freebsd-ports/math/R-cran-KFAS/Makefile

# Created by: TAKATSU Tomonari <tota@FreeBSD.org>
# $FreeBSD$

PORTNAME=	KFAS
PORTVERSION=	1.2.4
PORTREVISION=	1
CATEGORIES=	math
DISTNAME=	${PORTNAME}_${PORTVERSION}

MAINTAINER=	tota@FreeBSD.org
COMMENT=	Kalman filter and smoothers for exponential family state space models

LICENSE=	GPLv2+

USES=	cran:auto-plist,fortran

.include <bsd.port.mk>
update the R base ports to 2.15.2 and adjust dependent ports Reviewed by: thierry, tota, wen 2013-01-04 18:51:39 +01:00			`# Created by: TAKATSU Tomonari <tota@FreeBSD.org>`
- Add a new port: math/R-cran-KFAS 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/ 2012-02-11 15:59:28 +01:00			`# $FreeBSD$`

			`PORTNAME= KFAS`
- Update to 1.2.4 2016-07-30 10:15:11 +02:00			`PORTVERSION= 1.2.4`
Bump PORTREVISIONS for ports depending on the canonical version of GCC and lang/gcc which have moved from GCC 4.8.5 to GCC 4.9.4 (at least under some circumstances such as versions of FreeBSD or platforms), part II. The first part covered ports with USE_GCC=yes, USE_GCC=any, or one of gcc-c++11-lib, openmp, nestedfct, c++11-lib as well as c++14-lang, c++11-lang, c++0x, c11 requested via USES=compiler. This adds ports with USES=fortran and ports using Mk/bsd.octave.mk which in turn has USES=fortran. PR: 214965 Reported by: thierry 2016-12-07 14:24:56 +01:00			`PORTREVISION= 1`
- Add a new port: math/R-cran-KFAS 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/ 2012-02-11 15:59:28 +01:00			`CATEGORIES= math`
			`DISTNAME= ${PORTNAME}_${PORTVERSION}`

			`MAINTAINER= tota@FreeBSD.org`
			`COMMENT= Kalman filter and smoothers for exponential family state space models`

- Update to 1.2.0 - Fix LICENSE 2016-02-03 04:07:05 +01:00			`LICENSE= GPLv2+`
- Add a new port: math/R-cran-KFAS 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/ 2012-02-11 15:59:28 +01:00
- Update to 1.2.3 2016-07-18 14:05:52 +02:00			`USES= cran:auto-plist,fortran`
- Add a new port: math/R-cran-KFAS 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/ 2012-02-11 15:59:28 +01:00
			`.include <bsd.port.mk>`