head	1.6;
access;
symbols
	pkgsrc-2026Q1:1.6.0.10
	pkgsrc-2026Q1-base:1.6
	pkgsrc-2025Q4:1.6.0.8
	pkgsrc-2025Q4-base:1.6
	pkgsrc-2025Q3:1.6.0.6
	pkgsrc-2025Q3-base:1.6
	pkgsrc-2025Q2:1.6.0.4
	pkgsrc-2025Q2-base:1.6
	pkgsrc-2025Q1:1.6.0.2
	pkgsrc-2025Q1-base:1.6
	pkgsrc-2024Q4:1.5.0.14
	pkgsrc-2024Q4-base:1.5
	pkgsrc-2024Q3:1.5.0.12
	pkgsrc-2024Q3-base:1.5
	pkgsrc-2024Q2:1.5.0.10
	pkgsrc-2024Q2-base:1.5
	pkgsrc-2024Q1:1.5.0.8
	pkgsrc-2024Q1-base:1.5
	pkgsrc-2023Q4:1.5.0.6
	pkgsrc-2023Q4-base:1.5
	pkgsrc-2023Q3:1.5.0.4
	pkgsrc-2023Q3-base:1.5
	pkgsrc-2023Q2:1.5.0.2
	pkgsrc-2023Q2-base:1.5
	pkgsrc-2023Q1:1.4.0.12
	pkgsrc-2023Q1-base:1.4
	pkgsrc-2022Q4:1.4.0.10
	pkgsrc-2022Q4-base:1.4
	pkgsrc-2022Q3:1.4.0.8
	pkgsrc-2022Q3-base:1.4
	pkgsrc-2022Q2:1.4.0.6
	pkgsrc-2022Q2-base:1.4
	pkgsrc-2022Q1:1.4.0.4
	pkgsrc-2022Q1-base:1.4
	pkgsrc-2021Q4:1.4.0.2
	pkgsrc-2021Q4-base:1.4
	pkgsrc-2021Q3:1.2.0.14
	pkgsrc-2021Q3-base:1.2
	pkgsrc-2021Q2:1.2.0.12
	pkgsrc-2021Q2-base:1.2
	pkgsrc-2021Q1:1.2.0.10
	pkgsrc-2021Q1-base:1.2
	pkgsrc-2020Q4:1.2.0.8
	pkgsrc-2020Q4-base:1.2
	pkgsrc-2020Q3:1.2.0.6
	pkgsrc-2020Q3-base:1.2
	pkgsrc-2020Q2:1.2.0.4
	pkgsrc-2020Q2-base:1.2
	pkgsrc-2020Q1:1.2.0.2
	pkgsrc-2020Q1-base:1.2
	pkgsrc-2019Q4:1.1.0.6
	pkgsrc-2019Q4-base:1.1
	pkgsrc-2019Q3:1.1.0.2
	pkgsrc-2019Q3-base:1.1;
locks; strict;
comment	@# @;


1.6
date	2025.01.04.14.38.30;	author mef;	state Exp;
branches;
next	1.5;
commitid	8xbfD4I9zB422bEF;

1.5
date	2023.06.12.15.33.04;	author mef;	state Exp;
branches;
next	1.4;
commitid	q20ZURi7eBZGIFsE;

1.4
date	2021.10.26.10.26.00;	author nia;	state Exp;
branches;
next	1.3;
commitid	Sx37QeYJ6gZ27jeD;

1.3
date	2021.10.07.13.53.49;	author nia;	state Exp;
branches;
next	1.2;
commitid	ZW512wDymtKhSSbD;

1.2
date	2020.02.15.23.53.07;	author mef;	state Exp;
branches;
next	1.1;
commitid	EJlK81cptpRktPWB;

1.1
date	2019.08.09.15.39.04;	author brook;	state Exp;
branches;
next	;
commitid	8tE6Bgb79K4sQmyB;


desc
@@


1.6
log
@(finance/R-bayesm) Updated 3.1.5 to 3.1.6, NEWS.md unknown, make test passed
@
text
@$NetBSD: distinfo,v 1.5 2023/06/12 15:33:04 mef Exp $

BLAKE2s (R/bayesm_3.1-6.tar.gz) = 827a7068a8b69af8e57e989949e5ec0d16ec0f7ab76db371d60bac95db4bca89
SHA512 (R/bayesm_3.1-6.tar.gz) = d953b44153bec423554e08562aca8f49cbb361a0b2931d555a611dc510b232a452107c6573dd17047bb2eaf10d81dd53689ec8fe444dc0c22d66bb00ba919734
Size (R/bayesm_3.1-6.tar.gz) = 2267618 bytes
@


1.5
log
@(finance/R-bayesm) Updated 3.1.4 to 3.1.5, NEWS.md unknown
@
text
@d1 1
a1 1
$NetBSD: distinfo,v 1.4 2021/10/26 10:26:00 nia Exp $
d3 3
a5 3
BLAKE2s (R/bayesm_3.1-5.tar.gz) = b2c0ecec5d10a4718ad25dbc9e0c10ce26a95b4ef8147959cb7acac652c066df
SHA512 (R/bayesm_3.1-5.tar.gz) = 95ced9c2d4f549c1027c074f65530d9d0c1b3779e2ec9287436f44513825a9fa2d8d1984e74ab155a65dc63412fd9b6b16ddb1238eac57cc20e50166c92796c0
Size (R/bayesm_3.1-5.tar.gz) = 2268160 bytes
@


1.4
log
@finance: Replace RMD160 checksums with BLAKE2s checksums

All checksums have been double-checked against existing RMD160 and
SHA512 hashes
@
text
@d1 1
a1 1
$NetBSD: distinfo,v 1.3 2021/10/07 13:53:49 nia Exp $
d3 3
a5 3
BLAKE2s (R/bayesm_3.1-4.tar.gz) = c4ab7db7909e5f57d1331611e15a24049cb7259c26c9e4becbe660f6a17ed8c9
SHA512 (R/bayesm_3.1-4.tar.gz) = 1fe81650ff141e3023d197a659e782bab61b33efcfaf398237e725e17ad571f50bbd24eb7d9e25d83d110c1d438405ebf3a731cd18e3638b486ee69ee65528e3
Size (R/bayesm_3.1-4.tar.gz) = 2269364 bytes
@


1.3
log
@finance: Remove SHA1 hashes for distfiles
@
text
@d1 1
a1 1
$NetBSD: distinfo,v 1.2 2020/02/15 23:53:07 mef Exp $
d3 1
a3 1
RMD160 (R/bayesm_3.1-4.tar.gz) = 9970fb91f10de9ec74cc04d69970143a476946f7
@


1.2
log
@(finance/R-bayesm) Updated to 3.1.4, ChangeLog not known, or not easily found,sorry
@
text
@d1 1
a1 1
$NetBSD: distinfo,v 1.1 2019/08/09 15:39:04 brook Exp $
a2 1
SHA1 (R/bayesm_3.1-4.tar.gz) = 27b76738cc656a33d28550544f16a29cf480064a
@


1.1
log
@R-bayesm: initial commit

Covers many important models used in marketing and micro-econometrics
applications. The package includes: Bayes Regression (univariate or
multivariate dep var), Bayes Seemingly Unrelated Regression (SUR),
Binary and Ordinal Probit, Multinomial Logit (MNL) and Multinomial
Probit (MNP), Multivariate Probit, Negative Binomial (Poisson)
Regression, Multivariate Mixtures of Normals (including clustering),
Dirichlet Process Prior Density Estimation with normal base,
Hierarchical Linear Models with normal prior and covariates,
Hierarchical Linear Models with a mixture of normals prior and
covariates, Hierarchical Multinomial Logits with a mixture of normals
prior and covariates, Hierarchical Multinomial Logits with a Dirichlet
Process prior and covariates, Hierarchical Negative Binomial
Regression Models, Bayesian analysis of choice-based conjoint data,
Bayesian treatment of linear instrumental variables models, Analysis
of Multivariate Ordinal survey data with scale usage heterogeneity (as
in Rossi et al, JASA (01)), Bayesian Analysis of Aggregate Random
Coefficient Logit Models as in BLP (see Jiang, Manchanda, Rossi 2009)
For further reference, consult our book, Bayesian Statistics and
Marketing by Rossi, Allenby and McCulloch (Wiley 2005) and Bayesian
Non- and Semi-Parametric Methods and Applications (Princeton U Press
2014).
@
text
@d1 1
a1 1
$NetBSD$
d3 4
a6 4
SHA1 (R/bayesm_3.1-3.tar.gz) = 98e60c5c3f81139d22de27fcbe06172bdfba10b5
RMD160 (R/bayesm_3.1-3.tar.gz) = f8288e802fa3dfe4e9d8a6d731469ff8ae5d0c96
SHA512 (R/bayesm_3.1-3.tar.gz) = c55892113ae6af3b9c626870b7de30f90c81dff446dc997da1b938a69c76960e71f25f86e62614125ab2709ce22c953013f8963a2b93afe7db08a56163f7c21d
Size (R/bayesm_3.1-3.tar.gz) = 2269029 bytes
@