master changes to 0.5.0.9000

.Rbuildignore

@@ -1,10 +1,13 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
-README.rmd
-README_cache/
-codecov.yml
-README.md
-cran-comments.md
 man-roxygen
-\.travis.yml
-_config.yml
+^README\.Rmd$
+^README-.*\.png$
+README_cache
+^\.travis\.yml$
+^_config\.yml$
+^codecov\.yml$
+^cran-comments\.md$
+^/\.gitattributes$
+^doc$
+^Meta$

.gitattributes

@@ -0,0 +1,3 @@
+# Declare files that will always have LF line endings on checkout.
+^configure\.ac$ text eol=lf
+^cleanup$ text eol=lf

.gitignore

@@ -1,10 +1,11 @@
 .Rproj.user
 .Rhistory
 .RData
-.Ruserdata
 src/*.o
 src/*.so
 src/*.dll
-autom4te.cache/
+README_cache/
 src/.DS_Store
+Meta
 .DS_Store
+doc

.travis.yml

@@ -1,29 +1,21 @@
 # R for travis: see documentation at https://docs.travis-ci.com/user/languages/r
 
 language: R
-sudo: false
+sudo: true
 cache: packages
+warnings_are_errors: true
 os:
   - linux
   - osx
 r:
-  - oldrel
   - release
-  - devel
-
-r_check_args: --as-cran
-
-matrix:
-  exclude:
-    - os: osx
-      r: devel
 
 branches:
   only:
   - master
 
 r_github_packages:
-  - jimhester/covr
+  - r-lib/covr
 r_binary_packages:
   - Rcpp
   - RcppArmadillo
@@ -32,6 +24,16 @@ r_binary_packages:
   - testthat
   - roxygen2
   - devtools
+  - factorstochvol
+  - tinytex
+
+before_install:
+  - chmod +x configure
+  - chmod +x cleanup
+
+r_build_args: --no-build-vignettes --no-manual --no-resave-data
+r_check_args: --ignore-vignettes --no-manual
+
 
 after_success:
   - tar -C .. -xf $PKG_TARBALL

DESCRIPTION

@@ -1,21 +1,38 @@
 Package: mfbvar
 Type: Package
 Title: Mixed-Frequency Bayesian VAR Models 
-Version: 0.5.0.9000
-Date: 2019-05-09
+Version: 0.5.6
+Date: 2021-02-09
 Authors@R: c(
     person("Sebastian", "Ankargren", email = "[email protected]", role = c("cre", "aut"), comment = c(ORCID = "0000-0003-4415-8734")),
-    person("Yukai", "Yang", email = "[email protected]", role = c("aut"), comment=c(ORCID="0000-0002-2623-8549")))
-Description: Estimation of mixed-frequency Bayesian vector autoregressive (VAR) models with Minnesota or steady-state priors. The package implements a state space-based VAR model that handles mixed frequencies of the data. The model is estimated using Markov Chain Monte Carlo to numerically approximate the posterior distribution, where the prior can be either the Minnesota prior, as used by Schorfheide and Song (2015) <doi:10.1080/07350015.2014.954707>, or the steady-state prior, as advocated by Ankargren, Unosson and Yang (2018) <http://uu.diva-portal.org/smash/get/diva2:1260262/FULLTEXT01.pdf>.
+    person("Yukai", "Yang", email = "[email protected]", role = c("aut"), comment=c(ORCID="0000-0002-2623-8549")),
+    person("Gregor", "Kastner", role = "ctb", comment = c(ORCID="0000-0002-8237-8271")))
+Description: Functions and tools for estimation of mixed-frequency Bayesian vector autoregressive (VAR) models. The package implements a state space-based VAR model that handles mixed frequencies of the data as proposed by Schorfheide and Song (2015) <doi:10.1080/07350015.2014.954707>, and extensions thereof developed by Ankargren, Unosson and Yang (2020) <doi:10.1515/jtse-2018-0034>, Ankargren and Joneus (2019) <arXiv:1912.02231>, and Ankargren and Joneus (2020) <doi:10.1016/j.ecosta.2020.05.007>. The models are estimated using Markov Chain Monte Carlo to numerically approximate the posterior distribution. Prior distributions that can be used include normal-inverse Wishart and normal-diffuse priors as well as steady-state priors. Stochastic volatility can be handled by common or factor stochastic volatility models.
 License: GPL-3
 LazyData: TRUE
 URL: https://github.com/ankargren/mfbvar
 BugReports: https://github.com/ankargren/mfbvar/issues
 Imports:
-    Rcpp (>= 0.12.7), ggplot2 (>= 2.2.1), methods, pbapply, utils, factorstochvol, progress, lubridate, GIGrvg
+    Rcpp (>= 0.12.7), 
+    ggplot2 (>= 3.3.0), 
+    methods, 
+    lubridate, 
+    GIGrvg, 
+    stochvol (>= 2.0.3), 
+    RcppParallel,
+    dplyr,
+    magrittr,
+    tibble, 
+    zoo
 LinkingTo:
-    Rcpp, RcppArmadillo, RcppProgress
-Depends: R (>= 2.10)
-Suggests: testthat, covr, tidyverse
-RoxygenNote: 6.1.1
+    Rcpp, 
+    RcppArmadillo, 
+    RcppProgress, 
+    stochvol (>= 2.0.3), 
+    RcppParallel
+Depends: R (>= 3.5.0)
+Suggests: testthat, covr, knitr, ggridges, alfred, factorstochvol
+RoxygenNote: 7.1.1
 Encoding: UTF-8
+SystemRequirements: GNU make
+VignetteBuilder: knitr

MFBVAR.Rproj

@@ -17,6 +17,8 @@ StripTrailingWhitespace: Yes
 
 BuildType: Package
 PackageUseDevtools: Yes
-PackageInstallArgs: --no-multiarch --with-keep.source --clean
-PackageCheckArgs: --as-cran --clean
+PackageInstallArgs: --no-multiarch --with-keep.source --clean --compact-vignettes=both
+PackageBuildArgs: --compact-vignettes=both
+PackageBuildBinaryArgs: --compact-vignettes=both
+PackageCheckArgs: --as-cran --clean --compact-vignettes=both
 PackageRoxygenize: rd,collate

NAMESPACE

@@ -1,34 +1,48 @@
 importFrom(Rcpp, evalCpp)
 useDynLib(mfbvar, .registration = TRUE)
 import(stats)
-importFrom("utils", "setTxtProgressBar", "txtProgressBar")
-importFrom("pbapply", "setTimerProgressBar", "timerProgressBar")
-importFrom("progress", "progress_bar")
 importFrom(magrittr,"%>%")
 importFrom(tibble,"tibble")
-importFrom(dplyr, "group_by", "summarize", "ungroup", "mutate", "transmute", "bind_rows")
-importFrom(lubridate, "%m-%", "%m+%", "days", "ymd", "quarter", "month", "year")
+importFrom(dplyr, "group_by", "summarize", "ungroup", "mutate", "transmute", "bind_rows", "pull")
+importFrom(lubridate, "%m-%", "%m+%", "days", "ymd", "quarter", "month", "year",
+           "as_date", "day", "days_in_month", "ceiling_date", "floor_date")
 import(ggplot2)
+importFrom(zoo, "as.Date", "zoo", "as.Date.ts", "index", "merge.zoo")
+importFrom(stochvol,svsample)
 S3method(print, mfbvar_prior)
 S3method(summary, mfbvar_prior)
-S3method(mdd, mfbvar_minn_iw)
-S3method(mdd, mfbvar_ss_iw)
-S3method(mcmc_sampler, mfbvar_minn_iw)
-S3method(mcmc_sampler, mfbvar_ss_iw)
 S3method(print, mfbvar)
 S3method(summary, mfbvar)
 S3method(plot, mfbvar_minn)
 S3method(plot, mfbvar_ss)
 S3method(plot, mfbvar_ssng)
+S3method(plot, mfbvar_dl)
 S3method(plot, mfbvar_prior)
+S3method(mcmc_sampler, mfbvar_minn_fsv)
+S3method(mcmc_sampler, mfbvar_dl_fsv)
+S3method(mcmc_sampler, mfbvar_ss_fsv)
+S3method(mcmc_sampler, mfbvar_ssng_fsv)
+S3method(mcmc_sampler, mfbvar_minn_diffuse)
+S3method(mcmc_sampler, mfbvar_dl_diffuse)
+S3method(mcmc_sampler, mfbvar_ss_diffuse)
+S3method(mcmc_sampler, mfbvar_ssng_diffuse)
+S3method(mcmc_sampler, mfbvar_minn_csv)
+S3method(mcmc_sampler, mfbvar_ss_csv)
+S3method(mcmc_sampler, mfbvar_ssng_csv)
+S3method(mcmc_sampler, mfbvar_minn_iw)
+S3method(mcmc_sampler, mfbvar_ss_iw)
+S3method(mcmc_sampler, mfbvar_ssng_iw)
+S3method(mdd, mfbvar_ss_iw)
+S3method(mdd, mfbvar_minn_iw)
 S3method(predict, mfbvar)
 S3method(predict, sfbvar)
 importFrom("methods", "hasArg")
+export(mdd)
 export(set_prior)
 export(update_prior)
 export(estimate_mfbvar)
 export(interval_to_moments)
-export(mdd)
-export(mcmc_sampler)
 export(varplot)
 importFrom(GIGrvg,rgig)
+importFrom(RcppParallel, RcppParallelLibs)
+

NEWS.md

@@ -0,0 +1,23 @@
+# mfbvar 0.5.6 (2021-02-03)
+* Removed use of internet connection in vignette
+* Enabled use of weekly-monthly frequency mix
+
+# mfbvar 0.5.4 (2020-05-14)
+* Changes to the main interface. Data can (and should) now be given as a list of `zooreg` or `ts` objects.
+
+# mfbvar 0.5.3 (2020-03-18)
+* Fixed a bug caused by the plotting functions
+
+# mfbvar 0.5.1 (2019-08-16)
+* Support for more priors
+* Stochastic volatility models
+* Better `predict` functions
+* Faster implementations
+* Some support for quarterly/monthly (i.e. single-frequency) models
+* Vignette added
+
+# TODO
+* Impulse responses
+* Marginal data densities for more specifications (currently `minn`-`iw` with `average` only), and in C++
+* Enable use of less lags than what the aggregations need
+