vignettes/a1_bayesdfa.Rmd
a1_bayesdfa.Rmd## Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
## Running the chains for more iterations may help. See
@@ -844,7 +844,7 @@ DFA model with weights
-Site built with pkgdown 2.0.7.
+Site built with pkgdown 2.0.9.
diff --git a/docs/articles/a1_bayesdfa_files/figure-html/plot-extreme-loadings-1.png b/docs/articles/a1_bayesdfa_files/figure-html/plot-extreme-loadings-1.png
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diff --git a/docs/articles/a2_combining_data.html b/docs/articles/a2_combining_data.html
index f2e6d37..479ff35 100644
--- a/docs/articles/a2_combining_data.html
+++ b/docs/articles/a2_combining_data.html
@@ -6,7 +6,7 @@
Combining data with bayesdfa • bayesdfa
-
+
@@ -102,7 +102,7 @@ Eric J. Ward,
Sean C. Anderson, Mary E. Hunsicker, Mike A. Litzow, Luis A. Damiano,
Mark D. Scheuerell, Elizabeth E. Holmes, Nick Tolimieri
- 2024-02-26
+ 2024-10-18
Source: vignettes/a2_combining_data.Rmd
a2_combining_data.Rmd
@@ -230,7 +230,7 @@ Example
-Site built with pkgdown 2.0.7.
+Site built with pkgdown 2.0.9.
diff --git a/docs/articles/a2_combining_data_files/figure-html/unnamed-chunk-9-1.png b/docs/articles/a2_combining_data_files/figure-html/unnamed-chunk-9-1.png
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diff --git a/docs/articles/a3_covariates.html b/docs/articles/a3_covariates.html
index 0cdf590..7dfa5e2 100644
--- a/docs/articles/a3_covariates.html
+++ b/docs/articles/a3_covariates.html
@@ -6,7 +6,7 @@
Examples of including covariates with bayesdfa • bayesdfa
-
+
@@ -103,7 +103,7 @@ Eric J. Ward,
Sean C. Anderson, Mary E. Hunsicker, Mike A. Litzow, Luis A. Damiano,
Mark D. Scheuerell, Elizabeth E. Holmes, Nick Tolimieri
- 2024-02-26
+ 2024-10-18
Source: vignettes/a3_covariates.Rmd
a3_covariates.Rmd
@@ -265,7 +265,7 @@ Examples – process covariates
-Site built with pkgdown 2.0.7.
+Site built with pkgdown 2.0.9.
diff --git a/docs/articles/a4_smooth.html b/docs/articles/a4_smooth.html
index 945a7c0..d0c6e21 100644
--- a/docs/articles/a4_smooth.html
+++ b/docs/articles/a4_smooth.html
@@ -6,7 +6,7 @@
Examples of fitting smooth trend DFA models • bayesdfa
-
+
@@ -102,7 +102,7 @@ Eric J. Ward,
Sean C. Anderson, Mary E. Hunsicker, Mike A. Litzow, Luis A. Damiano,
Mark D. Scheuerell, Elizabeth E. Holmes, Nick Tolimieri
- 2024-02-26
+ 2024-10-18
Source: vignettes/a4_smooth.Rmd
a4_smooth.Rmd
@@ -212,7 +212,7 @@ Comparing approaches
-Site built with pkgdown 2.0.7.
+Site built with pkgdown 2.0.9.
diff --git a/docs/articles/a5_estimate_process_sigma.html b/docs/articles/a5_estimate_process_sigma.html
index b46ee7b..40b357e 100644
--- a/docs/articles/a5_estimate_process_sigma.html
+++ b/docs/articles/a5_estimate_process_sigma.html
@@ -6,7 +6,7 @@
Estimating process trend variability with bayesdfa • bayesdfa
-
+
@@ -103,7 +103,7 @@ Eric J. Ward,
Sean C. Anderson, Mary E. Hunsicker, Mike A. Litzow, Luis A. Damiano,
Mark D. Scheuerell, Elizabeth E. Holmes, Nick Tolimieri
- 2024-02-26
+ 2024-10-18
Source: vignettes/a5_estimate_process_sigma.Rmd
a5_estimate_process_sigma.Rmd
@@ -373,7 +373,7 @@ Summary
-Site built with pkgdown 2.0.7.
+Site built with pkgdown 2.0.9.
diff --git a/docs/articles/a6_compositional.html b/docs/articles/a6_compositional.html
index e5f4ca1..5c895fb 100644
--- a/docs/articles/a6_compositional.html
+++ b/docs/articles/a6_compositional.html
@@ -6,7 +6,7 @@
Fitting compositional dynamic factor models with bayesdfa • bayesdfa
-
+
@@ -103,7 +103,7 @@ Eric J. Ward,
Sean C. Anderson, Mary E. Hunsicker, Mike A. Litzow, Luis A. Damiano,
Mark D. Scheuerell, Elizabeth E. Holmes, Nick Tolimieri
- 2024-02-26
+ 2024-10-18
Source: vignettes/a6_compositional.Rmd
a6_compositional.Rmd
@@ -239,7 +239,7 @@ 3 - trend model
-Site built with pkgdown 2.0.7.
+Site built with pkgdown 2.0.9.
diff --git a/docs/articles/a6_compositional_files/figure-html/unnamed-chunk-12-1.png b/docs/articles/a6_compositional_files/figure-html/unnamed-chunk-12-1.png
index 65f9814..a258904 100644
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diff --git a/docs/articles/a7_bigdata.html b/docs/articles/a7_bigdata.html
index 85e7a64..d107295 100644
--- a/docs/articles/a7_bigdata.html
+++ b/docs/articles/a7_bigdata.html
@@ -6,7 +6,7 @@
Examples of fitting DFA models with lots of data • bayesdfa
-
+
@@ -103,7 +103,7 @@ Eric J. Ward,
Sean C. Anderson, Mary E. Hunsicker, Mike A. Litzow, Luis A. Damiano,
Mark D. Scheuerell, Elizabeth E. Holmes, Nick Tolimieri
- 2024-02-26
+ 2024-10-18
Source: vignettes/a7_bigdata.Rmd
a7_bigdata.Rmd
@@ -226,7 +226,7 @@ Posterior approximation
-Site built with pkgdown 2.0.7.
+Site built with pkgdown 2.0.9.
diff --git a/docs/articles/index.html b/docs/articles/index.html
index 8fb57af..3cb55d3 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -1,5 +1,5 @@
-Articles • bayesdfa Articles • bayesdfa
@@ -102,7 +102,7 @@ All vignettes
diff --git a/docs/authors.html b/docs/authors.html
index b3a1bfe..87d9fa0 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -1,5 +1,5 @@
-Authors and Citation • bayesdfa Authors and Citation • bayesdfa
@@ -72,7 +72,7 @@
diff --git a/docs/index.html b/docs/index.html
index fef3b4c..39434e9 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -6,21 +6,14 @@
Bayesian Dynamic Factor Analysis (DFA) with Stan • bayesdfa
-
+
-
+
Changelog • bayesdfa Changelog • bayesdfa
@@ -129,7 +129,7 @@ bayesdfa 1.3.2Changed weights argument to ‘inv_var_weights’ and ‘likelihood_weights’ for the glmmTMB/sdmTMB/brms style
@@ -146,7 +146,7 @@ bayesdfa 1.3.3
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 5d8fbd6..91dbdb1 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -1,5 +1,5 @@
-pandoc: 3.1.1
-pkgdown: 2.0.7
+pandoc: 3.1.11
+pkgdown: 2.0.9
pkgdown_sha: ~
articles:
a1_bayesdfa: a1_bayesdfa.html
@@ -9,5 +9,5 @@ articles:
a5_estimate_process_sigma: a5_estimate_process_sigma.html
a6_compositional: a6_compositional.html
a7_bigdata: a7_bigdata.html
-last_built: 2024-02-26T18:38Z
+last_built: 2024-10-18T23:33Z
diff --git a/docs/reference/bayesdfa-package.html b/docs/reference/bayesdfa-package.html
index af1af72..9c02db4 100644
--- a/docs/reference/bayesdfa-package.html
+++ b/docs/reference/bayesdfa-package.html
@@ -1,5 +1,5 @@
-The 'bayesdfa' package. — bayesdfa-package • bayesdfa The 'bayesdfa' package. — bayesdfa-package • bayesdfa
@@ -117,7 +117,7 @@ Author
diff --git a/docs/reference/dfa_cv.html b/docs/reference/dfa_cv.html
index 516c6df..99bb391 100644
--- a/docs/reference/dfa_cv.html
+++ b/docs/reference/dfa_cv.html
@@ -1,5 +1,5 @@
-Apply cross validation to DFA model — dfa_cv • bayesdfa Apply cross validation to DFA model — dfa_cv • bayesdfa
@@ -188,7 +188,7 @@ Examples
diff --git a/docs/reference/dfa_fitted.html b/docs/reference/dfa_fitted.html
index 131fcce..d486680 100644
--- a/docs/reference/dfa_fitted.html
+++ b/docs/reference/dfa_fitted.html
@@ -1,5 +1,5 @@
-Get the fitted values from a DFA as a data frame — dfa_fitted • bayesdfa Get the fitted values from a DFA as a data frame — dfa_fitted • bayesdfa
@@ -117,8 +117,8 @@ Examples
#>
#> SAMPLING FOR MODEL 'dfa' NOW (CHAIN 1).
#> Chain 1:
-#> Chain 1: Gradient evaluation took 0.000138 seconds
-#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.38 seconds.
+#> Chain 1: Gradient evaluation took 0.000137 seconds
+#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.37 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
@@ -143,9 +143,9 @@ Examples
#> Chain 1: Iteration: 45 / 50 [ 90%] (Sampling)
#> Chain 1: Iteration: 50 / 50 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 0.022 seconds (Warm-up)
-#> Chain 1: 0.208 seconds (Sampling)
-#> Chain 1: 0.23 seconds (Total)
+#> Chain 1: Elapsed Time: 0.02 seconds (Warm-up)
+#> Chain 1: 0.169 seconds (Sampling)
+#> Chain 1: 0.189 seconds (Total)
#> Chain 1:
#> Warning: There were 11 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
@@ -318,7 +318,7 @@ Examples
diff --git a/docs/reference/dfa_loadings.html b/docs/reference/dfa_loadings.html
index ca503bb..5860063 100644
--- a/docs/reference/dfa_loadings.html
+++ b/docs/reference/dfa_loadings.html
@@ -1,5 +1,5 @@
-Get the loadings from a DFA as a data frame — dfa_loadings • bayesdfa Get the loadings from a DFA as a data frame — dfa_loadings • bayesdfa
@@ -155,9 +155,9 @@ Examples
#> Chain 1: Iteration: 45 / 50 [ 90%] (Sampling)
#> Chain 1: Iteration: 50 / 50 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 0.032 seconds (Warm-up)
-#> Chain 1: 0.35 seconds (Sampling)
-#> Chain 1: 0.382 seconds (Total)
+#> Chain 1: Elapsed Time: 0.035 seconds (Warm-up)
+#> Chain 1: 0.366 seconds (Sampling)
+#> Chain 1: 0.401 seconds (Total)
#> Chain 1:
#> Warning: There were 1 chains where the estimated Bayesian Fraction of Missing Information was low. See
#> https://mc-stan.org/misc/warnings.html#bfmi-low
@@ -268,7 +268,7 @@ Examples
diff --git a/docs/reference/dfa_trends.html b/docs/reference/dfa_trends.html
index 99ea0db..aacc534 100644
--- a/docs/reference/dfa_trends.html
+++ b/docs/reference/dfa_trends.html
@@ -1,5 +1,5 @@
-Get the trends from a DFA as a data frame — dfa_trends • bayesdfa Get the trends from a DFA as a data frame — dfa_trends • bayesdfa
@@ -139,9 +139,9 @@ Examples
#> Chain 1: Iteration: 45 / 50 [ 90%] (Sampling)
#> Chain 1: Iteration: 50 / 50 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 0.009 seconds (Warm-up)
-#> Chain 1: 0.004 seconds (Sampling)
-#> Chain 1: 0.013 seconds (Total)
+#> Chain 1: Elapsed Time: 0.01 seconds (Warm-up)
+#> Chain 1: 0.005 seconds (Sampling)
+#> Chain 1: 0.015 seconds (Total)
#> Chain 1:
#> Warning: There were 1 chains where the estimated Bayesian Fraction of Missing Information was low. See
#> https://mc-stan.org/misc/warnings.html#bfmi-low
@@ -286,7 +286,7 @@ Examples
diff --git a/docs/reference/find_dfa_trends.html b/docs/reference/find_dfa_trends.html
index 8fe5625..6e558e9 100644
--- a/docs/reference/find_dfa_trends.html
+++ b/docs/reference/find_dfa_trends.html
@@ -1,5 +1,5 @@
-Find the best number of trends according to LOOIC — find_dfa_trends • bayesdfa Find the best number of trends according to LOOIC — find_dfa_trends • bayesdfa Find which chains to invert — find_inverted_chains • bayesdfa Find which chains to invert — find_inverted_chains • bayesdfa Fit multiple models with differing numbers of regimes to trend data — find_regimes • bayesdfa Fit multiple models with differing numbers of regimes to trend data — find_regimes • bayesdfa
@@ -182,8 +182,8 @@ Examples
#>
#> SAMPLING FOR MODEL 'hmm_gaussian' NOW (CHAIN 1).
#> Chain 1:
-#> Chain 1: Gradient evaluation took 0.000123 seconds
-#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.23 seconds.
+#> Chain 1: Gradient evaluation took 0.000128 seconds
+#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.28 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
@@ -208,9 +208,9 @@ Examples
#> Chain 1: Iteration: 45 / 50 [ 90%] (Sampling)
#> Chain 1: Iteration: 50 / 50 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 0.52 seconds (Warm-up)
-#> Chain 1: 0.398 seconds (Sampling)
-#> Chain 1: 0.918 seconds (Total)
+#> Chain 1: Elapsed Time: 0.461 seconds (Warm-up)
+#> Chain 1: 0.36 seconds (Sampling)
+#> Chain 1: 0.821 seconds (Total)
#> Chain 1:
#> Warning: The largest R-hat is NA, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
@@ -439,7 +439,7 @@ Examples
#> log_lik[100] 0.17 0.02 0.09 -0.05 0.13 0.19 0.24 0.28 25 0.96
#> lp__ 114.02 0.15 0.74 112.57 113.57 114.22 114.57 114.94 25 1.09
#>
-#> Samples were drawn using NUTS(diag_e) at Mon Feb 26 10:38:15 2024.
+#> Samples were drawn using NUTS(diag_e) at Fri Oct 18 16:33:38 2024.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
@@ -487,7 +487,7 @@ Examples
diff --git a/docs/reference/find_swans-2.png b/docs/reference/find_swans-2.png
index f69969e..f72ad96 100644
Binary files a/docs/reference/find_swans-2.png and b/docs/reference/find_swans-2.png differ
diff --git a/docs/reference/find_swans-3.png b/docs/reference/find_swans-3.png
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diff --git a/docs/reference/find_swans.html b/docs/reference/find_swans.html
index 0fd2387..53a7c92 100644
--- a/docs/reference/find_swans.html
+++ b/docs/reference/find_swans.html
@@ -1,5 +1,5 @@
-Find outlying "black swan" jumps in trends — find_swans • bayesdfa Find outlying "black swan" jumps in trends — find_swans • bayesdfa
@@ -127,8 +127,8 @@ Examples
#>
#> SAMPLING FOR MODEL 'dfa' NOW (CHAIN 1).
#> Chain 1:
-#> Chain 1: Gradient evaluation took 4.1e-05 seconds
-#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.41 seconds.
+#> Chain 1: Gradient evaluation took 4.2e-05 seconds
+#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.42 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
@@ -153,9 +153,9 @@ Examples
#> Chain 1: Iteration: 45 / 50 [ 90%] (Sampling)
#> Chain 1: Iteration: 50 / 50 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 0.251 seconds (Warm-up)
-#> Chain 1: 0.002 seconds (Sampling)
-#> Chain 1: 0.253 seconds (Total)
+#> Chain 1: Elapsed Time: 0.266 seconds (Warm-up)
+#> Chain 1: 0.003 seconds (Sampling)
+#> Chain 1: 0.269 seconds (Total)
#> Chain 1:
#> Warning: There were 24 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
@@ -325,7 +325,7 @@ Examples
diff --git a/docs/reference/fit_dfa.html b/docs/reference/fit_dfa.html
index 675749c..9b2182b 100644
--- a/docs/reference/fit_dfa.html
+++ b/docs/reference/fit_dfa.html
@@ -1,5 +1,5 @@
-Fit a Bayesian DFA — fit_dfa • bayesdfa Fit a Bayesian DFA — fit_dfa • bayesdfa
@@ -330,8 +330,8 @@ Examples
#>
#> SAMPLING FOR MODEL 'dfa' NOW (CHAIN 1).
#> Chain 1:
-#> Chain 1: Gradient evaluation took 3.6e-05 seconds
-#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.36 seconds.
+#> Chain 1: Gradient evaluation took 3.7e-05 seconds
+#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.37 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
@@ -356,9 +356,9 @@ Examples
#> Chain 1: Iteration: 45 / 50 [ 90%] (Sampling)
#> Chain 1: Iteration: 50 / 50 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 0.011 seconds (Warm-up)
-#> Chain 1: 0.103 seconds (Sampling)
-#> Chain 1: 0.114 seconds (Total)
+#> Chain 1: Elapsed Time: 0.012 seconds (Warm-up)
+#> Chain 1: 0.108 seconds (Sampling)
+#> Chain 1: 0.12 seconds (Total)
#> Chain 1:
#> Warning: There were 16 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
@@ -538,7 +538,7 @@ Examples
diff --git a/docs/reference/fit_regimes.html b/docs/reference/fit_regimes.html
index 520d151..ab53f00 100644
--- a/docs/reference/fit_regimes.html
+++ b/docs/reference/fit_regimes.html
@@ -1,5 +1,5 @@
-Fit models with differing numbers of regimes to trend data — fit_regimes • bayesdfa Fit models with differing numbers of regimes to trend data — fit_regimes • bayesdfa
@@ -133,8 +133,8 @@ Examples
#>
#> SAMPLING FOR MODEL 'regime_1' NOW (CHAIN 1).
#> Chain 1:
-#> Chain 1: Gradient evaluation took 7e-06 seconds
-#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.
+#> Chain 1: Gradient evaluation took 8e-06 seconds
+#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
@@ -159,9 +159,9 @@ Examples
#> Chain 1: Iteration: 45 / 50 [ 90%] (Sampling)
#> Chain 1: Iteration: 50 / 50 [100%] (Sampling)
#> Chain 1:
-#> Chain 1: Elapsed Time: 0 seconds (Warm-up)
-#> Chain 1: 0 seconds (Sampling)
-#> Chain 1: 0 seconds (Total)
+#> Chain 1: Elapsed Time: 0.001 seconds (Warm-up)
+#> Chain 1: 0.001 seconds (Sampling)
+#> Chain 1: 0.002 seconds (Total)
#> Chain 1:
#> Warning: The largest R-hat is 1.52, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
@@ -383,7 +383,7 @@ Examples
#> log_lik[100] 0.16 0.03 0.13 -0.05 0.06 0.15 0.27 0.38 17 0.97
#> lp__ 113.34 0.44 1.19 111.43 112.13 113.48 114.37 114.92 7 1.50
#>
-#> Samples were drawn using NUTS(diag_e) at Mon Feb 26 10:38:22 2024.
+#> Samples were drawn using NUTS(diag_e) at Fri Oct 18 16:33:44 2024.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
@@ -424,7 +424,7 @@ Examples
diff --git a/docs/reference/hmm_init.html b/docs/reference/hmm_init.html
index 107aa87..a51f7f6 100644
--- a/docs/reference/hmm_init.html
+++ b/docs/reference/hmm_init.html
@@ -1,5 +1,5 @@
-Create initial values for the HMM model. — hmm_init • bayesdfa Create initial values for the HMM model. — hmm_init • bayesdfa
@@ -113,7 +113,7 @@ Value
diff --git a/docs/reference/index.html b/docs/reference/index.html
index 8319ea0..5e4bc35 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -1,5 +1,5 @@
-Function reference • bayesdfa Function reference • bayesdfa
@@ -187,7 +187,7 @@ Plotting