integer(1)}]\cr
 #' upper limit for the innovations.
@@ -42,10 +42,10 @@
 #' coefficients \eqn{\code{alpha}_1,...,\code{alpha}_p} and the estimated
 #' parameter(s) of the innovation distribution.}
 #' \item{\code{bs_ci_percentile}}{[\code{named matrix}] with the lower and
-#' upper bounds of Hall's bootstrap percentile confidence intervals for each
+#' upper bounds of the bootstrap percentile confidence intervals for each
 #' parameter in \code{parameters_star}.}
 #' \item{\code{bs_ci_hall}}{[\code{named matrix}] with the lower and
-#' upper bounds of the bootstrap percentile confidence intervals for each
+#' upper bounds of Hall's bootstrap percentile confidence intervals for each
 #' parameter in \code{parameters_star}.}
 #' }
 #'
@@ -64,15 +64,15 @@
 #' }
 #'
 #' @export spinar_boot
-spinar_boot <- function(x, p, B, setting, type = NA, distr = NA, M = 100, level = 0.05, progress = TRUE){
-  assert_integerish(p, lower = 1, upper = 2, len = 1)
+spinar_boot <- function(x, p, B, setting, type = "mom", distr = "poi", M = 100, level = 0.05, progress = TRUE){
+  assert_integerish(p, lower = 1, upper = 2, len = 1, any.missing = FALSE)
   assert_integerish(B, lower = 1, len = 1)
   assert_integerish(x, lower = 0, min.len = p+1)
   assert_choice(setting, c("sp", "p"))
-  assert_choice(type, c("mom", "ml", NA))
-  assert_choice(distr, c("poi", "geo", "nb", NA))
+  assert_choice(type, c("mom", "ml"))
+  assert_choice(distr, c("poi", "geo", "nb"))
   assert_integerish(M, lower = 0, len =  1)
-  assert_numeric(level, lower = 0, upper = 1, len = 1)
+  assert_numeric(level, lower = 1e-16, upper = 1, len = 1)
 
   bs <- list(x_star = matrix(NA, length(x), B), parameters_star = matrix(0, B, M+p+1),
              bs_ci_percentile = NULL, bs_ci_hall = NULL)
@@ -136,7 +136,8 @@ spinar_boot <- function(x, p, B, setting, type = NA, distr = NA, M = 100, level
       bs$bs_ci_percentile[1,i] <- srt[B*level/2]
       bs$bs_ci_percentile[2,i] <- srt[B*(1-level/2)]
     } else{
-      K <- ceiling((B+1)*level/2)
+      K <- floor((B+1)*level/2)
+      if(K == 0){K <- 1}
       bs$bs_ci_percentile[1,i] <- srt[K]
       bs$bs_ci_percentile[2,i] <- srt[B+1-K]
     }
@@ -152,7 +153,8 @@ spinar_boot <- function(x, p, B, setting, type = NA, distr = NA, M = 100, level
       bs$bs_ci_hall[1,i] <- parameters[i] - srt[B*(1-level/2)]
       bs$bs_ci_hall[2,i] <- parameters[i] - srt[B*level/2]
     } else{
-      K <- ceiling((B+1)*level/2)
+      K <- floor((B+1)*level/2)
+      if(K == 0){K <- 1}
       bs$bs_ci_hall[1,i] <- parameters[i] - srt[B+1-K]
       bs$bs_ci_hall[2,i] <- parameters[i] - srt[K]
     }

diff --git a/R/spinar_est.R b/R/spinar_est.R
@@ -29,7 +29,7 @@
 #'
 #' @export spinar_est
 spinar_est <- function(x, p) {
-  assert_integerish(p, lower = 1, upper = 2, len =  1)
+  assert_integerish(p, lower = 1, upper = 2, len =  1, any.missing = FALSE)
   assert_integerish(x, min.len = p+1, lower = 0)
   xmax <- max(x)
   if(p==1){

diff --git a/R/spinar_est_param.R b/R/spinar_est_param.R
@@ -37,7 +37,7 @@
 #'
 #' @export spinar_est_param
 spinar_est_param <- function(x, p, type, distr){
-  assert_integerish(p, lower = 1, upper = 2, len = 1)
+  assert_integerish(p, lower = 1, upper = 2, len = 1, any.missing = FALSE)
   assert_integerish(x, min.len = p+1, lower = 0)
   assert(checkChoice(type, c("mom", "ml")))
   assert(checkChoice(distr, c("poi", "geo", "nb")))
@@ -163,11 +163,13 @@ spinar_est_param <- function(x, p, type, distr){
       else {
         if(p==1){
           prob <- max(min(mean(x)/(var(x)*(1+eacf1)-eacf1*mean(x)),0.99),0)
-          theta <- c(max(eacf1, 1e-5), round((mean(x)*prob*(1-eacf1))/(1-prob)), prob)
+          rs <- ifelse(round((mean(x)*prob*(1-eacf1))/(1-prob)) == 0, 1, round((mean(x)*prob*(1-eacf1))/(1-prob)))
+          theta <- c(max(eacf1, 1e-5), rs, prob)
         }
         if(p==2){
           prob <- max(min(mean(x)/(var(x)*(1+ealpha1+ealpha2)-(ealpha1+ealpha2)*mean(x)),0.99),0)
-          theta <- c(max(ealpha1, 1e-5), max(ealpha2, 1e-5), round((mean(x)*prob*(1-ealpha1-ealpha2))/(1-prob)), prob)
+          rs <- ifelse(round((mean(x)*prob*(1-ealpha1-ealpha2))/(1-prob)) == 0, 1, round((mean(x)*prob*(1-ealpha1-ealpha2))/(1-prob)))
+          theta <- c(max(ealpha1, 1e-5), max(ealpha2, 1e-5), rs, prob)
         }
         est <- suppressWarnings(constrOptim(
           theta = theta,

diff --git a/R/spinar_penal.R b/R/spinar_penal.R
@@ -28,7 +28,7 @@
 #'
 #' @export spinar_penal
 spinar_penal <- function(x, p, penal1 = 0, penal2 = 0) {
-  assert_integerish(p, lower = 1, upper = 2, len = 1)
+  assert_integerish(p, lower = 1, upper = 2, len = 1, any.missing = FALSE)
   assert_integerish(x, lower = 0, min.len = p+1)
   assert_numeric(penal1, len = 1)
   assert_numeric(penal2, len = 1)

diff --git a/R/spinar_penal_val.R b/R/spinar_penal_val.R
@@ -53,8 +53,9 @@
 #'
 #' @export spinar_penal_val
 spinar_penal_val <- function(x, p, validation, penal1 = NA, penal2 = NA, over = NA, folds = 10, init1 = 1, init2 = 1, progress = TRUE){
-  assert_integerish(p, lower = 1, len = 1, upper = 2)
+  assert_integerish(p, lower = 1, len = 1, upper = 2, any.missing = FALSE)
   assert_integerish(x, lower = 0, min.len = p+1)
+  assert_logical(validation, any.missing = FALSE)
   assert_numeric(penal1, len = 1)
   assert_numeric(penal2, len = 1)
 
@@ -64,7 +65,6 @@ spinar_penal_val <- function(x, p, validation, penal1 = NA, penal2 = NA, over =
     if(is.na(penal2)){penal2 <- 0}
     parameters <- spinar_penal(x, p, penal1, penal2)
   } else{
-    assert_logical(validation)
     assert(checkChoice(over, c("L1", "L2", "both")))
     assert_integerish(folds, lower = 2, upper = floor((length(x)/(p+1))), len = 1)
     assert_numeric(init1, len = 1)

diff --git a/R/spinar_sim.R b/R/spinar_sim.R
@@ -32,14 +32,17 @@
 #'
 #' @export spinar_sim
 spinar_sim <- function(n, p, alpha, pmf, prerun = 500) {
-  assert_integerish(p, lower = 1, upper = 2, len = 1)
+  assert_integerish(p, lower = 1, upper = 2, len = 1, any.missing = FALSE)
   assert_numeric(alpha, lower = 0, upper = 1, len = p)
   assert_numeric(pmf, lower = 0, upper = 1, min.len = p+1)
-  assert_integerish(n, lower = 0, len = 1)
+  assert_integerish(n, lower = 0, len = 1, any.missing = FALSE)
   assert_integerish(prerun, lower = 0, len = 1)
   if (round(sum(pmf), 6) != 1) {
     warning("Sum of pmf entries has been standardized to 1.")
   }
+  if (n + prerun <= p) {
+    stop("Sum of n and prerun must be greater than p.")
+  }
   pmf <- pmf / sum(pmf)
   err <-
     sample(0:(length(pmf) - 1),
@@ -62,6 +65,11 @@ spinar_sim <- function(n, p, alpha, pmf, prerun = 500) {
         rbinom(1, x[i - 1], alpha1) + rbinom(1, x[i - 2], alpha2) + err[i]
     }
   }
-  return(x[-seq_len(prerun)])
+  if (prerun > 0){
+    return(x[-seq_len(prerun)])
+  }
+  else {
+    return(x)
+  }
 }
 
diff --git a/README.md b/README.md
@@ -1,5 +1,6 @@
 # spINAR
-[![status](https://joss.theoj.org/papers/6fcfcc77635fdd18153b35d5986fe2aa/status.svg)](https://github.com/openjournals/joss-reviews/issues/5152)
+[![status](https://joss.theoj.org/papers/6fcfcc77635fdd18153b35d5986fe2aa/status.svg)](https://github.com/openjournals/joss-reviews/issues/5386)
+[![CRAN](https://www.r-pkg.org/badges/version/spINAR)](https://cran.r-project.org/package=spINAR)
 [![R build status](https://github.com/MFaymon/spINAR/workflows/R-CMD-check/badge.svg)](https://github.com/MFaymon/spINAR/actions)
 [![codecov](https://codecov.io/gh/MFaymon/spINAR/branch/main/graph/badge.svg?token=U5KPFSY3XN)](https://app.codecov.io/gh/MFaymon/spINAR)
 [![DOI](https://zenodo.org/badge/520425653.svg)](https://zenodo.org/badge/latestdoi/520425653)
@@ -10,10 +11,15 @@ The package provides flexible simulation of INAR data using a general pmf to def
 
 ## References (related to the methodology)
 
-* Faymonville, M., Jentsch, C., Weiß, C.H. und Aleksandrov, B. (2022). Semiparametric Estimation of INAR Models using Roughness Penalization. Statistical Methods & Applications. [DOI](https://doi.org/10.1007/s10260-022-00655-0)
+* Faymonville, M., Jentsch, C., Weiß, C.H. and Aleksandrov, B. (2022). "Semiparametric Estimation of INAR Models using Roughness Penalization". Statistical Methods & Applications. [DOI](https://doi.org/10.1007/s10260-022-00655-0)
 * Jentsch, C. and Weiß, C.H. (2017), “Bootstrapping INAR Models”. Bernoulli 25(3), pp. 2359-2408. [DOI](https://doi.org/10.3150/18-BEJ1057)
 * Drost, F., Van den Akker, R. and Werker, B. (2009), “Efficient estimation of auto-regression parameters and inovation distributions for semiparametric integer-valued AR(p) models”. Journal of the Royal Statistical Society. Series B 71(2), pp. 467-485. [DOI](https://doi.org/10.1111/j.1467-9868.2008.00687.x)
 
+## Contribution
+This R package is licensed under the [GPLv3](https://www.gnu.org/licenses/gpl-3.0.en.html).
+For bug reports (lack of documentation, misleading or wrong documentation, unexpected behaviour, ...) and feature requests please use the [issue tracker](https://github.com/MFaymon/spINAR/issues).
+Pull requests are welcome and will be included at the discretion of the author.
+
 ## Installation
 
 For installation of the development version use [devtools](https://cran.r-project.org/package=devtools):
@@ -73,3 +79,9 @@ spinar_boot(dat1, 1, 500, setting = "sp")
 spinar_boot(dat1, 1, 500, setting = "p", type = "mom", distr = "poi")
 ```
 
+## Application
+The file vignette.md provides reproduced results from the literature for each provided functionality of the spINAR package.
+
+## Outlook
+
+A possible extension of the spINAR package is to not only cover INAR models but also the extension to GINAR (generalized INAR) models, see [Latour (1997)](https://doi.org/10.1017/s0001867800027865).  This model class does not only cover the binomial thinning but also allows for other thinning operations, e.g. thinning using geometrically distributed random variables. 
diff --git a/build/vignette.rds b/build/vignette.rds
diff --git a/inst/CITATION b/inst/CITATION
@@ -1,18 +1,13 @@
 citHeader("To cite spINAR in publications use:")
 
-year = sub(".*(2[[:digit:]]{3})-.*", "\\1", meta$Date)
-vers = paste("R package version", meta$Version)
-
-bibentry(bibtype = "Manual",
-  title        = "{spINAR}: Semiparametric and Parametric Estimation and Bootstrapping of Integer-Valued Autoregressive (INAR) Models",
+bibentry(bibtype = "Article",
+  title        = "{spINAR}: An {R} Package for Semiparametric and Parametric Estimation and Bootstrapping of Integer-Valued Autoregressive ({INAR}) Models",
   author       = "Maxime Faymonville and Javiera Riffo and Jonas Rieger and Carsten Jentsch",
-  year         = year,
-  doi          = "10.5281/zenodo.7611601",
+  year         = "2024",
+  journal      = "Journal of Open Source Software",
   url          = "https://github.com/MFaymon/spINAR",
-  note         = vers,
   textVersion  =
-  paste("Faymonville et al. (", year, "). ",
-        "spINAR: Semiparametric and Parametric Estimation and Bootstrapping of Integer-Valued Autoregressive (INAR) Models. ",
-        vers, ". ",
-        "DOI: 10.5281/zenodo.7611601.", sep = "")
-)
+  paste("Faymonville et al. (2024).",
+        "{spINAR}: An {R} Package for Semiparametric and Parametric Estimation and Bootstrapping of Integer-Valued Autoregressive ({INAR}) Models.",
+        "Journal of Open Source Software")
+)