[Vegan-commits] r1914 - in pkg/vegan: . inst man tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Thu Sep 29 18:11:27 CEST 2011
Author: jarioksa
Date: 2011-09-29 18:11:26 +0200 (Thu, 29 Sep 2011)
New Revision: 1914
Modified:
pkg/vegan/DESCRIPTION
pkg/vegan/inst/ChangeLog
pkg/vegan/man/add1.cca.Rd
pkg/vegan/man/deviance.cca.Rd
pkg/vegan/man/envfit.Rd
pkg/vegan/man/ordistep.Rd
pkg/vegan/tests/Examples/vegan-Ex.Rout.save
Log:
some time saving in examples
Modified: pkg/vegan/DESCRIPTION
===================================================================
--- pkg/vegan/DESCRIPTION 2011-09-28 16:08:00 UTC (rev 1913)
+++ pkg/vegan/DESCRIPTION 2011-09-29 16:11:26 UTC (rev 1914)
@@ -1,7 +1,7 @@
Package: vegan
Title: Community Ecology Package
-Version: 2.1-1
-Date: September 20, 2011
+Version: 2.1-2
+Date: September 29, 2011
Author: Jari Oksanen, F. Guillaume Blanchet, Roeland Kindt, Pierre Legendre,
Peter R. Minchin, R. B. O'Hara, Gavin L. Simpson, Peter Solymos,
M. Henry H. Stevens, Helene Wagner
Modified: pkg/vegan/inst/ChangeLog
===================================================================
--- pkg/vegan/inst/ChangeLog 2011-09-28 16:08:00 UTC (rev 1913)
+++ pkg/vegan/inst/ChangeLog 2011-09-29 16:11:26 UTC (rev 1914)
@@ -2,6 +2,13 @@
VEGAN DEVEL VERSIONS at http://r-forge.r-project.org/
+Version 2.1-2 (opened September 29, 20119
+
+ * examples: cut donw some excessively time consuming examples.
+ Profiling of all vegan examples showed that 25% of total time was
+ spent in anova.cca, and 12.6% in ordistep, but they probably are
+ sufficiently documented more quickly.
+
Version 2.1-1 (opened September 20, 2011)
* oecosimu: the 'comm' argument can be either 1) community data,
Modified: pkg/vegan/man/add1.cca.Rd
===================================================================
--- pkg/vegan/man/add1.cca.Rd 2011-09-28 16:08:00 UTC (rev 1913)
+++ pkg/vegan/man/add1.cca.Rd 2011-09-29 16:11:26 UTC (rev 1914)
@@ -72,8 +72,10 @@
data(dune.env)
## Automatic model building based on AIC but with permutation tests
step(cca(dune ~ 1, dune.env), reformulate(names(dune.env)), test="perm")
-## The same, but based on permutation P-values
+## see ?ordistep to do the same, but based on permutation P-values
+\dontrun{
ordistep(cca(dune ~ 1, dune.env), reformulate(names(dune.env)), perm.max=200)
+}
## Manual model building
## -- define the maximal model for scope
mbig <- rda(dune ~ ., dune.env)
Modified: pkg/vegan/man/deviance.cca.Rd
===================================================================
--- pkg/vegan/man/deviance.cca.Rd 2011-09-28 16:08:00 UTC (rev 1913)
+++ pkg/vegan/man/deviance.cca.Rd 2011-09-29 16:11:26 UTC (rev 1914)
@@ -91,15 +91,9 @@
data(dune.env)
chisq.test(dune)
deviance(cca(dune))
-# Backward elimination from a complete model "dune ~ ."
-ord <- cca(dune ~ ., dune.env)
-ord
-step(ord)
# Stepwise selection (forward from an empty model "dune ~ 1")
+ord <- cca(dune ~ ., dune.env)
step(cca(dune ~ 1, dune.env), scope = formula(ord))
-# ANOVA: added variable + the first left out
-anova(cca(dune ~ Moisture + Management, dune.env), permut=200,
- by = "terms")
}
\keyword{ multivariate }
\keyword{ models }
Modified: pkg/vegan/man/envfit.Rd
===================================================================
--- pkg/vegan/man/envfit.Rd 2011-09-28 16:08:00 UTC (rev 1913)
+++ pkg/vegan/man/envfit.Rd 2011-09-29 16:11:26 UTC (rev 1914)
@@ -187,7 +187,7 @@
data(dune.env)
attach(dune.env)
ord <- cca(dune)
-fit <- envfit(ord ~ Moisture + A1, dune.env)
+fit <- envfit(ord ~ Moisture + A1, dune.env, perm = 0)
plot(ord, type = "n")
ordispider(ord, Moisture, col="skyblue")
points(ord, display = "sites", col = as.numeric(Moisture), pch=16)
Modified: pkg/vegan/man/ordistep.Rd
===================================================================
--- pkg/vegan/man/ordistep.Rd 2011-09-28 16:08:00 UTC (rev 1913)
+++ pkg/vegan/man/ordistep.Rd 2011-09-29 16:11:26 UTC (rev 1914)
@@ -144,8 +144,9 @@
ordistep(mod1, perm.max = 200)
## Example of ordistep, forward
+\dontrun{
ordistep(mod0, scope = formula(mod1), direction="forward", perm.max = 200)
-
+}
### Mite data
data(mite)
data(mite.env)
@@ -159,9 +160,11 @@
step.res$anova # Summary table
## Example of ordiR2step with direction = "forward"
+\dontrun{
step.res <- ordiR2step(mod0, scope = formula(mod1), direction="forward")
step.res$anova # Summary table
}
+}
\keyword{ multivariate }
\keyword{ models }
Modified: pkg/vegan/tests/Examples/vegan-Ex.Rout.save
===================================================================
--- pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2011-09-28 16:08:00 UTC (rev 1913)
+++ pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2011-09-29 16:11:26 UTC (rev 1914)
@@ -434,68 +434,10 @@
CA9 CA10 CA11 CA12 CA13 CA14 CA15 CA16
0.056606 0.046688 0.041926 0.020103 0.014335 0.009917 0.008505 0.008033
-> ## The same, but based on permutation P-values
-> ordistep(cca(dune ~ 1, dune.env), reformulate(names(dune.env)), perm.max=200)
-
-Start: dune ~ 1
-
- Df AIC F N.Perm Pr(>F)
-+ Moisture 3 86.608 2.2536 199 0.005 **
-+ Management 3 86.935 2.1307 199 0.005 **
-+ Manure 4 88.832 1.5251 199 0.025 *
-+ A1 1 87.411 2.1400 199 0.035 *
-+ Use 2 89.134 1.1431 99 0.130
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-
-Step: dune ~ Moisture
-
- Df AIC F N.Perm Pr(>F)
-- Moisture 3 87.657 2.2536 99 0.01 **
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-
- Df AIC F N.Perm Pr(>F)
-+ Management 3 86.813 1.4565 199 0.035 *
-+ Use 2 87.259 1.2760 199 0.095 .
-+ Manure 4 87.342 1.3143 199 0.095 .
-+ A1 1 86.992 1.2624 99 0.170
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-
-Step: dune ~ Moisture + Management
-
- Df AIC F N.Perm Pr(>F)
-- Management 3 86.608 1.4565 199 0.035 *
-- Moisture 3 86.935 1.5518 99 0.020 *
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-
- Df AIC F N.Perm Pr(>F)
-+ A1 1 86.190 1.6817 199 0.09 .
-+ Manure 3 88.430 0.8167 99 0.58
-+ Use 2 88.245 0.7534 99 0.65
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-
-Call: cca(formula = dune ~ Moisture + Management, data = dune.env)
-
- Inertia Proportion Rank
-Total 2.1153 1.0000
-Constrained 1.0024 0.4739 6
-Unconstrained 1.1129 0.5261 13
-Inertia is mean squared contingency coefficient
-
-Eigenvalues for constrained axes:
- CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
-0.44583 0.28869 0.11239 0.07166 0.04937 0.03444
-
-Eigenvalues for unconstrained axes:
- CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
-0.350396 0.152057 0.125084 0.109838 0.092209 0.077107 0.059441 0.047755
- CA9 CA10 CA11 CA12 CA13
-0.036958 0.022266 0.020700 0.010827 0.008252
-
+> ## see ?ordistep to do the same, but based on permutation P-values
+> ## Not run:
+> ##D ordistep(cca(dune ~ 1, dune.env), reformulate(names(dune.env)), perm.max=200)
+> ## End(Not run)
> ## Manual model building
> ## -- define the maximal model for scope
> mbig <- rda(dune ~ ., dune.env)
@@ -505,21 +447,21 @@
> add1(m0, scope=formula(mbig), test="perm")
Df AIC F N.Perm Pr(>F)
<none> 89.620
-A1 1 89.591 1.9217 199 0.055 .
+A1 1 89.591 1.9217 199 0.070 .
Moisture 3 87.707 2.5883 199 0.005 **
Management 3 87.082 2.8400 199 0.005 **
-Use 2 91.032 1.1741 99 0.270
+Use 2 91.032 1.1741 99 0.180
Manure 4 89.232 1.9539 199 0.010 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> m0 <- update(m0, . ~ . + Management)
> add1(m0, scope=formula(mbig), test="perm")
- Df AIC F N.Perm Pr(>F)
-<none> 87.082
-A1 1 87.424 1.2965 99 0.21
-Moisture 3 85.567 1.9764 199 0.03 *
-Use 2 88.284 1.0510 99 0.41
-Manure 3 87.517 1.3902 199 0.07 .
+ Df AIC F N.Perm Pr(>F)
+<none> 87.082
+A1 1 87.424 1.2965 99 0.240
+Moisture 3 85.567 1.9764 199 0.005 **
+Use 2 88.284 1.0510 99 0.430
+Manure 3 87.517 1.3902 199 0.130
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> m0 <- update(m0, . ~ . + Moisture)
@@ -527,16 +469,16 @@
> drop1(m0, test="perm")
Df AIC F N.Perm Pr(>F)
<none> 85.567
-Management 3 87.707 2.1769 199 0.015 *
-Moisture 3 87.082 1.9764 199 0.005 **
+Management 3 87.707 2.1769 199 0.010 **
+Moisture 3 87.082 1.9764 199 0.015 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> add1(m0, scope=formula(mbig), test="perm")
Df AIC F N.Perm Pr(>F)
<none> 85.567
-A1 1 86.220 0.8359 99 0.66
-Use 2 86.842 0.8027 99 0.66
-Manure 3 85.762 1.1225 99 0.31
+A1 1 86.220 0.8359 99 0.72
+Use 2 86.842 0.8027 99 0.77
+Manure 3 85.762 1.1225 99 0.26
>
>
>
@@ -2224,78 +2166,8 @@
> deviance(cca(dune))
[1] 1448.956
-> # Backward elimination from a complete model "dune ~ ."
-> ord <- cca(dune ~ ., dune.env)
-> ord
-Call: cca(formula = dune ~ A1 + Moisture + Management + Use + Manure,
-data = dune.env)
-
- Inertia Proportion Rank
-Total 2.1153 1.0000
-Constrained 1.5032 0.7106 12
-Unconstrained 0.6121 0.2894 7
-Inertia is mean squared contingency coefficient
-Some constraints were aliased because they were collinear (redundant)
-
-Eigenvalues for constrained axes:
- CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8 CCA9 CCA10
-0.46713 0.34102 0.17606 0.15317 0.09528 0.07027 0.05887 0.04993 0.03183 0.02596
- CCA11 CCA12
-0.02282 0.01082
-
-Eigenvalues for unconstrained axes:
- CA1 CA2 CA3 CA4 CA5 CA6 CA7
-0.27237 0.10876 0.08975 0.06305 0.03489 0.02529 0.01798
-
-> step(ord)
-Start: AIC=86.86
-dune ~ A1 + Moisture + Management + Use + Manure
-
- Df AIC
-- Use 2 86.711
-<none> 86.857
-- Management 2 87.470
-- Manure 3 87.819
-- A1 1 88.181
-- Moisture 3 89.179
-
-Step: AIC=86.71
-dune ~ A1 + Moisture + Management + Manure
-
- Df AIC
-- Manure 3 86.190
-- Management 2 86.446
-<none> 86.711
-- Moisture 3 87.873
-- A1 1 88.430
-
-Step: AIC=86.19
-dune ~ A1 + Moisture + Management
-
- Df AIC
-<none> 86.190
-- Moisture 3 86.460
-- A1 1 86.813
-- Management 3 86.992
-Call: cca(formula = dune ~ A1 + Moisture + Management, data = dune.env)
-
- Inertia Proportion Rank
-Total 2.1153 1.0000
-Constrained 1.1392 0.5385 7
-Unconstrained 0.9761 0.4615 12
-Inertia is mean squared contingency coefficient
-
-Eigenvalues for constrained axes:
- CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7
-0.44826 0.30014 0.14995 0.10733 0.05668 0.04335 0.03345
-
-Eigenvalues for unconstrained axes:
- CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
-0.306366 0.131911 0.115157 0.109469 0.077242 0.075754 0.048714 0.037582
- CA9 CA10 CA11 CA12
-0.031058 0.021024 0.012542 0.009277
-
> # Stepwise selection (forward from an empty model "dune ~ 1")
+> ord <- cca(dune ~ ., dune.env)
> step(cca(dune ~ 1, dune.env), scope = formula(ord))
Start: AIC=87.66
dune ~ 1
@@ -2336,19 +2208,6 @@
CA9 CA10 CA11 CA12 CA13 CA14 CA15 CA16
0.056606 0.046688 0.041926 0.020103 0.014335 0.009917 0.008505 0.008033
-> # ANOVA: added variable + the first left out
-> anova(cca(dune ~ Moisture + Management, dune.env), permut=200,
-+ by = "terms")
-Permutation test for cca under reduced model
-Terms added sequentially (first to last)
-
-Model: cca(formula = dune ~ Moisture + Management, data = dune.env)
- Df Chisq F N.Perm Pr(>F)
-Moisture 3 0.6283 2.4465 199 0.005 **
-Management 3 0.3741 1.4565 199 0.025 *
-Residual 13 1.1129
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
>
>
@@ -2684,7 +2543,7 @@
> data(dune.env)
> attach(dune.env)
> ord <- cca(dune)
-> fit <- envfit(ord ~ Moisture + A1, dune.env)
+> fit <- envfit(ord ~ Moisture + A1, dune.env, perm = 0)
> plot(ord, type = "n")
> ordispider(ord, Moisture, col="skyblue")
> points(ord, display = "sites", col = as.numeric(Moisture), pch=16)
@@ -4717,55 +4576,9 @@
>
> ## Example of ordistep, forward
-> ordistep(mod0, scope = formula(mod1), direction="forward", perm.max = 200)
-
-Start: dune ~ 1
-
- Df AIC F N.Perm Pr(>F)
-+ Management 3 87.082 2.8400 199 0.005 **
-+ Moisture 3 87.707 2.5883 199 0.005 **
-+ Manure 4 89.232 1.9539 199 0.010 **
-+ A1 1 89.591 1.9217 199 0.045 *
-+ Use 2 91.032 1.1741 99 0.350
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-
-Step: dune ~ Management
-
- Df AIC F N.Perm Pr(>F)
-+ Moisture 3 85.567 1.9764 199 0.005 **
-+ Manure 3 87.517 1.3902 199 0.095 .
-+ A1 1 87.424 1.2965 99 0.180
-+ Use 2 88.284 1.0510 99 0.380
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-
-Step: dune ~ Management + Moisture
-
- Df AIC F N.Perm Pr(>F)
-+ Manure 3 85.762 1.1225 99 0.32
-+ A1 1 86.220 0.8359 99 0.61
-+ Use 2 86.842 0.8027 99 0.66
-
-Call: rda(formula = dune ~ Management + Moisture, data = dune.env)
-
- Inertia Proportion Rank
-Total 84.1237 1.0000
-Constrained 46.4249 0.5519 6
-Unconstrained 37.6988 0.4481 13
-Inertia is variance
-
-Eigenvalues for constrained axes:
- RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
-21.588 14.075 4.123 3.163 2.369 1.107
-
-Eigenvalues for unconstrained axes:
- PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11
-8.2409 7.1380 5.3547 4.4086 3.1430 2.7697 1.8779 1.7409 0.9517 0.9088 0.6265
- PC12 PC13
-0.3107 0.2273
-
->
+> ## Not run:
+> ##D ordistep(mod0, scope = formula(mod1), direction="forward", perm.max = 200)
+> ## End(Not run)
> ### Mite data
> data(mite)
> data(mite.env)
@@ -4885,95 +4698,10 @@
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> ## Example of ordiR2step with direction = "forward"
-> step.res <- ordiR2step(mod0, scope = formula(mod1), direction="forward")
-Step: R2.adj= 0
-Call: mite.hel ~ 1
-
- R2.adjusted
-<All variables> 0.43670383
-+ WatrCont 0.26084533
-+ Shrub 0.20716190
-+ Topo 0.15205437
-+ Substrate 0.07718348
-+ SubsDens 0.02632468
-<none> 0.00000000
-
- Df AIC F N.Perm Pr(>F)
-+ WatrCont 1 -84.336 25.35 199 0.005 **
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-
-Step: R2.adj= 0.2608453
-Call: mite.hel ~ WatrCont
-
- R2.adjusted
-<All variables> 0.4367038
-+ Shrub 0.3177536
-+ Topo 0.3120057
-+ Substrate 0.3091579
-+ SubsDens 0.3066715
-<none> 0.2608453
-
- Df AIC F N.Perm Pr(>F)
-+ Shrub 2 -88.034 3.836 199 0.005 **
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-
-Step: R2.adj= 0.3177536
-Call: mite.hel ~ WatrCont + Shrub
-
- R2.adjusted
-<All variables> 0.4367038
-+ Substrate 0.3653551
-+ Topo 0.3525851
-+ SubsDens 0.3446967
-<none> 0.3177536
-
- Df AIC F N.Perm Pr(>F)
-+ Substrate 6 -87.768 1.8251 199 0.01 **
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-
-Step: R2.adj= 0.3653551
-Call: mite.hel ~ WatrCont + Shrub + Substrate
-
- R2.adjusted
-<All variables> 0.4367038
-+ Topo 0.4004249
-+ SubsDens 0.3901844
-<none> 0.3653551
-
- Df AIC F N.Perm Pr(>F)
-+ Topo 1 -90.924 4.5095 199 0.005 **
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-
-Step: R2.adj= 0.4004249
-Call: mite.hel ~ WatrCont + Shrub + Substrate + Topo
-
- R2.adjusted
-<All variables> 0.4367038
-+ SubsDens 0.4367038
-<none> 0.4004249
-
- Df AIC F N.Perm Pr(>F)
-+ SubsDens 1 -94.489 4.7999 199 0.005 **
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-
-Step: R2.adj= 0.4367038
-Call: mite.hel ~ WatrCont + Shrub + Substrate + Topo + SubsDens
-
-> step.res$anova # Summary table
- R2.adj Df AIC F N.Perm Pr(>F)
-+ WatrCont 0.26085 1 -84.336 25.3499 199 0.005 **
-+ Shrub 0.31775 2 -88.034 3.8360 199 0.005 **
-+ Substrate 0.36536 6 -87.768 1.8251 199 0.010 **
-+ Topo 0.40042 1 -90.924 4.5095 199 0.005 **
-+ SubsDens 0.43670 1 -94.489 4.7999 199 0.005 **
-<All variables> 0.43670
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+> ## Not run:
+> ##D step.res <- ordiR2step(mod0, scope = formula(mod1), direction="forward")
+> ##D step.res$anova # Summary table
+> ## End(Not run)
>
>
>
@@ -5004,7 +4732,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x10729d470>
+<environment: 0x1075ffb90>
Estimated degrees of freedom:
6.4351 total = 7.435071
@@ -5020,7 +4748,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x1075933c8>
+<environment: 0x107aa9f48>
Estimated degrees of freedom:
6.1039 total = 7.103853
@@ -5176,7 +4904,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x107035b48>
+<environment: 0x10859b828>
Estimated degrees of freedom:
8.9275 total = 9.927492
@@ -5189,7 +4917,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x107723990>
+<environment: 0x1070ec438>
Estimated degrees of freedom:
7.7529 total = 8.75294
@@ -5202,7 +4930,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x107713a00>
+<environment: 0x107471a68>
Estimated degrees of freedom:
8.8962 total = 9.89616
@@ -7464,7 +7192,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x10701aa68>
+<environment: 0x1047b6498>
Estimated degrees of freedom:
2 total = 3
@@ -7940,7 +7668,7 @@
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", pos = 'CheckExEnv'),"\n")
-Time elapsed: 115.862 1.458 121.541 0 0
+Time elapsed: 105.636 1.365 108.878 0 0
> grDevices::dev.off()
null device
1
More information about the Vegan-commits
mailing list