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| author | TAKATSU Tomonari <tota@FreeBSD.org> | 2012-02-11 14:59:28 +0000 |
|---|---|---|
| committer | TAKATSU Tomonari <tota@FreeBSD.org> | 2012-02-11 14:59:28 +0000 |
| commit | 9e8084ffbd4077ca06dc78d3ffb5e63407792a2b (patch) | |
| tree | cfe2617bff84bcf86c8c45bc61c9ff9418f1bf3d /math/R-cran-KFAS | |
| parent | f8e89f0fd2ecb9f1bd133eb514f0fad5c4d0a111 (diff) | |
| download | ports-9e8084ffbd4077ca06dc78d3ffb5e63407792a2b.tar.gz ports-9e8084ffbd4077ca06dc78d3ffb5e63407792a2b.zip | |
Notes
Diffstat (limited to 'math/R-cran-KFAS')
| -rw-r--r-- | math/R-cran-KFAS/Makefile | 22 | ||||
| -rw-r--r-- | math/R-cran-KFAS/distinfo | 2 | ||||
| -rw-r--r-- | math/R-cran-KFAS/pkg-descr | 12 |
3 files changed, 36 insertions, 0 deletions
diff --git a/math/R-cran-KFAS/Makefile b/math/R-cran-KFAS/Makefile new file mode 100644 index 000000000000..5df41a70d277 --- /dev/null +++ b/math/R-cran-KFAS/Makefile @@ -0,0 +1,22 @@ +# New ports collection makefile for: R-cran-KFAS +# Date created: 2012-01-04 +# Whom: TAKATSU Tomonari <tota@FreeBSD.org> +# +# $FreeBSD$ +# + +PORTNAME= KFAS +PORTVERSION= 0.6.1 +CATEGORIES= math +DISTNAME= ${PORTNAME}_${PORTVERSION} + +MAINTAINER= tota@FreeBSD.org +COMMENT= Kalman filter and smoothers for exponential family state space models + +LICENSE= GPLv2 GPLv3 +LICENSE_COMB= dual + +USE_R_MOD= yes +R_MOD_AUTOPLIST= yes + +.include <bsd.port.mk> diff --git a/math/R-cran-KFAS/distinfo b/math/R-cran-KFAS/distinfo new file mode 100644 index 000000000000..e15ff0cee6b6 --- /dev/null +++ b/math/R-cran-KFAS/distinfo @@ -0,0 +1,2 @@ +SHA256 (KFAS_0.6.1.tar.gz) = 77e013bdc49be81a20abfc29d37ef9a7066188247ce279f26437921b4687bb15 +SIZE (KFAS_0.6.1.tar.gz) = 24981 diff --git a/math/R-cran-KFAS/pkg-descr b/math/R-cran-KFAS/pkg-descr new file mode 100644 index 000000000000..733201203c17 --- /dev/null +++ b/math/R-cran-KFAS/pkg-descr @@ -0,0 +1,12 @@ +Package KFAS provides funchtions for Kalman filtering, state, +disturbance and simulation smoothing, forecasting and simulation +of state space models. All functions can use exact diffuse +initialisation when distributions of some or all elements of initial +state vector are unknown. Filtering, state smoothing and simulation +functions use sequential processing algorithm, which is faster than +standard approach, and it also allows singularity of prediction +error variance matrix. KFAS also contains function for computing +the likelihood of exponential family state space models and function +for state smoothing of exponential family state space models. + +WWW: http://cran.r-project.org/web/packages/KFAS/ |