[r-cran-vgam] 41/63: Import Upstream version 0.9-6
Andreas Tille
tille at debian.org
Tue Jan 24 13:54:37 UTC 2017
This is an automated email from the git hooks/post-receive script.
tille pushed a commit to branch master
in repository r-cran-vgam.
commit 2d2ec7575787fa685fa059017558e011b3126efc
Author: Andreas Tille <tille at debian.org>
Date: Tue Jan 24 14:17:01 2017 +0100
Import Upstream version 0.9-6
---
DESCRIPTION | 35 ++++++------
MD5 | 124 +++++++++++++++++++++----------------------
NEWS | 15 ++++++
R/aamethods.q | 4 +-
R/cqo.fit.q | 6 +--
R/family.aunivariate.R | 11 ++--
R/family.binomial.R | 40 ++++++++------
R/family.categorical.R | 1 -
R/family.censored.R | 5 +-
R/family.circular.R | 15 +++---
R/family.functions.R | 2 +-
R/family.math.R | 6 +--
R/family.normal.R | 18 ++++---
R/family.rrr.R | 70 ++++++++++++------------
R/family.univariate.R | 27 +++++++---
R/family.zeroinf.R | 1 +
R/predict.vlm.q | 2 +-
R/print.vlm.q | 12 ++---
R/rrvglm.R | 2 +-
R/rrvglm.fit.q | 2 +-
R/s.vam.q | 2 +-
R/summary.vlm.q | 10 ++--
R/vgam.R | 2 +-
R/vglm.R | 2 +-
R/vglm.control.q | 2 +-
R/vglm.fit.q | 2 +-
R/vlm.R | 6 +--
R/vlm.wfit.q | 18 +++----
inst/doc/categoricalVGAM.pdf | Bin 735446 -> 734870 bytes
man/ParetoUC.Rd | 6 ++-
man/SURff.Rd | 8 ++-
man/bifgmcopUC.Rd | 6 ++-
man/bifrankcopUC.Rd | 5 +-
man/binom2.orUC.Rd | 5 +-
man/binom2.rhoUC.Rd | 7 ++-
man/biplackettcopUC.Rd | 5 +-
man/cardUC.Rd | 6 ++-
man/frechet.Rd | 8 ++-
man/gengammaUC.Rd | 6 ++-
man/gompertzUC.Rd | 6 ++-
man/gumbelIIUC.Rd | 6 ++-
man/lgammaUC.Rd | 6 ++-
man/lindUC.Rd | 1 +
man/linoUC.Rd | 5 +-
man/logUC.Rd | 6 ++-
man/makehamUC.Rd | 6 ++-
man/nakagamiUC.Rd | 8 ++-
man/paretoIVUC.Rd | 6 ++-
man/perksUC.Rd | 5 +-
man/riceUC.Rd | 2 +-
man/rrvglm-class.Rd | 2 +-
man/s.Rd | 15 +++---
man/slashUC.Rd | 2 +
man/tikuvUC.Rd | 5 +-
man/vgam-class.Rd | 8 ++-
man/vglm-class.Rd | 2 +-
man/vglm.Rd | 2 +-
man/yulesimonUC.Rd | 2 +-
man/zibinomUC.Rd | 2 +-
man/zigeomUC.Rd | 2 +-
man/zinegbinUC.Rd | 2 +-
src/vdigami.f | 15 ++++--
src/vgam3.c | 5 +-
63 files changed, 381 insertions(+), 244 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index bd634e0..5d4c6e8 100755
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,30 +1,31 @@
Package: VGAM
-Version: 0.9-5
-Date: 2014-11-06
+Version: 0.9-6
+Date: 2014-12-08
Title: Vector Generalized Linear and Additive Models
Author: Thomas W. Yee <t.yee at auckland.ac.nz>
Maintainer: Thomas Yee <t.yee at auckland.ac.nz>
Depends: R (>= 3.0.0), methods, stats, stats4, splines
Suggests: VGAMdata, MASS
-Description: This package fits many (150+) models and
- distributions by maximum likelihood estimation (MLE)
- or penalized MLE, using Fisher scoring. This is done
- via the vector generalized linear and additive model
- (VGLM/VGAM) classes and associated models (reduced-rank
- VGLMs, quadratic RR-VGLMs, reduced-rank VGAMs, row-column
- interaction models). These are abbreviated RR-VGLMs,
- QRR-VGLMs, RR-VGAMs and RCIMs. These include constrained
- and unconstrained quadratic ordination (CQO/UQO) models
- in ecology as well as constrained additive ordination
- (CAO). Note that these functions are subject to change,
- especially before version 1.0.0 is released; see the NEWS
- file for latest changes.
+Description: An implementation of about 6 major classes of
+ statistical regression models. At the heart of it are the
+ vector generalized linear and additive model (VGLM/VGAM)
+ classes. Many (150+) models and distributions are estimated
+ by maximum likelihood estimation (MLE) or penalized MLE, using
+ Fisher scoring. VGLMs can be loosely thought of as multivariate
+ GLMs. VGAMs are data-driven VGLMs (i.e., with smoothing). The
+ other classes are RR-VGLMs (reduced-rank VGLMs), quadratic
+ RR-VGLMs, reduced-rank VGAMs, RCIMs (row-column interaction
+ models)---these classes perform constrained and unconstrained
+ quadratic ordination (CQO/UQO) models in ecology, as well
+ as constrained additive ordination (CAO). Note that these
+ functions are subject to change, especially before version
+ 1.0.0 is released; see the NEWS file for latest changes.
License: GPL-2
URL: http://www.stat.auckland.ac.nz/~yee/VGAM
NeedsCompilation: yes
BuildVignettes: yes
LazyLoad: yes
LazyData: yes
-Packaged: 2014-11-05 19:32:04 UTC; tyee001
+Packaged: 2014-12-08 03:56:18 UTC; tyee001
Repository: CRAN
-Date/Publication: 2014-11-06 01:01:45
+Date/Publication: 2014-12-08 08:23:11
diff --git a/MD5 b/MD5
index 5dd4448..f0a3e12 100644
--- a/MD5
+++ b/MD5
@@ -1,9 +1,9 @@
66414b6ed296192426033f4ac29a6af2 *BUGS
-433bd389fd4d3ea7c7a8edb43164404a *DESCRIPTION
+56b5a065696061695eb04927c166cce3 *DESCRIPTION
80e495256dd8946f3468ec738fc98bc6 *NAMESPACE
-bb948f5823a8d0fc9bc8ac61663d207a *NEWS
+3a21077264e7f4a39e7aad9f086afd11 *NEWS
21f682d56c4fc9327d48e2179788422e *R/Links.R
-c99f9c8830068a399945b4dbcd19db8e *R/aamethods.q
+50817106214d06314a10a612ab1fc95a *R/aamethods.q
a0675022e96fd5fb7fddac8f0a57fd30 *R/add1.vglm.q
b2b2be3f5bab46f3400b3e0448dd1e37 *R/attrassign.R
c6e91684ada0ba6cadd66223b65ffb88 *R/bAIC.q
@@ -13,27 +13,27 @@ c6e91684ada0ba6cadd66223b65ffb88 *R/bAIC.q
25144d1bbfed30056f51b9c1583e7876 *R/cao.fit.q
4dc53e3eae827b9729fb799d6d8b3d7a *R/coef.vlm.q
c8c22372a0a69c23aeb94fddab38428e *R/cqo.R
-fbdb96d0b2e55debaf13fd2772ea6e78 *R/cqo.fit.q
+13a22d60defef24b2a813cde2323f321 *R/cqo.fit.q
4efd72fef4a0ae5b14bd8a05377b14f7 *R/deviance.vlm.q
3d38c5950f4f9e52085e9ba782feeb90 *R/effects.vglm.q
f7c7f03bc4324676171f90c5635b2ad1 *R/family.actuary.R
-f7ae3a664396d372c36c9291d1349238 *R/family.aunivariate.R
+ef2622e509ead5e9e7e2c6fa962dd35e *R/family.aunivariate.R
968e7fe2052ece7ad00a01b2b13270bf *R/family.basics.R
-d83ff1a3505017d355b06bf1ffd14d16 *R/family.binomial.R
+96d7aa9d86b2877fbc25c71762ec9e67 *R/family.binomial.R
15c543df3e3f471cd6a944827b9977d6 *R/family.bivariate.R
-f41eb715c5b57c1fb662416db358808a *R/family.categorical.R
-380d5e67774cb7fe9751119c27e6510b *R/family.censored.R
-1f475904b76686075b7022bb972a9464 *R/family.circular.R
+69d07ebc28871093dc5afa23679c5f8d *R/family.categorical.R
+49a734273e6984063a90345afa6baa76 *R/family.censored.R
+70190886f33094f971e04d36895f80e6 *R/family.circular.R
d46827c37e9fb9ba5d17afb1b4bb8359 *R/family.exp.R
c85261079319b92e74b49c310d73c9aa *R/family.extremes.R
-a18a6ff13edf752a0c48498c559a16be *R/family.functions.R
+216cab69ae6b0960bdefcedebf260217 *R/family.functions.R
86505922fc0f9dfba086820523c6aef1 *R/family.genetic.R
70345321a3bf8553aed029b758fef4e9 *R/family.glmgam.R
cf4a5403b58f1f1f8ff459488f5b0843 *R/family.loglin.R
-f10cb3d2544ed342015c9a7ed74191b2 *R/family.math.R
+93eb79120f4e952cf7b378a83267d010 *R/family.math.R
0de0891b2e1603fd5eed723f3984bc50 *R/family.mixture.R
7a559228e6eddad64acd3f662a579b74 *R/family.nonlinear.R
-94d97559eb73c68676ef5cc6cda108d8 *R/family.normal.R
+2f417428f0e564d8c90b45dc2887c740 *R/family.normal.R
b7e285f97e1c7aa401b71485a639ec4b *R/family.others.R
e765d07abcd78f51028896740e4cb33b *R/family.positive.R
fc390b75cf9c8f6c882b905968d2d645 *R/family.qreg.R
@@ -41,13 +41,13 @@ c05609977f1289db83e83a11f19302d4 *R/family.quantal.R
3d2ce925abe6b8dcd367759b115d7486 *R/family.rcim.R
b333e00eb110446c92c2edad114072fd *R/family.rcqo.R
80d6bdca76c8a853483497dc403e971a *R/family.robust.R
-d0167179094f7ec535370d2d88d01adf *R/family.rrr.R
+95dc68427644418097fba1b3f2fa588c *R/family.rrr.R
6aaa42bed6ddb4f4429fac5d4ffbf7d9 *R/family.sur.R
76b17657c66fa8405996ccdeeb20d82c *R/family.survival.R
b32cbe9457bc213c65a6e937e2892b01 *R/family.ts.R
-d6c62ada6a6960782bb7da092c1d5188 *R/family.univariate.R
+d691f008e7de2d0a162aa5b233e5b757 *R/family.univariate.R
23e25d09aed81048919b15e35d2a3fdf *R/family.vglm.R
-46ceb5189af72b4644c08d872eb5bfeb *R/family.zeroinf.R
+89df0901db87c9c811fa7686b633cf8b *R/family.zeroinf.R
e77bfa0f3a2e6a802a308611f84b75c1 *R/fittedvlm.R
e0f39e9a543e616c179f53c153ada17b *R/formula.vlm.q
66dceb0aa1906a6358ddf5840e5c3a10 *R/generic.q
@@ -60,32 +60,32 @@ a111fc4dd1dbd7280c07009277eec304 *R/logLik.vlm.q
f3eeccb2f0f1740637f48308484ddf80 *R/plot.vglm.q
d3050d098e945c23a2617410e4698b9a *R/predict.vgam.q
b503ba5f6eb50d39b773c79d7e57a2d8 *R/predict.vglm.q
-1685c6757a7ddf887d842bfdcf66bff9 *R/predict.vlm.q
+b0473f38d6b4fb6358ea912ae739777b *R/predict.vlm.q
6b6c07d978349b4c2dd82d1e3768f763 *R/print.vglm.q
-2a6435e29721cdb571796c0f2b2ba2f4 *R/print.vlm.q
+77e6d91384fb55e09abdabfc0648084d *R/print.vlm.q
6b18d42adf25ab762686f45111fc9908 *R/qrrvglm.control.q
13edf9b27deeec4720ce3c63805c0826 *R/qtplot.q
cd95e96c163efedcf7dc452b5b9b80aa *R/residuals.vlm.q
-26fdc28282fb9f20f338f878e2078edb *R/rrvglm.R
+2343981c7ca4d3f31b09234ee76de9aa *R/rrvglm.R
ee1d3fe8e9731d47aab09577d16f296d *R/rrvglm.control.q
-7c9d06ff0cd0f5c02ae0f2d7e96f9ed6 *R/rrvglm.fit.q
+fcd355968577b16ae0572413f3126232 *R/rrvglm.fit.q
4d6331d1b7f7e9f49511ac68ed7ab830 *R/s.q
-8818a393944e9aacf3ca58907a9e0b8a *R/s.vam.q
+e692982477e8969b40987bef7739c508 *R/s.vam.q
400c72e71f4820b58f292523029c6245 *R/simulate.vglm.R
277ba59aa1a252dbfb97c6ca24e95b66 *R/smart.R
40b65c32c98ed7fe00459f089182209f *R/step.vglm.q
df48678099b0d4b643d22d0a25adc5f1 *R/summary.vgam.q
02d08e22bbacafdecfb36cf581a04ccb *R/summary.vglm.q
-da2b6c528168d9b72212223ffa9d151a *R/summary.vlm.q
-7053fc5a348fa10962cf86faa0cd6be5 *R/vgam.R
+b27c8d54a7efff12d6e6940459ddf2d7 *R/summary.vlm.q
+4d61781fd7a1619eeae76a619599c247 *R/vgam.R
aee3a2ac9b9b36985e8f8c3d15709590 *R/vgam.control.q
2aa25abd6c64976065b527635b5ce52a *R/vgam.fit.q
58bb89dc29480d468782ac4e410716de *R/vgam.match.q
-fd0eeed4746bd415316290c34d907f2d *R/vglm.R
-fa14b37834baabbffd171057e27a607c *R/vglm.control.q
-046141a6bfaa4a1787a3a22f9faab14f *R/vglm.fit.q
-0d279e4ac54a18c3b86931b97b9cb686 *R/vlm.R
-19455ed547e314ec5996c798587d2442 *R/vlm.wfit.q
+546998232ec7d26de97f27780b611a81 *R/vglm.R
+da6d3cf2bc2312861b94e3c7c630cd47 *R/vglm.control.q
+35e660f31d1d739eb5b8f62fd178024e *R/vglm.fit.q
+6625b38a35a6485139fed10b5e2e3a90 *R/vlm.R
+090851ac7b3e8f536ba4626140dba2f0 *R/vlm.wfit.q
128ebb1abdd41656f5662b00a16600cc *R/vsmooth.spline.q
fccfbabb1be99d6b15eb5e5449d1b66e *build/vignette.rds
2cdabbff91d4f47a58705b2fff199298 *data/Huggins89.t1.rda
@@ -148,7 +148,7 @@ ab8081763fe2144558be25f3a154327b *demo/vgam.R
60616e1e78fe61c1fd4acdf0d3129747 *inst/CITATION
4ff0e35d38b3c5bb38f1f7232b9af863 *inst/doc/categoricalVGAM.R
bfa11dbdbff271fb20342560f2bacd53 *inst/doc/categoricalVGAM.Rnw
-832bec013a1fc295ca49f4d927f35d21 *inst/doc/categoricalVGAM.pdf
+009bdce7afc060ca4590c33e0eeddc8a *inst/doc/categoricalVGAM.pdf
5ecb530e834d36b923e5167e587e5301 *man/A1A2A3.Rd
c0d1e33c2b490cfa5d2bfcf15d8df7b4 *man/AA.Aa.aa.Rd
26a120083d1d9d77ac0a5193d0c186b9 *man/AB.Ab.aB.ab.Rd
@@ -168,10 +168,10 @@ ce79d0626711d299c9c0cc2efab3abac *man/Inv.gaussian.Rd
e53a7b5f977320e9a2b3cfba16e097ee *man/MNSs.Rd
5ddd860d2b28b025dbf94b80062e3fc6 *man/Max.Rd
00dce9ac476270fc8ce02ea1e75de191 *man/Opt.Rd
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9012ad8444a0b750e3155cd43d8965bc *man/QvarUC.Rd
bd689bfc27028aea403c93863cf2e207 *man/Rcim.Rd
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685985b08b4668ae66206e9d72170b45 *man/Select.Rd
20a760cb2a7468d974d2de5c88d870e3 *man/SurvS4-class.Rd
6ed5239b716d4aaef069b66f248503f0 *man/SurvS4.Rd
@@ -207,25 +207,25 @@ f41bc1b37620bca37ba4d2f16fdae05d *man/biamhcop.Rd
003ba5eb60e8e27f6c9a022ae1e336d1 *man/biclaytoncop.Rd
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57536bc44454e58eb293b928919c92ca *man/bifgmexp.Rd
5e0bc6b73af5b7a56805a2f7600a439d *man/bifrankcop.Rd
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3996c974a214c0d706d20d820a9a1fa0 *man/bigamma.mckay.Rd
7a1c045834b0bd9de92a4aa97f52ab3c *man/bigumbelIexp.Rd
ffcbfc72f334094f6dfd4842ab522e96 *man/bilogisUC.Rd
cd241d3985e2b0dcf817f19417406596 *man/bilogistic.Rd
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a8cc7cbfa4c21672956a187c4ffba22d *man/binom2.rho.Rd
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53f8bc3da41aabe202d80304f2f84b63 *man/binormal.Rd
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9758ba4618c9c24caafec486b01238f5 *man/binormcopUC.Rd
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4b35070bbd74b15afd585110514a55f7 *man/bisa.Rd
9901ef6bbaed14ee55eda08dc810867e *man/bisaUC.Rd
@@ -243,7 +243,7 @@ b121ffb4e604644ef7082d777b4411df *man/calibrate.Rd
22e9a881f2f077f7e01e1dde9043dc7d *man/calibrate.qrrvglm.control.Rd
8a71703f9846bdda282e59f67832e941 *man/cao.Rd
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-af70e01bb01bebbc1d06e309d8ec6ba5 *man/cardUC.Rd
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7d96d29fad17cf0d10564c04f00c3ecb *man/cardioid.Rd
f4674b1787a58c87fbabdb369dc8a1ca *man/cauchit.Rd
d361f0253fb328f70a716c09fd597fdc *man/cauchy.Rd
@@ -308,7 +308,7 @@ c75d3ae0a8669fed4a71f54b8be64266 *man/fittedvlm.Rd
742b72298fd6b2ca944812681ad625a6 *man/flourbeetle.Rd
cd73efab4c3e718d1a77a603eb5e341c *man/foldnormUC.Rd
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537fb4f91167bddf5e76d732b9c4ad38 *man/frechetUC.Rd
cad07bc11ec21b13ecdbc3b93ec8efc0 *man/freund61.Rd
17c995a0692e2f600397ade32fcd6399 *man/fsqrt.Rd
@@ -320,7 +320,7 @@ edd2c4cefb99138667d2528f3d878bad *man/garma.Rd
e0fdd50e95e43075ac79c911f05c0b61 *man/gaussianff.Rd
a3a18ab32413faddd08a064dc1a07d9b *man/genbetaII.Rd
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65c6a7b53c50b4e20c9c9b2acfec6d0a *man/genrayleighUC.Rd
@@ -332,14 +332,14 @@ d89a22500e2031841b7bcfa1d8607d44 *man/get.smart.prediction.Rd
fd070015282f2cca2b0a4b8200822551 *man/gew.Rd
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4d6b5b18dc48d7884f978c040d2ac4cd *man/gpd.Rd
abb05712cc0126954637a4aeacc603e2 *man/gpdUC.Rd
7e50fed7b6ffe72b14e243fcc601fc50 *man/grain.us.Rd
87ec862c14d795b891259f1e4af22946 *man/grc.Rd
00bd52370e6b9e28b1ec106c6ecb2b09 *man/gumbel.Rd
bd6be76e82363793b9186e55d0e35bd0 *man/gumbelII.Rd
-f2d0c51e632d7e98d9be50a4a4fac4f2 *man/gumbelIIUC.Rd
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6e8fe2f3bce2e1f173f97fcd5f25d38d *man/gumbelUC.Rd
fc6b1658cbcb87054ab516552b6875f9 *man/guplot.Rd
d5ad348b7727127369874c7e7faf49bd *man/hatvalues.Rd
@@ -375,12 +375,12 @@ e80a85ec4d067a1549cc8249666f75c2 *man/laplace.Rd
2e88465ad75446bbbccf208661193a8c *man/lerch.Rd
8c7fca39c92e5f79391a7881a0f44026 *man/leukemia.Rd
632c83ea2a7b229a64a4679f9fa6b52f *man/levy.Rd
-18ae3dfb75762026bf20b93849bd3b89 *man/lgammaUC.Rd
+d375ce578b139d336603f32c4b3f4512 *man/lgammaUC.Rd
745ab1fea005b7572910ae5919111054 *man/lgammaff.Rd
-22cc8bb5e5ce47158dc867012db7c9c5 *man/lindUC.Rd
+66acbe44a180b5adb8fa0c5ea4897a18 *man/lindUC.Rd
271536a592dedaff73d9cde20c844d76 *man/lindley.Rd
20873e71a07de6b42d07fc6e0008ea05 *man/lino.Rd
-8a4a3a1cc12bdb111c6de98ec1c45e9f *man/linoUC.Rd
+0729b015342bba6152263c4ff9b07b8f *man/linoUC.Rd
b5dfa4faa955b15ebade0a3bdc8f93fe *man/lirat.Rd
1ecc473854215d5c5209ea54ad206370 *man/lms.bcg.Rd
194627e9dc632ec82df59b116971582a *man/lms.bcn.Rd
@@ -389,7 +389,7 @@ eea220ccf6de89caf996cf8edf346064 *man/lms.yjn.Rd
34cbd6bc583c55d2acd79a46a66e064e *man/logF.Rd
06a1ce6e6f01fca7e7037eabc6cf3dad *man/logF.UC.Rd
9f80bd504e1c75b0c7b29b3449cf7362 *man/logLikvlm.Rd
-f840f8e85c2092093673d6805cd21dc8 *man/logUC.Rd
+236716ee0347bd21a08aec9fec2a810b *man/logUC.Rd
e956c4aae749e9034b7cf7fdf8661a64 *man/logc.Rd
8c871e5697ed43662cd313fc777c2bcd *man/loge.Rd
20cc0c73ee555790179879533cb526f7 *man/logff.Rd
@@ -411,7 +411,7 @@ f0a38f0b82c1525dcd51687a2f2768c1 *man/lvplot.Rd
16b238586876d84bad0a1420402b5718 *man/lvplot.rrvglm.Rd
c5760c3960748f906230ded119478271 *man/machinists.Rd
eb7e6bf84eead25f006dc2fb6bfa55f7 *man/makeham.Rd
-a31274ff2b0c56cdb095a4cb93a31506 *man/makehamUC.Rd
+053679132aed00d872f73cc2954defee *man/makehamUC.Rd
583f3f406844c550079d2592ecba0c25 *man/margeff.Rd
b5c6a5a36ebe07a60b152387e8096d9a *man/marital.nz.Rd
b2f1aa9cecaec318a14cc5d4fbb20d67 *man/maxwell.Rd
@@ -429,7 +429,7 @@ c007d94fac5c46a26baae899a04aaf9d *man/melbmaxtemp.Rd
764cafd682a3364a495cdf243e3a528e *man/multilogit.Rd
d2ecbe308776f1e5065b0399959e2d99 *man/multinomial.Rd
c3248f9d509aecb0726bd0e6e36a13d4 *man/nakagami.Rd
-54346d08bf5b7e822c1b166365850222 *man/nakagamiUC.Rd
+15f93d300e50fe4c89470dccbc1b9fd8 *man/nakagamiUC.Rd
892ee6d069216d6568be506a7460c1c4 *man/nbcanlink.Rd
798f2e547a94356359c3d50a57ccef17 *man/nbolf.Rd
e707b37436b27c43ce07b77492e4fde2 *man/negbinomial.Rd
@@ -442,10 +442,10 @@ d361e050435d7a4e64474487ecfd782c *man/olym.Rd
687d43f8b77241bea9e7cbee86333fdb *man/paralogistic.Rd
73228cd851fcf468b1fe1ff209ef5eca *man/paralogisticUC.Rd
b8a1bd0580460ec6155b7c7bb2dae503 *man/paretoIV.Rd
-d0228dcb5ba3bd2a99272100a401c989 *man/paretoIVUC.Rd
+bb67a2455bfecfa9b6244178a15ced06 *man/paretoIVUC.Rd
c0c60830c70e697aeab8bc6d11472b78 *man/paretoff.Rd
97cf8349af611f4a6acf10e445e6587e *man/perks.Rd
-22126a9f4b6e01d96fb88f43e85d9d6a *man/perksUC.Rd
+3b6b40a1a031e3158efb9ab7c2760eb2 *man/perksUC.Rd
60fac0e03c8dce88e04e2c3f6def20b9 *man/persp.qrrvglm.Rd
a38168dd57b4be503cf47732714e441b *man/pgamma.deriv.Rd
8e0120c68b69d0760218c483490aed8e *man/pgamma.deriv.unscaled.Rd
@@ -499,17 +499,17 @@ bbde69d1bad346cd4ad04763c96d6ffe *man/qvar.Rd
64ea5646e75515a8b40fbd136fa6065e *man/rec.normal.Rd
49abf27f1c088a43cda71f0723cf188b *man/reciprocal.Rd
a56ddce8598af2320fdadb94c42a9b24 *man/rhobit.Rd
-70cd63e2118605590e782f086bf47b41 *man/riceUC.Rd
+e17b5680243d2c545139e60ff084ab47 *man/riceUC.Rd
728fcd45a64fbe92638143f6b1800038 *man/riceff.Rd
9dd5a151bfc05adcce0ae88a02eb08a8 *man/rigff.Rd
0e12c48578228c300e8c04ab3b08c04a *man/rlplot.egev.Rd
3c6afb0af10ae003dfa8cf9caa567d9b *man/rrar.Rd
-c1638b6d6833abcd2eb5814a328a6777 *man/rrvglm-class.Rd
+21af7f47c09e9758460cbf6d2ebf79cc *man/rrvglm-class.Rd
b95a04698f6a2a7163a03717d72f7dc0 *man/rrvglm.Rd
cf46faf7bd3cb7bbe65811130f78084f *man/rrvglm.control.Rd
eb0e4a0a8b0c63cd0c17120e9ca8df53 *man/rrvglm.optim.control.Rd
ecc44804896b8f3d4a9d469a952fe9a6 *man/ruge.Rd
-850477e7023b0617c4dd9bf177881736 *man/s.Rd
+b60106c185ce93eb2c09bc34d1f7b349 *man/s.Rd
3ebe2abf58080c4588a912c695adae77 *man/sc.studentt2.Rd
e5c019ffe15b61578ec4c5ed894d70ea *man/sc.t2UC.Rd
c3096134b4f765a7d1d893fb9388488b *man/seq2binomial.Rd
@@ -524,14 +524,14 @@ f158e6c60a4e6b6e13f2a9519515a021 *man/simplexUC.Rd
b62da6a60b01916a10d691e980253bc0 *man/skewnormUC.Rd
3797084c4e552d460e8b3942a661260a *man/skewnormal.Rd
9f34bfb220e6d0400971a1efa5db28c6 *man/slash.Rd
-213b0f18e657b3c80f1af5f2bc1f4c6b *man/slashUC.Rd
+d36053a053c3cdf9619cbc7b1f27f3bc *man/slashUC.Rd
21bada3a13aca65ba49fb28127575144 *man/smart.expression.Rd
5726ef8bb900532df62b24bd4b7b8fe4 *man/smart.mode.is.Rd
3d5d3a55f66ef8048b446da063e36ceb *man/smartpred.Rd
098bc8b943b6ae2e0de9a4da57fcfd22 *man/sratio.Rd
0c48da9ab33eb24273c6348320a64f64 *man/studentt.Rd
0258a94ee53da230fb2aea74fd90192a *man/tikuv.Rd
-dc0ae67e1d293040bf2d088e9bd4945b *man/tikuvUC.Rd
+18fb4965cd111cd04fd37a8a8ba1cde2 *man/tikuvUC.Rd
5fbf542c18e27e990c98bacedd614a39 *man/tobit.Rd
2b4e875a4415043bf0cd019e71e955cd *man/tobitUC.Rd
b70afa170b0cf98a6c2a9eea9dc58483 *man/toxop.Rd
@@ -545,11 +545,11 @@ d77a2419400b9ae1059949803b8a1dd2 *man/truncparetoUC.Rd
db1902b011f19b59642d53797848dcc8 *man/undocumented-methods.Rd
2fd783dbf5c2dbcb81727fe479729163 *man/uninormal.Rd
f787bf505e7e68f5f16a49f48abb9bcb *man/venice.Rd
-ecf0058b783f675c77a3ca1e5ab1a90a *man/vgam-class.Rd
+215970e9b9824a503e8984e432c5c924 *man/vgam-class.Rd
6db59f46bb2fbdbd6329f07498eca6d5 *man/vgam.Rd
ea3fe248b860921783367037c8302c49 *man/vgam.control.Rd
-1efef5d732a8585b81478fd03e103e5f *man/vglm-class.Rd
-cecde8d7fd2706132b92762bfed8055a *man/vglm.Rd
+d11e5c5279c115678bb103f5b4575938 *man/vglm-class.Rd
+c29f7ab8b46a70949458bbc9b2412c69 *man/vglm.Rd
c21cd55efce9d242cbe555cb65aea5e3 *man/vglm.control.Rd
8d9fa0cc290e49e459947c38c292df4c *man/vglmff-class.Rd
d1e31ea42a122762891de9a8589e2a4e *man/vonmises.Rd
@@ -563,7 +563,7 @@ f5a3078b689d511325cb1dc0fd4e21f3 *man/wrapup.smart.Rd
622f0105b04159f54fcfb361972e4fb7 *man/yeo.johnson.Rd
ebfff81b0f4730417de95f80b7c82c41 *man/yip88.Rd
225fcd19868f17b4a5d2590e834cb888 *man/yulesimon.Rd
-5057781d1bd6967a924665f8d63f6447 *man/yulesimonUC.Rd
+c4ec36c55401ffa660f63e3a03900465 *man/yulesimonUC.Rd
ae671324c0f93f66adc72f053ef9ebd9 *man/zabinomUC.Rd
87b0b38fe7357a2259edc9f1159add84 *man/zabinomial.Rd
7d5df5fee6f78c5cf37faaf71adbbb91 *man/zageomUC.Rd
@@ -576,11 +576,11 @@ b4bcb3a52a6e60efbdaa5d3cfed6fbf4 *man/zapoisUC.Rd
7985338d08e88fa23cce9cc0a09724b6 *man/zeta.Rd
e0ef189ae8251b5e0d20b614c18cdd5a *man/zetaUC.Rd
648342ad0677587e55e4f92d906d0d42 *man/zetaff.Rd
-4e7a3f2f0afb030cf5b4f1a30373871a *man/zibinomUC.Rd
+bce8783175ca63f89475e705b2fb1709 *man/zibinomUC.Rd
476f5935d0a6fcbe67f6e8cb39509a35 *man/zibinomial.Rd
-cf47526db95bc439da054ac97d2da36f *man/zigeomUC.Rd
+7b1d2ee37f339b9a218f1db4abb30cdd *man/zigeomUC.Rd
8de969235239ce10332c2b91304931f5 *man/zigeometric.Rd
-b4d704d064746b54f31f7d3d5c7e71c8 *man/zinegbinUC.Rd
+025dd2763701ec5b6880bcd6f4a9f35a *man/zinegbinUC.Rd
87def1c11bb8e7e5f4857a8c7eeda491 *man/zinegbinomial.Rd
a9b1d67033daa03a9880227187041ae5 *man/zipebcom.Rd
abfe2e5adf8a4fcd610adccf060e4f45 *man/zipf.Rd
@@ -601,10 +601,10 @@ feba7ba09eca8007392e0405c4b373a8 *src/muxr3.c
4814bb73b4c3eedc7507ad99511c7dc5 *src/tyeepolygamma.f
10939d9fb380d54da716a835d37fdf75 *src/tyeepolygamma3.c
79cf39f1d83f25e29a6c56d344ea8d76 *src/vcall2.f
-83c304cbbe3f0a9bfbe7ab5aa0eefd4e *src/vdigami.f
+3bc5ecda1e1216006e74ebd72b77d662 *src/vdigami.f
3e145d8721d17dbd0e642508c2de1472 *src/veigen.f
5ea414b5b42454c8efa73152c45ea62b *src/vgam.f
-cc42ab525129d3d763a8f590fd4a8238 *src/vgam3.c
+73b8d37419685738d4a7a151284299b4 *src/vgam3.c
bbb4ca20dcf50cd985b411b9a65b68f2 *src/vlinpack1.f
80c0a0f512ae74ecbed144c5f115fb16 *src/vlinpack2.f
e9187111f5c6ce1e5808bbb3dc088c17 *src/vlinpack3.f
diff --git a/NEWS b/NEWS
index e6d6516..e53096e 100755
--- a/NEWS
+++ b/NEWS
@@ -6,6 +6,21 @@
+ CHANGES IN VGAM VERSION 0.9-6
+
+NEW FEATURES
+
+ o All d-type functions handle the 'n' argument the same
+ way as runif(). This was done with the help of Kai Huang.
+
+
+BUG FIXES and CHANGES
+
+ o Slot "res.ss" changed to "ResSS".
+ o Some argument reference errors found by valgrind have been fixed.
+
+
+
CHANGES IN VGAM VERSION 0.9-5
NEW FEATURES
diff --git a/R/aamethods.q b/R/aamethods.q
index 09134d5..9c6c2c4 100644
--- a/R/aamethods.q
+++ b/R/aamethods.q
@@ -168,7 +168,7 @@ setClass("vlm", representation(
"qr" = "list",
"R" = "matrix",
"rank" = "integer",
- "res.ss" = "numeric",
+ "ResSS" = "numeric",
"smart.prediction" = "list",
"terms" = "list",
"Xm2" = "matrix",
@@ -312,7 +312,7 @@ new("vglm", "extra"=from at extra,
"R"=from at R,
"rank"=from at rank,
"residuals"=from at residuals,
- "res.ss"=from at res.ss,
+ "ResSS"=from at ResSS,
"smart.prediction"=from at smart.prediction,
"terms"=from at terms,
"weights"=from at weights,
diff --git a/R/cqo.fit.q b/R/cqo.fit.q
index a527459..bda3f30 100644
--- a/R/cqo.fit.q
+++ b/R/cqo.fit.q
@@ -582,7 +582,7 @@ ny <- names(y)
iter = iter,
misc = misc,
post = post,
- res.ss = 000,
+ ResSS = 000,
x = x,
y = y)),
vclass = family at vfamily)
@@ -662,7 +662,7 @@ ny <- names(y)
alt <- valt(x = cbind(X1, X2), z = etamat,
U = sqrt(t(wts)), Rank = effrank,
Hlist = NULL, Cinit = NULL, trace = FALSE,
- colx1.index = 1:ncol(X1), Criterion = "res.ss")
+ colx1.index = 1:ncol(X1), Criterion = "ResSS")
temp.control <- list(Rank = effrank, colx1.index = 1:ncol(X1),
Alpha = 0.5,
colx2.index = (ncol(X1)+1):(ncol(X1) + ncol(X2)),
@@ -689,7 +689,7 @@ ny <- names(y)
U <- t(sqrt(wts))
tmp <- vlm.wfit(xmat = X1, zmat = etamat, Hlist = NULL, U = U,
matrix.out = TRUE,
- is.vlmX = FALSE, res.ss = TRUE, qr = FALSE, xij = xij)
+ is.vlmX = FALSE, ResSS = TRUE, qr = FALSE, xij = xij)
ans <- crow1C(as.matrix(tmp$resid),
rep(Crow1positive, length.out = effrank))
if (effrank < Rank) {
diff --git a/R/family.aunivariate.R b/R/family.aunivariate.R
index 31f4528..7cde7e6 100644
--- a/R/family.aunivariate.R
+++ b/R/family.aunivariate.R
@@ -265,9 +265,8 @@ drice <- function(x, sigma, vee, log = FALSE) {
}
+
rrice <- function(n, sigma, vee) {
- if (!is.Numeric(n, integer.valued = TRUE, length.arg = 1))
- stop("bad input for argument 'n'")
theta <- 1 # any number
X <- rnorm(n, mean = vee * cos(theta), sd = sigma)
Y <- rnorm(n, mean = vee * sin(theta), sd = sigma)
@@ -780,13 +779,15 @@ dyules <- function(x, rho, log = FALSE) {
}
+
+
ryules <- function(n, rho) {
- if (!is.Numeric(n, integer.valued = TRUE, length.arg = 1))
- stop("bad input for argument 'n'")
- rgeom(n, prob = exp(-rexp(n, rate=rho))) + 1
+ rgeom(n, prob = exp(-rexp(n, rate = rho))) + 1
}
+
+
pyules <- function(q, rho) {
tq <- trunc(q)
ans <- 1 - tq * beta(abs(tq), rho+1)
diff --git a/R/family.binomial.R b/R/family.binomial.R
index 4cd04c2..e8df510 100644
--- a/R/family.binomial.R
+++ b/R/family.binomial.R
@@ -420,10 +420,12 @@ rbinom2.or <-
colnames = if (twoCols) c("y1", "y2") else
c("00", "01", "10", "11"),
ErrorCheck = TRUE) {
+ use.n <- if ((length.n <- length(n)) > 1) length.n else
+ if (!is.Numeric(n, integer.valued = TRUE,
+ length.arg = 1, positive = TRUE))
+ stop("bad input for argument 'n'") else n
+
if (ErrorCheck) {
- if (!is.Numeric(n, integer.valued = TRUE, positive = TRUE,
- length.arg = 1))
- stop("bad input for argument 'n'")
if (!is.Numeric(mu1, positive = TRUE) || max(mu1) >= 1)
stop("bad input for argument 'mu1'")
if (!is.Numeric(mu2, positive = TRUE) || max(mu2) >= 1)
@@ -441,10 +443,10 @@ rbinom2.or <-
exchangeable = exchangeable,
tol = tol, ErrorCheck = ErrorCheck)
- answer <- matrix(0, n, 2,
+ answer <- matrix(0, use.n, 2,
dimnames = list(NULL,
if (twoCols) colnames else NULL))
- yy <- runif(n)
+ yy <- runif(use.n)
cs1 <- dmat[, "00"] + dmat[, "01"]
cs2 <- cs1 + dmat[, "10"]
index <- (dmat[, "00"] < yy) & (yy <= cs1)
@@ -453,9 +455,11 @@ rbinom2.or <-
answer[index, 1] <- 1
index <- (yy > cs2)
answer[index,] <- 1
- if (twoCols) answer else {
- answer4 <- matrix(0, n, 4, dimnames = list(NULL, colnames))
- answer4[cbind(1:n, 1 + 2*answer[, 1] + answer[, 2])] <- 1
+ if (twoCols) {
+ answer
+ } else {
+ answer4 <- matrix(0, use.n, 4, dimnames = list(NULL, colnames))
+ answer4[cbind(1:use.n, 1 + 2*answer[, 1] + answer[, 2])] <- 1
answer4
}
}
@@ -730,10 +734,12 @@ rbinom2.rho <-
colnames = if (twoCols) c("y1", "y2") else
c("00", "01", "10", "11"),
ErrorCheck = TRUE) {
+ use.n <- if ((length.n <- length(n)) > 1) length.n else
+ if (!is.Numeric(n, integer.valued = TRUE,
+ length.arg = 1, positive = TRUE))
+ stop("bad input for argument 'n'") else n
+
if (ErrorCheck) {
- if (!is.Numeric(n, integer.valued = TRUE,
- positive = TRUE, length.arg = 1))
- stop("bad input for argument 'n'")
if (!is.Numeric(mu1, positive = TRUE) ||
max(mu1) >= 1)
stop("bad input for argument 'mu1'")
@@ -754,10 +760,10 @@ rbinom2.rho <-
exchangeable = exchangeable,
ErrorCheck = ErrorCheck)
- answer <- matrix(0, n, 2,
+ answer <- matrix(0, use.n, 2,
dimnames = list(NULL,
if (twoCols) colnames else NULL))
- yy <- runif(n)
+ yy <- runif(use.n)
cs1 <- dmat[, "00"] + dmat[, "01"]
cs2 <- cs1 + dmat[, "10"]
index <- (dmat[, "00"] < yy) & (yy <= cs1)
@@ -766,9 +772,11 @@ rbinom2.rho <-
answer[index, 1] <- 1
index <- (yy > cs2)
answer[index,] <- 1
- if (twoCols) answer else {
- answer4 <- matrix(0, n, 4, dimnames = list(NULL, colnames))
- answer4[cbind(1:n, 1 + 2*answer[, 1] + answer[, 2])] <- 1
+ if (twoCols) {
+ answer
+ } else {
+ answer4 <- matrix(0, use.n, 4, dimnames = list(NULL, colnames))
+ answer4[cbind(1:use.n, 1 + 2*answer[, 1] + answer[, 2])] <- 1
answer4
}
}
diff --git a/R/family.categorical.R b/R/family.categorical.R
index d2efbc7..1af19f3 100644
--- a/R/family.categorical.R
+++ b/R/family.categorical.R
@@ -1998,7 +1998,6 @@ InverseBrat <-
mu
}, list( .link = link, .earg = earg, .countdata = countdata ))),
last = eval(substitute(expression({
-print("y.names")
if ( .countdata ) {
misc$link <- .link
misc$earg <- list( .earg )
diff --git a/R/family.censored.R b/R/family.censored.R
index 4339f6f..49e67a8 100644
--- a/R/family.censored.R
+++ b/R/family.censored.R
@@ -1087,8 +1087,7 @@ pgamma.deriv.unscaled <- function(q, shape) {
- gam0 <- exp(lgamma(shape) +
- pgamma(q = q, shape = shape, log.p = TRUE))
+ gam0 <- exp(lgamma(shape) + pgamma(q = q, shape = shape, log.p = TRUE))
I.sq <- pgamma(q = q, shape = shape)
@@ -1099,7 +1098,7 @@ pgamma.deriv.unscaled <- function(q, shape) {
gam1 <- gam0 * G1s
- dG1s <- trigamma(shape) + alld[, 4] / I.sq - tmp3^2 # eqn (4.13)
+ dG1s <- trigamma(shape) + alld[, 4] / I.sq - tmp3^2 # eqn (4.13)
G2s <- dG1s + G1s^2 # eqn (4.12)
gam2 <- gam0 * G2s
diff --git a/R/family.circular.R b/R/family.circular.R
index 7a9d360..35fcd43 100644
--- a/R/family.circular.R
+++ b/R/family.circular.R
@@ -79,17 +79,20 @@ qcard <- function(p, mu, rho, tolerance=1.0e-7, maxits=500) {
rcard <- function(n, mu, rho, ...) {
+ use.n <- if ((length.n <- length(n)) > 1) length.n else
+ if (!is.Numeric(n, integer.valued = TRUE,
+ length.arg = 1, positive = TRUE))
+ stop("bad input for argument 'n'") else n
+
+
if (!is.Numeric(mu) || any(mu < 0) || any(mu > 2*pi))
stop("argument 'mu' must be between 0 and 2*pi inclusive")
if (!is.Numeric(rho) || max(abs(rho) > 0.5))
stop("argument 'rho' must be between -0.5 and 0.5 inclusive")
- if (!is.Numeric(n, positive = TRUE,
- integer.valued = TRUE, length.arg = 1))
- stop("argument 'n' must be a single positive integer")
- mu <- rep(mu, len = n)
- rho <- rep(rho, len = n)
- qcard(runif (n), mu = mu, rho = rho, ...)
+ mu <- rep(mu, len = use.n)
+ rho <- rep(rho, len = use.n)
+ qcard(runif(use.n), mu = mu, rho = rho, ...)
}
diff --git a/R/family.functions.R b/R/family.functions.R
index 4ad19ba..99d5ca3 100644
--- a/R/family.functions.R
+++ b/R/family.functions.R
@@ -110,7 +110,7 @@ tapplymat1 <- function(mat, function.arg = c("cumsum", "diff", "cumprod")) {
NR <- nrow(mat)
NC <- ncol(mat)
fred <- .C("tapply_mat1", mat = as.double(mat), as.integer(NR),
- as.integer(NC), as.integer(type), PACKAGE = "VGAM")
+ as.integer(NC), as.integer(type)) # , PACKAGE = "VGAM"
dim(fred$mat) <- c(NR, NC)
dimnames(fred$mat) <- dimnames(mat)
switch(function.arg,
diff --git a/R/family.math.R b/R/family.math.R
index 0559f29..94adc65 100644
--- a/R/family.math.R
+++ b/R/family.math.R
@@ -105,10 +105,8 @@ lambertW <- function(x, tolerance = 1.0e-10, maxit = 50) {
pgamma.deriv <- function(q, shape, tmax = 100) {
nnn <- max(length(q), length(shape))
- if (length(q) != nnn)
- q <- rep(q, length = nnn)
- if (length(shape) != nnn)
- shape <- rep(shape, length = nnn)
+ if (length(q) != nnn) q <- rep(q, length = nnn)
+ if (length(shape) != nnn) shape <- rep(shape, length = nnn)
if (!is.Numeric(q, positive = TRUE))
stop("bad input for argument 'q'")
diff --git a/R/family.normal.R b/R/family.normal.R
index 3d95daf..8192deb 100644
--- a/R/family.normal.R
+++ b/R/family.normal.R
@@ -610,8 +610,10 @@ qtikuv <- function(p, d, mean = 0, sigma = 1, ...) {
rtikuv <- function(n, d, mean = 0, sigma = 1, Smallno = 1.0e-6) {
- if (!is.Numeric(n, positive = TRUE, integer.valued = TRUE))
- stop("bad input for argument 'n'")
+ use.n <- if ((length.n <- length(n)) > 1) length.n else
+ if (!is.Numeric(n, integer.valued = TRUE,
+ length.arg = 1, positive = TRUE))
+ stop("bad input for argument 'n'") else n
if (!is.Numeric(d, length.arg = 1) || max(d) >= 2)
stop("bad input for argument 'd'")
if (!is.Numeric(mean, length.arg = 1))
@@ -622,7 +624,7 @@ rtikuv <- function(n, d, mean = 0, sigma = 1, Smallno = 1.0e-6) {
Smallno > 0.01 ||
Smallno < 2 * .Machine$double.eps)
stop("bad input for argument 'Smallno'")
- ans <- rep(0.0, len = n)
+ ans <- rep(0.0, len = use.n)
ptr1 <- 1; ptr2 <- 0
hh <- 2 - d
@@ -631,20 +633,20 @@ rtikuv <- function(n, d, mean = 0, sigma = 1, Smallno = 1.0e-6) {
dtikuv(x = mean + sigma*sqrt(4 - 2*hh),
d = d, mean = mean, sigma = sigma),
KK / (sqrt(2 * pi) * sigma))
- while (ptr2 < n) {
+ while (ptr2 < use.n) {
Lower <- mean - 5 * sigma
while (ptikuv(q = Lower, d = d, mean = mean, sigma = sigma) > Smallno)
Lower <- Lower - sigma
Upper <- mean + 5 * sigma
while (ptikuv(q = Upper, d = d, mean = mean, sigma = sigma) < 1-Smallno)
Upper <- Upper + sigma
- x <- runif(2*n, min = Lower, max = Upper)
- index <- runif(2*n, max = ymax) <
+ x <- runif(2*use.n, min = Lower, max = Upper)
+ index <- runif(2*use.n, max = ymax) <
dtikuv(x, d = d, mean = mean, sigma = sigma)
sindex <- sum(index)
if (sindex) {
- ptr2 <- min(n, ptr1 + sindex - 1)
- ans[ptr1:ptr2] = (x[index])[1:(1+ptr2-ptr1)]
+ ptr2 <- min(use.n, ptr1 + sindex - 1)
+ ans[ptr1:ptr2] <- (x[index])[1:(1+ptr2-ptr1)]
ptr1 <- ptr2 + 1
}
}
diff --git a/R/family.rrr.R b/R/family.rrr.R
index d40d828..9f27063 100644
--- a/R/family.rrr.R
+++ b/R/family.rrr.R
@@ -21,7 +21,7 @@ replace.constraints <- function(Hlist, cm, index) {
valt.control <- function(
Alphavec = c(2, 4, 6, 9, 12, 16, 20, 25, 30, 40, 50,
60, 80, 100, 125, 2^(8:12)),
- Criterion = c("res.ss", "coefficients"),
+ Criterion = c("ResSS", "coefficients"),
Linesearch = FALSE,
Maxit = 7,
Suppress.warning = TRUE,
@@ -29,7 +29,7 @@ replace.constraints <- function(Hlist, cm, index) {
if (mode(Criterion) != "character" && mode(Criterion) != "name")
Criterion <- as.character(substitute(Criterion))
- Criterion <- match.arg(Criterion, c("res.ss", "coefficients"))[1]
+ Criterion <- match.arg(Criterion, c("ResSS", "coefficients"))[1]
list(Alphavec = Alphavec,
Criterion = Criterion,
@@ -68,7 +68,7 @@ qrrvglm.xprod <- function(numat, Aoffset, Quadratic, I.tolerances) {
Cinit = NULL,
Alphavec = c(2, 4, 6, 9, 12, 16, 20, 25, 30, 40, 50,
60, 80, 100, 125, 2^(8:12)),
- Criterion = c("res.ss", "coefficients"),
+ Criterion = c("ResSS", "coefficients"),
Crow1positive = rep(TRUE, length.out = Rank),
colx1.index,
Linesearch = FALSE,
@@ -88,7 +88,7 @@ qrrvglm.xprod <- function(numat, Aoffset, Quadratic, I.tolerances) {
if (mode(Criterion) != "character" && mode(Criterion) != "name")
Criterion <- as.character(substitute(Criterion))
- Criterion <- match.arg(Criterion, c("res.ss", "coefficients"))[1]
+ Criterion <- match.arg(Criterion, c("ResSS", "coefficients"))[1]
if (any(diff(Alphavec) <= 0))
stop("'Alphavec' must be an increasing sequence")
@@ -130,10 +130,10 @@ qrrvglm.xprod <- function(numat, Aoffset, Quadratic, I.tolerances) {
if (is.null(Cinit))
Cinit <- matrix(rnorm(p2*Rank, sd = sd.Cinit), p2, Rank)
- fit <- list(res.ss = 0) # Only for initial old.crit below
+ fit <- list(ResSS = 0) # Only for initial old.crit below
C <- Cinit # This is input for the main iter loop
- old.crit <- switch(Criterion, coefficients = C, res.ss = fit$res.ss)
+ old.crit <- switch(Criterion, coefficients = C, ResSS = fit$ResSS)
recover <- 0 # Allow a few iterations between different line searches
for (iter in 1:Maxit) {
@@ -144,13 +144,13 @@ qrrvglm.xprod <- function(numat, Aoffset, Quadratic, I.tolerances) {
if (p1) x[, colx1.index] else NULL)
fit <- vlm.wfit(xmat = new.latvar.model.matrix, z, Hlist = clist2,
U = U, matrix.out = TRUE, is.vlmX = FALSE,
- res.ss = FALSE, qr = FALSE, xij = xij)
+ ResSS = FALSE, qr = FALSE, xij = xij)
A <- t(fit$mat.coef[1:Rank, , drop = FALSE])
clist1 <- replace.constraints(Hlist, A, colx2.index)
fit <- vlm.wfit(xmat = x, z, Hlist = clist1, U = U,
matrix.out = TRUE, is.vlmX = FALSE,
- res.ss = TRUE, qr = FALSE, xij = xij)
+ ResSS = TRUE, qr = FALSE, xij = xij)
C <- fit$mat.coef[colx2.index, , drop = FALSE] %*% A %*%
solve(t(A) %*% A)
@@ -169,14 +169,14 @@ qrrvglm.xprod <- function(numat, Aoffset, Quadratic, I.tolerances) {
switch(Criterion,
coefficients = max(abs(C - old.crit) / (
Tolerance + abs(C))),
- res.ss = max(abs(fit$res.ss - old.crit) / (
- Tolerance + fit$res.ss)))
+ ResSS = max(abs(fit$ResSS - old.crit) / (
+ Tolerance + fit$ResSS)))
if (trace) {
cat(" Alternating iteration", iter,
", Convergence criterion = ", format(ratio), "\n")
- if (!is.null(fit$res.ss))
- cat(" ResSS = ", fit$res.ss, "\n")
+ if (!is.null(fit$ResSS))
+ cat(" ResSS = ", fit$ResSS, "\n")
flush.console()
}
@@ -192,7 +192,7 @@ qrrvglm.xprod <- function(numat, Aoffset, Quadratic, I.tolerances) {
xnew <- C
direction1 <- (xnew - xold) # / sqrt(1 + sum((xnew-xold)^2))
- ftemp <- fit$res.ss # Most recent objective function
+ ftemp <- fit$ResSS # Most recent objective function
use.alpha <- 0 # The current step relative to (xold, yold)
for (itter in 1:length(Alphavec)) {
CC <- xold + Alphavec[itter] * direction1
@@ -204,12 +204,12 @@ qrrvglm.xprod <- function(numat, Aoffset, Quadratic, I.tolerances) {
try <- vlm.wfit(xmat = try.new.latvar.model.matrix, z,
Hlist = clist2, U = U, matrix.out = TRUE,
- is.vlmX = FALSE, res.ss = TRUE, qr = FALSE,
+ is.vlmX = FALSE, ResSS = TRUE, qr = FALSE,
xij = xij)
- if (try$res.ss < ftemp) {
+ if (try$ResSS < ftemp) {
use.alpha <- Alphavec[itter]
fit <- try
- ftemp <- try$res.ss
+ ftemp <- try$ResSS
C <- CC
A <- t(fit$mat.coef[1:Rank, , drop = FALSE])
latvar.mat <- x[, colx2.index, drop = FALSE] %*% C
@@ -229,14 +229,14 @@ qrrvglm.xprod <- function(numat, Aoffset, Quadratic, I.tolerances) {
xold <- C # Do not take care of drift
old.crit <- switch(Criterion,
coefficients = C,
- res.ss = fit$res.ss)
+ ResSS = fit$ResSS)
} # End of iter loop
list(A = A,
C = C,
fitted = fit$fitted,
new.coeffs = fit$coef,
- res.ss = fit$res.ss)
+ ResSS = fit$ResSS)
}
@@ -329,13 +329,13 @@ valt.2iter <- function(x, z, U, Hlist, A, control) {
clist1 <- replace.constraints(Hlist, A, control$colx2.index)
fit <- vlm.wfit(xmat = x, z, Hlist = clist1, U = U, matrix.out = TRUE,
- is.vlmX = FALSE, res.ss = TRUE, qr = FALSE, xij = control$xij)
+ is.vlmX = FALSE, ResSS = TRUE, qr = FALSE, xij = control$xij)
C <- fit$mat.coef[control$colx2.index, , drop = FALSE] %*%
A %*% solve(t(A) %*% A)
list(A = A, C = C,
fitted = fit$fitted, new.coeffs = fit$coef,
- Hlist = clist1, res.ss = fit$res.ss)
+ Hlist = clist1, ResSS = fit$ResSS)
}
@@ -363,7 +363,7 @@ valt.1iter <- function(x, z, U, Hlist, C, control,
zedd[,Index.corner] <- zedd[,Index.corner] - latvar.mat
if (nice31 && MSratio == 1) {
- fit <- list(mat.coef = NULL, fitted.values = NULL, res.ss = 0)
+ fit <- list(mat.coef = NULL, fitted.values = NULL, ResSS = 0)
clist2 <- NULL # for vlm.wfit
@@ -376,12 +376,12 @@ valt.1iter <- function(x, z, U, Hlist, C, control,
Hlist = clist2,
U = U[i5,, drop = FALSE],
matrix.out = TRUE,
- is.vlmX = FALSE, res.ss = TRUE,
+ is.vlmX = FALSE, ResSS = TRUE,
qr = FALSE,
Eta.range = control$Eta.range,
xij = control$xij,
lp.names = lp.names[i5])
- fit$res.ss <- fit$res.ss + tmp100$res.ss
+ fit$ResSS <- fit$ResSS + tmp100$ResSS
fit$mat.coef <- cbind(fit$mat.coef, tmp100$mat.coef)
fit$fitted.values <- cbind(fit$fitted.values,
tmp100$fitted.values)
@@ -390,7 +390,7 @@ valt.1iter <- function(x, z, U, Hlist, C, control,
fit <- vlm.wfit(xmat = new.latvar.model.matrix,
zedd, Hlist = clist2, U = U,
matrix.out = TRUE,
- is.vlmX = FALSE, res.ss = TRUE, qr = FALSE,
+ is.vlmX = FALSE, ResSS = TRUE, qr = FALSE,
Eta.range = control$Eta.range,
xij = control$xij, lp.names = lp.names)
}
@@ -419,7 +419,7 @@ valt.1iter <- function(x, z, U, Hlist, C, control,
list(Amat = A, B1 = B1, Cmat = C, Dmat = Dmat,
fitted = if (M == 1) c(fv) else fv,
- new.coeffs = fit$coef, constraints = clist2, res.ss = fit$res.ss,
+ new.coeffs = fit$coef, constraints = clist2, ResSS = fit$ResSS,
offset = if (length(tmp833$offset)) tmp833$offset else NULL)
}
@@ -740,7 +740,7 @@ rrr.derivative.expression <- expression({
rrcontrol$Reltol[iter] else rev(rrcontrol$Reltol)[1]
quasi.newton <-
optim(par = theta0,
- fn = rrr.derivC.res.ss,
+ fn = rrr.derivC.ResSS,
method = which.optimizer,
control = list(fnscale = rrcontrol$Fnscale,
maxit = rrcontrol$Maxit,
@@ -808,7 +808,7 @@ rrr.derivative.expression <- expression({
-rrr.derivC.res.ss <- function(theta, U, z, M, xmat, Hlist, rrcontrol,
+rrr.derivC.ResSS <- function(theta, U, z, M, xmat, Hlist, rrcontrol,
omit.these = NULL) {
if (rrcontrol$trace) {
@@ -848,10 +848,10 @@ rrr.derivC.res.ss <- function(theta, U, z, M, xmat, Hlist, rrcontrol,
vlm.wfit(xmat = tmp700$new.latvar.model.matrix, zmat = z,
- Hlist = Hlist, ncolx = ncol(xmat), U = U, only.res.ss = TRUE,
- matrix.out = FALSE, is.vlmX = FALSE, res.ss = TRUE,
+ Hlist = Hlist, ncolx = ncol(xmat), U = U, only.ResSS = TRUE,
+ matrix.out = FALSE, is.vlmX = FALSE, ResSS = TRUE,
qr = FALSE, Eta.range = rrcontrol$Eta.range,
- xij = rrcontrol$xij)$res.ss
+ xij = rrcontrol$xij)$ResSS
}
@@ -1619,7 +1619,7 @@ summary.rrvglm <- function(object, correlation = FALSE,
answer at df[1] <- answer at df[1] + tmp8 * object at control$Rank
answer at df[2] <- answer at df[2] - tmp8 * object at control$Rank
if (dispersion == 0) {
- dispersion <- tmp5$res.ss / answer at df[2] # Estimate
+ dispersion <- tmp5$ResSS / answer at df[2] # Estimate
}
answer at coef3 <- get.rrvglm.se2(answer at cov.unscaled,
@@ -1839,7 +1839,7 @@ get.rrvglm.se1 <- function(fit, omit13 = FALSE, kill.all = FALSE,
dimnames(ans) <- list(names(acoefs), names(acoefs))
list(cov.unscaled = ans,
coefficients = acoefs,
- res.ss = sfit1122 at res.ss)
+ ResSS = sfit1122 at ResSS)
}
@@ -1908,7 +1908,7 @@ num.deriv.rrr <- function(fit, M, r, x1mat, x2mat,
newfit <- vlm.wfit(xmat = x2mat, zmat = newzmat,
Hlist = small.Hlist, U = U,
matrix.out = FALSE, is.vlmX = FALSE,
- res.ss = TRUE, qr = FALSE, x.ret = FALSE,
+ ResSS = TRUE, qr = FALSE, x.ret = FALSE,
offset = NULL, xij = xij)
dct.da[ptr, ] <- (newfit$coef - t(Cimat)) / h.step
ptr <- ptr + 1
@@ -2096,7 +2096,7 @@ dcda.fast <- function(theta, wz, U, z, M, r, xmat, pp, Index.corner,
-rrr.deriv.res.ss <- function(theta, wz, U, z, M, r, xmat,
+rrr.deriv.ResSS <- function(theta, wz, U, z, M, r, xmat,
pp, Index.corner, intercept = TRUE,
xij = NULL) {
@@ -2116,7 +2116,7 @@ rrr.deriv.res.ss <- function(theta, wz, U, z, M, r, xmat,
}
vlm.wfit(xmat = xmat, z, Hlist, U = U, matrix.out = FALSE,
- res.ss = TRUE, xij = xij)$res.ss
+ ResSS = TRUE, xij = xij)$ResSS
}
diff --git a/R/family.univariate.R b/R/family.univariate.R
index 84368f8..4bb3b49 100644
--- a/R/family.univariate.R
+++ b/R/family.univariate.R
@@ -7886,18 +7886,16 @@ dgengamma.stacy <- function(x, scale = 1, d = 1, k = 1, log = FALSE) {
stop("bad input for argument 'log'")
rm(log)
- if (!is.Numeric(scale, positive = TRUE))
- stop("bad input for argument 'scale'")
if (!is.Numeric(d, positive = TRUE))
stop("bad input for argument 'd'")
if (!is.Numeric(k, positive = TRUE))
stop("bad input for argument 'k'")
- N <- max(length(x), length(scale), length(d), length(k))
- x <- rep(x, length.out = N);
- scale <- rep(scale, length.out = N);
- d <- rep(d, length.out = N);
- k <- rep(k, length.out = N);
+ N <- max(length(x), length(scale), length(d), length(k))
+ x <- rep(x, length.out = N)
+ scale <- rep(scale, length.out = N)
+ d <- rep(d, length.out = N)
+ k <- rep(k, length.out = N)
Loglik <- rep(log(0), length.out = N)
xok <- x > 0
@@ -7906,11 +7904,24 @@ dgengamma.stacy <- function(x, scale = 1, d = 1, k = 1, log = FALSE) {
Loglik[xok] <- log(d[xok]) + (-d[xok]*k[xok]) * log(scale[xok]) +
(d[xok]*k[xok]-1) * log(x[xok]) - zedd - lgamma(k[xok])
}
- if (log.arg) {
+
+
+ Loglik[is.infinite(x)] <- log(0) # 20141208; KaiH.
+
+
+ answer <- if (log.arg) {
Loglik
} else {
exp(Loglik)
}
+
+
+ answer[scale < 0] <- NaN
+ answer[scale == 0] <- NaN # Not strictly correct
+ if (any(scale <= 0))
+ warning("NaNs produced")
+
+ answer
}
diff --git a/R/family.zeroinf.R b/R/family.zeroinf.R
index 7ff1127..1f13b78 100644
--- a/R/family.zeroinf.R
+++ b/R/family.zeroinf.R
@@ -3137,6 +3137,7 @@ qzinegbin <- function(p, size, prob = NULL, munb = NULL, pstr0 = 0) {
}
+
rzinegbin <- function(n, size, prob = NULL, munb = NULL, pstr0 = 0) {
if (length(munb)) {
if (length(prob))
diff --git a/R/predict.vlm.q b/R/predict.vlm.q
index 1490b62..5e3524c 100644
--- a/R/predict.vlm.q
+++ b/R/predict.vlm.q
@@ -165,7 +165,7 @@ predict.vlm <- function(object,
object <- as(object, "vlm") # Coerce
fit.summary <- summaryvlm(object, dispersion=dispersion)
sigma <- if (is.numeric(fit.summary at sigma)) fit.summary at sigma else
- sqrt(deviance(object) / object at df.residual) # was @res.ss
+ sqrt(deviance(object) / object at df.residual) # was @ResSS
pred <- Build.terms.vlm(x = X_vlm, coefs = coefs,
cov = sigma^2 * fit.summary at cov.unscaled,
assign = vasgn,
diff --git a/R/print.vlm.q b/R/print.vlm.q
index 715a058..90b4bf1 100644
--- a/R/print.vlm.q
+++ b/R/print.vlm.q
@@ -34,9 +34,9 @@ show.vlm <- function(object) {
if (length(deviance(object)) &&
is.finite(deviance(object)))
cat("Deviance:", format(deviance(object)), "\n")
- if (length(object at res.ss) &&
- is.finite(object at res.ss))
- cat("Residual Sum of Squares:", format(object at res.ss), "\n")
+ if (length(object at ResSS) &&
+ is.finite(object at ResSS))
+ cat("Residual Sum of Squares:", format(object at ResSS), "\n")
invisible(object)
}
@@ -79,9 +79,9 @@ print.vlm <- function(x, ...) {
if (length(deviance(x)) &&
is.finite(deviance(x)))
cat("Deviance:", format(deviance(x)), "\n")
- if (length(x at res.ss) &&
- is.finite(x at res.ss))
- cat("Residual Sum of Squares:", format(x at res.ss), "\n")
+ if (length(x at ResSS) &&
+ is.finite(x at ResSS))
+ cat("Residual Sum of Squares:", format(x at ResSS), "\n")
invisible(x)
}
diff --git a/R/rrvglm.R b/R/rrvglm.R
index e70983f..7983c04 100644
--- a/R/rrvglm.R
+++ b/R/rrvglm.R
@@ -148,7 +148,7 @@ rrvglm <- function(formula,
"R" = fit$R,
"rank" = fit$rank,
"residuals" = as.matrix(fit$residuals),
- "res.ss" = fit$res.ss,
+ "ResSS" = fit$ResSS,
"smart.prediction" = as.list(fit$smart.prediction),
"terms" = list(terms = mt))
diff --git a/R/rrvglm.fit.q b/R/rrvglm.fit.q
index 0ebb583..4939d70 100644
--- a/R/rrvglm.fit.q
+++ b/R/rrvglm.fit.q
@@ -653,7 +653,7 @@ rrvglm.fit <-
iter = iter,
misc = misc,
post = post,
- res.ss = if (nice31) 000 else tfit$res.ss,
+ ResSS = if (nice31) 000 else tfit$ResSS,
x = x,
y = y)),
vclass = family at vfamily)
diff --git a/R/s.vam.q b/R/s.vam.q
index e292c20..2e3eadd 100644
--- a/R/s.vam.q
+++ b/R/s.vam.q
@@ -272,7 +272,7 @@ s.vam <- function(x, zedd, wz, smomat, which, smooth.frame, bf.maxit = 10,
R = R,
rank = qrank,
residuals = fit$y - fit$etamat,
- res.ss = fit$doubvec[2],
+ ResSS = fit$doubvec[2],
smomat = fit$smomat,
sparv = fit$sparv,
s.xargument = unlist(smooth.frame$s.xargument))
diff --git a/R/summary.vlm.q b/R/summary.vlm.q
index bdad485..69c2281 100644
--- a/R/summary.vlm.q
+++ b/R/summary.vlm.q
@@ -55,10 +55,10 @@ summaryvlm <-
}
} else if (dispersion == 0) {
dispersion <-
- if (!length(object at res.ss)) {
- stop("object at res.ss is empty")
+ if (!length(object at ResSS)) {
+ stop("object at ResSS is empty")
} else {
- object at res.ss / object at df.residual
+ object at ResSS / object at df.residual
}
object at misc$estimated.dispersion <- TRUE
} else {
@@ -190,8 +190,8 @@ show.summary.vlm <- function(x, digits = NULL, quote = TRUE,
}
- if (!is.null(x at res.ss))
- cat("\nResidual Sum of Squares:", format(round(x at res.ss, digits)),
+ if (!is.null(x at ResSS))
+ cat("\nResidual Sum of Squares:", format(round(x at ResSS, digits)),
"on", round(rdf, digits), "degrees of freedom\n")
diff --git a/R/vgam.R b/R/vgam.R
index a4b1487..7c85ea3 100644
--- a/R/vgam.R
+++ b/R/vgam.R
@@ -235,7 +235,7 @@ vgam <- function(formula,
"R" = fit$R,
"rank" = fit$rank,
"residuals" = as.matrix(fit$residuals),
- "res.ss" = fit$res.ss,
+ "ResSS" = fit$ResSS,
"smart.prediction" = as.list(fit$smart.prediction),
"terms" = list(terms = fit$terms))
diff --git a/R/vglm.R b/R/vglm.R
index f6dc7b9..ad01b9a 100644
--- a/R/vglm.R
+++ b/R/vglm.R
@@ -144,7 +144,7 @@ vglm <- function(formula,
"R" = fit$R,
"rank" = fit$rank,
"residuals" = as.matrix(fit$residuals),
- "res.ss" = fit$res.ss,
+ "ResSS" = fit$ResSS,
"smart.prediction" = as.list(fit$smart.prediction),
"terms" = list(terms = mt))
diff --git a/R/vglm.control.q b/R/vglm.control.q
index 24fdd77..a143ce9 100644
--- a/R/vglm.control.q
+++ b/R/vglm.control.q
@@ -11,7 +11,7 @@
"loglikelihood" = FALSE,
"AIC" = TRUE,
"Likelihood" = FALSE,
- "res.ss" = TRUE,
+ "ResSS" = TRUE,
"coefficients" = TRUE)
diff --git a/R/vglm.fit.q b/R/vglm.fit.q
index a5ec8fa..60dfe32 100644
--- a/R/vglm.fit.q
+++ b/R/vglm.fit.q
@@ -471,7 +471,7 @@ vglm.fit <-
iter = iter,
misc = misc,
post = post,
- res.ss = tfit$res.ss,
+ ResSS = tfit$ResSS,
x = x,
y = y)),
vclass = slot(family, "vfamily"))
diff --git a/R/vlm.R b/R/vlm.R
index f905390..9ad719a 100644
--- a/R/vlm.R
+++ b/R/vlm.R
@@ -96,7 +96,7 @@ vlm <- function(formula,
fit <- vlm.wfit(xmat = x, zmat = y, Hlist = Hlist, wz = wz, U = NULL,
matrix.out = FALSE, is.vlmX = FALSE,
- res.ss = TRUE, qr = qr.arg,
+ ResSS = TRUE, qr = qr.arg,
x.ret = TRUE, offset = offset)
ncol.X.vlm <- fit$rank
@@ -148,7 +148,7 @@ vlm <- function(formula,
"coefficients" = fit$coefficients,
"constraints" = fit$constraints,
"control" = control,
- "criterion" = list(deviance = fit$res.ss),
+ "criterion" = list(deviance = fit$ResSS),
"dispersion" = 1,
"df.residual" = fit$df.residual,
"df.total" = n*M,
@@ -159,7 +159,7 @@ vlm <- function(formula,
"R" = fit$R,
"rank" = fit$rank,
"residuals" = as.matrix(fit$residuals),
- "res.ss" = fit$res.ss,
+ "ResSS" = fit$ResSS,
"smart.prediction" = as.list(fit$smart.prediction),
"terms" = list(terms = mt))
diff --git a/R/vlm.wfit.q b/R/vlm.wfit.q
index 37935e3..23cbd5d 100644
--- a/R/vlm.wfit.q
+++ b/R/vlm.wfit.q
@@ -12,10 +12,10 @@
vlm.wfit <-
function(xmat, zmat, Hlist, wz = NULL, U = NULL,
- matrix.out = FALSE, is.vlmX = FALSE, res.ss = TRUE, qr = FALSE,
+ matrix.out = FALSE, is.vlmX = FALSE, ResSS = TRUE, qr = FALSE,
x.ret = FALSE,
offset = NULL,
- omit.these = NULL, only.res.ss = FALSE,
+ omit.these = NULL, only.ResSS = FALSE,
ncolx = if (matrix.out && is.vlmX) {
stop("need argument 'ncolx'")
} else {
@@ -29,7 +29,7 @@ vlm.wfit <-
zmat <- as.matrix(zmat)
n <- nrow(zmat)
M <- ncol(zmat)
- if (!only.res.ss) {
+ if (!only.ResSS) {
contrast.save <- attr(xmat, "contrasts")
znames <- dimnames(zmat)[[2]]
}
@@ -73,10 +73,10 @@ vlm.wfit <-
ans <- lm.fit(X.vlm, y = z.vlm, ...)
- if (res.ss) {
- ans$res.ss <- sum(ans$resid^2)
- if (only.res.ss)
- return(list(res.ss = ans$res.ss))
+ if (ResSS) {
+ ans$ResSS <- sum(ans$resid^2)
+ if (only.ResSS)
+ return(list(ResSS = ans$ResSS))
}
if (length(omit.these) && any(omit.these)) {
@@ -169,8 +169,8 @@ print.vlm.wfit <- function(x, ...) {
}
cat("\nDegrees of Freedom:", n*M, "Total;", rdf, "Residual\n")
- if (!is.null(x$res.ss)) {
- cat("Residual Sum of Squares:", format(x$res.ss), "\n")
+ if (!is.null(x$ResSS)) {
+ cat("Residual Sum of Squares:", format(x$ResSS), "\n")
}
invisible(x)
diff --git a/inst/doc/categoricalVGAM.pdf b/inst/doc/categoricalVGAM.pdf
index 73b83fa..f5a2faf 100644
Binary files a/inst/doc/categoricalVGAM.pdf and b/inst/doc/categoricalVGAM.pdf differ
diff --git a/man/ParetoUC.Rd b/man/ParetoUC.Rd
index 1b299ac..7aff0d5 100644
--- a/man/ParetoUC.Rd
+++ b/man/ParetoUC.Rd
@@ -20,7 +20,11 @@ rpareto(n, scale = 1, shape)
\arguments{
\item{x, q}{vector of quantiles.}
\item{p}{vector of probabilities.}
- \item{n}{number of observations. Must be a single positive integer. }
+ \item{n}{number of observations.
+ Same as in \code{\link[stats]{runif}}.
+
+
+ }
\item{scale, shape}{the \eqn{\alpha}{alpha} and \eqn{k} parameters.}
\item{log}{
Logical.
diff --git a/man/SURff.Rd b/man/SURff.Rd
index 99a9ead..64545d0 100644
--- a/man/SURff.Rd
+++ b/man/SURff.Rd
@@ -185,7 +185,7 @@ df.residual(zef1, type = "lm")
mle1 <- vglm(cbind(invest.g, invest.w) ~
capital.g + value.g + capital.w + value.w,
- SURff(mle.normal = TRUE, divisor = "n-max"),
+ SURff(mle.normal = TRUE),
epsilon = 1e-11,
data = gew, trace = TRUE, constraints = clist)
round(coef(mle1, matrix = TRUE), digits = 4) # MLE
@@ -195,3 +195,9 @@ round(sqrt(diag(vcov(mle1))), digits = 4) # SEs
% R documentation directory.
\keyword{models}
\keyword{regression}
+
+
+
+% Prior to 20141108:
+% SURff(mle.normal = TRUE, divisor = "n-max"),
+
diff --git a/man/bifgmcopUC.Rd b/man/bifgmcopUC.Rd
index 8485060..b9ecffb 100644
--- a/man/bifgmcopUC.Rd
+++ b/man/bifgmcopUC.Rd
@@ -19,7 +19,11 @@ rbifgmcop(n, apar)
\arguments{
\item{x1, x2, q1, q2}{vector of quantiles.}
\item{n}{number of observations.
- Must be a positive integer of length 1.}
+ Same as in \code{\link[stats]{runif}}.
+
+
+
+ }
\item{apar}{the association parameter.}
\item{log}{
Logical.
diff --git a/man/bifrankcopUC.Rd b/man/bifrankcopUC.Rd
index 0eb198d..d37df1c 100644
--- a/man/bifrankcopUC.Rd
+++ b/man/bifrankcopUC.Rd
@@ -17,7 +17,10 @@ rbifrankcop(n, apar)
\arguments{
\item{x1, x2, q1, q2}{vector of quantiles.}
\item{n}{number of observations.
- Must be a positive integer of length 1.}
+ Same as in \code{\link[stats]{runif}}.
+
+
+ }
\item{apar}{the positive association parameter. }
\item{log}{
Logical.
diff --git a/man/binom2.orUC.Rd b/man/binom2.orUC.Rd
index 39af990..4bd7c51 100644
--- a/man/binom2.orUC.Rd
+++ b/man/binom2.orUC.Rd
@@ -24,9 +24,10 @@ dbinom2.or(mu1,
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{n}{
- number of observations. Must be a single positive integer.
+ number of observations.
+ Same as in \code{\link[stats]{runif}}.
The arguments \code{mu1}, \code{mu2}, \code{oratio} are recycled to
- length \code{n}.
+ this value.
}
diff --git a/man/binom2.rhoUC.Rd b/man/binom2.rhoUC.Rd
index 0bc8ae1..b397787 100644
--- a/man/binom2.rhoUC.Rd
+++ b/man/binom2.rhoUC.Rd
@@ -24,9 +24,12 @@ dbinom2.rho(mu1,
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{n}{
- number of observations. Must be a single positive integer.
+ number of observations.
+ Same as in \code{\link[stats]{runif}}.
The arguments \code{mu1}, \code{mu2}, \code{rho} are recycled to
- length \code{n}.
+ this value.
+
+
}
\item{mu1, mu2}{
diff --git a/man/biplackettcopUC.Rd b/man/biplackettcopUC.Rd
index 0f4722b..ac00385 100644
--- a/man/biplackettcopUC.Rd
+++ b/man/biplackettcopUC.Rd
@@ -17,7 +17,10 @@ rbiplackcop(n, oratio)
\arguments{
\item{x1, x2, q1, q2}{vector of quantiles.}
\item{n}{number of observations.
- Must be a positive integer of length 1.}
+ Same as in \code{\link[stats]{runif}}.
+
+
+ }
\item{oratio}{the positive odds ratio \eqn{\psi}{psi}.}
\item{log}{
Logical.
diff --git a/man/cardUC.Rd b/man/cardUC.Rd
index d3fafc6..368847c 100644
--- a/man/cardUC.Rd
+++ b/man/cardUC.Rd
@@ -21,7 +21,11 @@ rcard(n, mu, rho, ...)
\arguments{
\item{x, q}{vector of quantiles.}
\item{p}{vector of probabilities.}
- \item{n}{number of observations. Must be a single positive integer. }
+ \item{n}{number of observations.
+ Same as in \code{\link[stats]{runif}}.
+
+
+ }
\item{mu, rho}{
See \code{\link{cardioid}} for more information.
diff --git a/man/frechet.Rd b/man/frechet.Rd
index fc938d2..ef2b8c9 100644
--- a/man/frechet.Rd
+++ b/man/frechet.Rd
@@ -8,18 +8,22 @@
Maximum likelihood estimation of the 2-parameter
Frechet distribution.
+
% and 3-parameter
+
}
\usage{
frechet(location = 0, lscale = "loge", lshape = logoff(offset = -2),
iscale = NULL, ishape = NULL, nsimEIM = 250, zero = NULL)
+}
%frechet3(anchor = NULL, ldifference = "loge", lscale = "loge",
% lshape = "loglog",
% ilocation = NULL, iscale = NULL, ishape = NULL,
% zero = NULL, effpos = .Machine$double.eps^0.75)
-}
%- maybe also 'usage' for other objects documented here.
+
+
\arguments{
\item{location}{
Numeric. Location parameter.
@@ -94,6 +98,7 @@ frechet(location = 0, lscale = "loge", lshape = logoff(offset = -2),
% Note that the \code{\link{loglog}} link ensures \eqn{s > 1}.
+
% whereas \code{frechet3} estimates it.
% Estimating \eqn{a} well requires a lot of data and
% a good choice of \code{ilocation} will help speed up convergence.
@@ -104,6 +109,7 @@ frechet(location = 0, lscale = "loge", lshape = logoff(offset = -2),
% the estimate is out of range (i.e., greater than \code{min(y)}).
+
}
\value{
An object of class \code{"vglmff"} (see \code{\link{vglmff-class}}).
diff --git a/man/gengammaUC.Rd b/man/gengammaUC.Rd
index b09d2ec..67ba760 100644
--- a/man/gengammaUC.Rd
+++ b/man/gengammaUC.Rd
@@ -22,7 +22,11 @@ rgengamma.stacy(n, scale = 1, d = 1, k = 1)
\arguments{
\item{x, q}{vector of quantiles.}
\item{p}{vector of probabilities.}
- \item{n}{number of observations. Positive integer of length 1.}
+ \item{n}{number of observations.
+ Same as in \code{\link[stats]{runif}}.
+
+
+ }
\item{scale}{the (positive) scale parameter \eqn{b}.}
\item{d, k}{the (positive) parameters \eqn{d} and \eqn{k}.}
\item{log}{
diff --git a/man/gompertzUC.Rd b/man/gompertzUC.Rd
index bb0c583..72930ff 100644
--- a/man/gompertzUC.Rd
+++ b/man/gompertzUC.Rd
@@ -23,7 +23,11 @@ rgompertz(n, scale = 1, shape)
\arguments{
\item{x, q}{vector of quantiles.}
\item{p}{vector of probabilities.}
- \item{n}{number of observations. }
+ \item{n}{number of observations.
+ Same as in \code{\link[stats]{runif}}.
+
+
+ }
\item{log}{
Logical.
If \code{log = TRUE} then the logarithm of the density is returned.
diff --git a/man/gumbelIIUC.Rd b/man/gumbelIIUC.Rd
index efb5a1c..514ef83 100644
--- a/man/gumbelIIUC.Rd
+++ b/man/gumbelIIUC.Rd
@@ -22,7 +22,11 @@ rgumbelII(n, scale = 1, shape)
\arguments{
\item{x, q}{vector of quantiles.}
\item{p}{vector of probabilities.}
- \item{n}{number of observations. }
+ \item{n}{number of observations.
+ Same as in \code{\link[stats]{runif}}.
+
+
+ }
\item{log}{
Logical.
If \code{log = TRUE} then the logarithm of the density is returned.
diff --git a/man/lgammaUC.Rd b/man/lgammaUC.Rd
index a0c809a..0c9d455 100644
--- a/man/lgammaUC.Rd
+++ b/man/lgammaUC.Rd
@@ -23,7 +23,11 @@ rlgamma(n, location = 0, scale = 1, shape = 1)
\arguments{
\item{x, q}{vector of quantiles.}
\item{p}{vector of probabilities.}
- \item{n}{number of observations. Positive integer of length 1.}
+ \item{n}{number of observations.
+ Same as \code{\link[stats]{runif}}.
+
+
+ }
\item{location}{the location parameter \eqn{a}.}
\item{scale}{the (positive) scale parameter \eqn{b}.}
\item{shape}{the (positive) shape parameter \eqn{k}.}
diff --git a/man/lindUC.Rd b/man/lindUC.Rd
index 67aa2e7..d4ecd8a 100644
--- a/man/lindUC.Rd
+++ b/man/lindUC.Rd
@@ -25,6 +25,7 @@ rlind(n, theta)
\item{x, q}{vector of quantiles.}
% \item{p}{vector of probabilities.}
\item{n}{number of observations.
+ Same as in \code{\link[stats]{runif}}.
}
diff --git a/man/linoUC.Rd b/man/linoUC.Rd
index b4d2e43..3ef379f 100644
--- a/man/linoUC.Rd
+++ b/man/linoUC.Rd
@@ -21,7 +21,10 @@ rlino(n, shape1, shape2, lambda = 1)
\item{x, q}{vector of quantiles.}
\item{p}{vector of probabilities.}
\item{n}{number of observations.
- Must be a positive integer of length 1.}
+ Same as in \code{\link[stats]{runif}}.
+
+
+ }
\item{shape1, shape2, lambda}{ see \code{\link{lino}}. }
\item{log}{
Logical.
diff --git a/man/logUC.Rd b/man/logUC.Rd
index 77df5d2..b868b42 100644
--- a/man/logUC.Rd
+++ b/man/logUC.Rd
@@ -28,7 +28,11 @@ rlog(n, prob, Smallno = 1.0e-6)
}
% \item{p}{vector of probabilities.}
- \item{n}{number of observations. A single positive integer.}
+ \item{n}{number of observations.
+ Same as in \code{\link[stats]{runif}}.
+
+
+ }
\item{prob}{
The parameter value \eqn{c} described in in \code{\link{logff}}.
Here it is called \code{prob} because \eqn{0<c<1} is the range.
diff --git a/man/makehamUC.Rd b/man/makehamUC.Rd
index 005a1f0..966b2e0 100644
--- a/man/makehamUC.Rd
+++ b/man/makehamUC.Rd
@@ -23,7 +23,11 @@ rmakeham(n, scale = 1, shape, epsilon = 0)
\arguments{
\item{x, q}{vector of quantiles.}
\item{p}{vector of probabilities.}
- \item{n}{number of observations. }
+ \item{n}{number of observations.
+ Same as in \code{\link[stats]{runif}}.
+
+
+ }
\item{log}{
Logical.
If \code{log = TRUE} then the logarithm of the density is returned.
diff --git a/man/nakagamiUC.Rd b/man/nakagamiUC.Rd
index 7dea8ab..a653bb2 100644
--- a/man/nakagamiUC.Rd
+++ b/man/nakagamiUC.Rd
@@ -21,7 +21,13 @@ rnaka(n, scale = 1, shape, Smallno = 1.0e-6)
\item{x, q}{vector of quantiles.}
\item{p}{vector of probabilities.}
\item{n}{number of observations.
- Must be a positive integer of length 1.}
+ Same as in \code{\link[stats]{runif}}.
+
+
+% Must be a positive integer of length 1.
+
+
+ }
\item{scale, shape}{
arguments for the parameters of the distribution.
See \code{\link{nakagami}} for more details.
diff --git a/man/paretoIVUC.Rd b/man/paretoIVUC.Rd
index 0edbad8..8ae99e1 100644
--- a/man/paretoIVUC.Rd
+++ b/man/paretoIVUC.Rd
@@ -47,7 +47,11 @@ rparetoI(n, scale = 1, shape = 1)
\arguments{
\item{x, q}{vector of quantiles. }
\item{p}{vector of probabilities. }
- \item{n}{number of observations. Must be a single positive integer.
+ \item{n}{number of observations.
+ Same as in \code{\link[stats]{runif}}.
+
+
+% Must be a single positive integer.
}
diff --git a/man/perksUC.Rd b/man/perksUC.Rd
index ea90e47..c8f3bf2 100644
--- a/man/perksUC.Rd
+++ b/man/perksUC.Rd
@@ -22,7 +22,10 @@ rperks(n, scale = 1, shape)
\arguments{
\item{x, q}{vector of quantiles.}
\item{p}{vector of probabilities.}
- \item{n}{number of observations. }
+ \item{n}{number of observations.
+ Same as in \code{\link[stats]{runif}}.
+
+ }
\item{log}{
Logical.
If \code{log = TRUE} then the logarithm of the density is returned.
diff --git a/man/riceUC.Rd b/man/riceUC.Rd
index 51d6cbc..a8b8154 100644
--- a/man/riceUC.Rd
+++ b/man/riceUC.Rd
@@ -27,7 +27,7 @@ rrice(n, sigma, vee)
\item{x, q}{vector of quantiles.}
\item{p}{vector of probabilities.}
\item{n}{number of observations.
- Must be a positive integer of length 1.
+ Same as in \code{\link[stats]{runif}}.
}
\item{vee, sigma}{
diff --git a/man/rrvglm-class.Rd b/man/rrvglm-class.Rd
index 3aa897d..6035ec7 100644
--- a/man/rrvglm-class.Rd
+++ b/man/rrvglm-class.Rd
@@ -147,7 +147,7 @@ Objects can be created by calls to \code{\link{rrvglm}}.
from class \code{ "vlm"}.
The \emph{working} residuals at the final IRLS iteration.
}
- \item{\code{res.ss}:}{
+ \item{\code{ResSS}:}{
Object of class \code{"numeric"},
from class \code{ "vlm"}.
Residual sum of squares at the final IRLS iteration with
diff --git a/man/s.Rd b/man/s.Rd
index da17c17..308de92 100644
--- a/man/s.Rd
+++ b/man/s.Rd
@@ -124,8 +124,8 @@ Vector generalized additive models.
\examples{
# Nonparametric logistic regression
-fit <- vgam(agaaus ~ s(altitude, df = 2), binomialff, data = hunua)
-\dontrun{ plot(fit, se = TRUE) }
+fit1 <- vgam(agaaus ~ s(altitude, df = 2), binomialff, data = hunua)
+\dontrun{ plot(fit1, se = TRUE) }
# Bivariate logistic model with artificial data
nn <- 300
@@ -133,11 +133,14 @@ bdata <- data.frame(x1 = runif(nn), x2 = runif(nn))
bdata <- transform(bdata,
y1 = rbinom(nn, size = 1, prob = logit(sin(2 * x2), inverse = TRUE)),
y2 = rbinom(nn, size = 1, prob = logit(sin(2 * x2), inverse = TRUE)))
-fit <- vgam(cbind(y1, y2) ~ x1 + s(x2, 3), trace = TRUE,
- binom2.or(exchangeable = TRUE ~ s(x2, 3)), data = bdata)
-coef(fit, matrix = TRUE) # Hard to interpret
-\dontrun{ plot(fit, se = TRUE, which.term = 2, scol = "blue") }
+fit2 <- vgam(cbind(y1, y2) ~ x1 + s(x2, 3), trace = TRUE,
+ binom2.or(exchangeable = TRUE), data = bdata)
+coef(fit2, matrix = TRUE) # Hard to interpret
+\dontrun{ plot(fit2, se = TRUE, which.term = 2, scol = "blue") }
}
\keyword{models}
\keyword{regression}
\keyword{smooth}
+
+% binom2.or(exchangeable = TRUE ~ s(x2, 3))
+
diff --git a/man/slashUC.Rd b/man/slashUC.Rd
index 1160b50..2efb709 100644
--- a/man/slashUC.Rd
+++ b/man/slashUC.Rd
@@ -20,6 +20,8 @@ rslash(n, mu = 0, sigma = 1)
\item{x, q}{vector of quantiles.}
\item{n}{
Same as \code{\link[stats]{runif}}.
+
+
% number of observations. Must be a single positive integer.
diff --git a/man/tikuvUC.Rd b/man/tikuvUC.Rd
index 59bd0c0..93e69ce 100644
--- a/man/tikuvUC.Rd
+++ b/man/tikuvUC.Rd
@@ -22,7 +22,10 @@ rtikuv(n, d, mean = 0, sigma = 1, Smallno = 1.0e-6)
\item{x, q}{vector of quantiles.}
\item{p}{vector of probabilities.}
\item{n}{number of observations.
- Must be a positive integer of length 1.}
+ Same as in \code{\link[stats]{runif}}.
+
+
+ }
\item{d, mean, sigma }{
arguments for the parameters of the distribution.
See \code{\link{tikuv}} for more details.
diff --git a/man/vgam-class.Rd b/man/vgam-class.Rd
index e083016..dd73aca 100644
--- a/man/vgam-class.Rd
+++ b/man/vgam-class.Rd
@@ -132,15 +132,21 @@ Numerical rank of the fitted model.
from class \code{ "vlm"}.
The \emph{working} residuals at the final IRLS iteration.
}
- \item{\code{res.ss}:}{Object of class \code{"numeric"},
+ \item{\code{ResSS}:}{Object of class \code{"numeric"},
from class \code{ "vlm"}.
Residual sum of squares at the final IRLS iteration with
the adjusted dependent vectors and weight matrices.
+
+
+
}
\item{\code{smart.prediction}:}{Object of class
\code{"list"}, from class \code{ "vlm"}.
A list of data-dependent parameters (if any)
that are used by smart prediction.
+
+
+
}
\item{\code{terms}:}{Object of class \code{"list"},
from class \code{ "vlm"}.
diff --git a/man/vglm-class.Rd b/man/vglm-class.Rd
index 97ae6fc..63453c2 100644
--- a/man/vglm-class.Rd
+++ b/man/vglm-class.Rd
@@ -120,7 +120,7 @@ Numerical rank of the fitted model.
from class \code{ "vlm"}.
The \emph{working} residuals at the final IRLS iteration.
}
- \item{\code{res.ss}:}{Object of class \code{"numeric"},
+ \item{\code{ResSS}:}{Object of class \code{"numeric"},
from class \code{ "vlm"}.
Residual sum of squares at the final IRLS iteration with
the adjusted dependent vectors and weight matrices.
diff --git a/man/vglm.Rd b/man/vglm.Rd
index b4f2881..49009c1 100644
--- a/man/vglm.Rd
+++ b/man/vglm.Rd
@@ -314,7 +314,7 @@ vglm(formula, family, data = list(), weights = NULL, subset = NULL,
\item{R}{the \bold{R} matrix in the QR decomposition used in the fitting.}
\item{rank}{numerical rank of the fitted model.}
\item{residuals}{the \emph{working} residuals at the final IRLS iteration.}
- \item{res.ss}{residual sum of squares at the final IRLS iteration with
+ \item{ResSS}{residual sum of squares at the final IRLS iteration with
the adjusted dependent vectors and weight matrices.}
\item{smart.prediction}{
a list of data-dependent parameters (if any)
diff --git a/man/yulesimonUC.Rd b/man/yulesimonUC.Rd
index 1f7cce0..78be631 100644
--- a/man/yulesimonUC.Rd
+++ b/man/yulesimonUC.Rd
@@ -26,7 +26,7 @@ ryules(n, rho)
}
% \item{p}{vector of probabilities.}
- \item{n}{number of observations. A single positive integer.}
+ \item{n}{number of observations. Same as in \code{\link[stats]{runif}}. }
\item{rho}{
See \code{\link{yulesimon}}.
diff --git a/man/zibinomUC.Rd b/man/zibinomUC.Rd
index 42fb198..53f56a1 100644
--- a/man/zibinomUC.Rd
+++ b/man/zibinomUC.Rd
@@ -26,7 +26,7 @@ rzibinom(n, size, prob, pstr0 = 0)
\item{size}{number of trials. It is the \eqn{N} symbol in the formula
given in \code{\link{zibinomial}}. }
\item{prob}{probability of success on each trial. }
- \item{n}{number of observations. Must be a single positive integer. }
+ \item{n}{ Same as in \code{\link[stats]{runif}}. }
\item{log, log.p, lower.tail}{ Arguments that are passed on to
\code{\link[stats:Binomial]{pbinom}}.}
\item{pstr0}{
diff --git a/man/zigeomUC.Rd b/man/zigeomUC.Rd
index 9a8dbba..abfbe58 100644
--- a/man/zigeomUC.Rd
+++ b/man/zigeomUC.Rd
@@ -23,7 +23,7 @@ rzigeom(n, prob, pstr0 = 0)
\item{x, q}{vector of quantiles.}
\item{p}{vector of probabilities.}
\item{prob}{see \code{\link[stats]{dgeom}}.}
- \item{n}{number of observations. }
+ \item{n}{ Same as in \code{\link[stats]{runif}}. }
\item{pstr0}{
Probability of structural zero (ignoring the geometric distribution),
called \eqn{\phi}{phi}. The default value corresponds
diff --git a/man/zinegbinUC.Rd b/man/zinegbinUC.Rd
index 470f71e..5f94c6d 100644
--- a/man/zinegbinUC.Rd
+++ b/man/zinegbinUC.Rd
@@ -23,7 +23,7 @@ rzinegbin(n, size, prob = NULL, munb = NULL, pstr0 = 0)
\arguments{
\item{x, q}{vector of quantiles.}
\item{p}{vector of probabilities.}
- \item{n}{number of observations. Must be a single positive integer. }
+ \item{n}{ Same as in \code{\link[stats]{runif}}. }
\item{size, prob, munb, log}{
Arguments matching \code{\link[stats:NegBinomial]{dnbinom}}.
The argument \code{munb} corresponds to \code{mu} in
diff --git a/src/vdigami.f b/src/vdigami.f
index 72f4efc..78eb241 100644
--- a/src/vdigami.f
+++ b/src/vdigami.f
@@ -22,14 +22,23 @@ C And a wrapper function written to call this subroutine.
C TMAX is now input.
C Seems to work but more testing is required.
C
+C 20141108; A, C, CP, CPP, DSP, DSPP, DFP, DFPP, F, S, TMAXP etc. now
+C declared, by T. W. Yee.
+C ABS() changed to DABS() too.
+C
+C
DOUBLE PRECISION X, P, GPLOG, GP1LOG, PSIP, PSIP1, PSIDP, PSIDP1
DOUBLE PRECISION TMAX
INTEGER IFAULT
C
+ DOUBLE PRECISION A, AN, B, C, CP, CPC, CPP, DSP, DSPP, DFP, DFPP
+ DOUBLE PRECISION F, PM1, S, S0, XLOG, TERM, TMAXP
+C
C
C
C
C
+ INTEGER I, I2
DOUBLE PRECISION PN(6), D(6), DP(6), DPP(6), ZERO, ONE, TWO
C DATA TMAX/100.0/
DATA E, OFLO, VSMALL/1.D-6, 1.D30, 1.D-30/
@@ -111,9 +120,9 @@ C
DPP(5) = B*DPP(3) + AN*DPP(1) + TWO*(TERM*DP(1) - DP(3))
DPP(6) = B*DPP(4) + AN*DPP(2) + TWO*(TERM*DP(2) - DP(4))
C
- IF (ABS(PN(6)) .LT. VSMALL) GO TO 35
+ IF (DABS(PN(6)) .LT. VSMALL) GO TO 35
S = PN(5) / PN(6)
- C = ABS(S - S0)
+ C = DABS(S - S0)
IF (C*P .GT. E) GO TO 34
IF (C .LE. E*S) GO TO 42
C
@@ -126,7 +135,7 @@ C
36 CONTINUE
C
IF (TERM .GT. TMAX) GO TO 1001
- IF (ABS(PN(5)) .LT. OFLO) GO TO 32
+ IF (DABS(PN(5)) .LT. OFLO) GO TO 32
DO 41 I = 1, 4
DP(I) = DP(I) / OFLO
DPP(I) = DPP(I) / OFLO
diff --git a/src/vgam3.c b/src/vgam3.c
index 926e940..f4dc4da 100644
--- a/src/vgam3.c
+++ b/src/vgam3.c
@@ -154,7 +154,7 @@ void Free_fapc0tnbewg7qruh(double *wkumc9idWrk1,
void F77_NAME(vdigami)(double*, double*, double*,
double*, double*, double*,
double*, double*, double*,
- double*, int*);
+ int*, double*);
void VGAM_C_vdigami(double d[], double x[], double p[],
double gplog[], double gp1log[], double psip[],
@@ -226,11 +226,12 @@ void VGAM_C_vdigami(double d[], double x[], double p[],
+
for (ayfnwr1v = 0; ayfnwr1v < *f8yswcat; ayfnwr1v++) {
F77_CALL(vdigami)(d, x, p,
gplog, gp1log, psip,
psip1, psidp, psidp1,
- tmax, ifault);
+ ifault, tmax);
d += 6;
x++;
p++;
--
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