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Merged
SebKrantz merged 2 commits into from
Sep 24, 2025
Merged

Development #76

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678b4fb
RcppArmadillo fix.
SebKrantz Sep 24, 2025
bdefbe5
RcppArmadillo 15 compatibility.
SebKrantz Sep 24, 2025
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4 changes: 2 additions & 2 deletions DESCRIPTION
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
Package: dfms
Version: 0.3.1
Version: 0.3.2
Title: Dynamic Factor Models
Authors@R: c(person("Sebastian", "Krantz", role = c("aut", "cre"), email = "sebastian.krantz@graduateinstitute.ch"),
person("Rytis", "Bagdziunas", role = "aut"),
Expand All @@ -26,6 +26,6 @@ License: GPL-3
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE, roclets = c ("namespace", "rd", "srr::srr_stats_roclet"))
RoxygenNote: 7.2.3
RoxygenNote: 7.3.2
Config/testthat/edition: 3
VignetteBuilder: knitr
4 changes: 4 additions & 0 deletions NEWS.md
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@@ -1,3 +1,7 @@
# dfms 0.3.2

* Minor internal C++ changes to ensure compatibility with *RcppArmadillo* 15.0.2.

# dfms 0.3.1

* Fixed bug which occurred with only one quarterly variable (#73). Thanks @SantiagoD999 for reporting.
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1 change: 1 addition & 0 deletions dfms.Rproj
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@@ -1,4 +1,5 @@
Version: 1.0
ProjectId: cf7def2c-03c5-48a1-9b32-b6eaced333b3

RestoreWorkspace: No
SaveWorkspace: No
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24 changes: 24 additions & 0 deletions man/dfms-package.Rd
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4 changes: 2 additions & 2 deletions src/KalmanFiltering.cpp
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Expand Up @@ -86,7 +86,7 @@ Rcpp::List SKF(arma::mat X, arma::mat A, arma::mat C, arma::mat Q,
// Compute likelihood. Skip this part if S is not positive definite.
if(retLL) {
detS = det(S);
if(detS > 0) loglik += log(detS) - conv_to<double>::from(et.t() * S * et) - dn;
if(detS > 0) loglik += log(detS) - as_scalar(et.t() * S * et) - dn;
}

} else { // If all missing: just prediction.
Expand Down Expand Up @@ -259,7 +259,7 @@ Rcpp::List SKFS(arma::mat X, arma::mat A, arma::mat C, arma::mat Q,
// Compute likelihood. Skip this part if S is not positive definite.
if(retLL) {
detS = det(S);
if(detS > 0) loglik += log(detS) - conv_to<double>::from(et.t() * S * et) - dn; // Standard Kalman Filter Likelihood
if(detS > 0) loglik += log(detS) - as_scalar(et.t() * S * et) - dn; // Standard Kalman Filter Likelihood
}

} else { // If all missing: just prediction.
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