Merge branch 'main' into joss-paper-draft

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: report
 Type: Package
 Title: Automated Reporting of Results and Statistical Models
-Version: 0.5.8
+Version: 0.5.8.1
 Authors@R:
     c(person(given = "Dominique",
              family = "Makowski",
@@ -55,11 +55,11 @@ BugReports: https://github.com/easystats/report/issues
 Depends:
     R (>= 3.6)
 Imports:
-    bayestestR (>= 0.13.1),
+    bayestestR (>= 0.13.2),
     effectsize (>= 0.8.6),
-    insight (>= 0.19.7),
-    parameters (>= 0.21.3),
-    performance (>= 0.10.8),
+    insight (>= 0.19.8),
+    parameters (>= 0.21.5),
+    performance (>= 0.10.9),
     datawizard (>= 0.9.1),
     stats,
     tools,
@@ -76,12 +76,12 @@ Suggests:
     survival,
     modelbased,
     emmeans,
-    testthat
+    testthat (>= 3.2.1)
 VignetteBuilder:
     knitr
 Encoding: UTF-8
 Language: en-US
-RoxygenNote: 7.2.3.9000
+RoxygenNote: 7.3.1
 Config/testthat/edition: 3
 Config/Needs/website:
     rstudio/bslib,

tests/testthat/_snaps/windows/report.brmsfit.md

@@ -4,6 +4,41 @@
       report(model, verbose = FALSE)
     Message
       Start sampling
+      Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
+      Chain 1 Exception: normal_id_glm_lpdf: Scale vector is 0, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62)
+      Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
+      Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
+      Chain 1 
+      Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
+      Chain 2 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62)
+      Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
+      Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
+      Chain 2 
+      Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
+      Chain 2 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62)
+      Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
+      Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
+      Chain 2 
+      Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
+      Chain 3 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62)
+      Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
+      Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
+      Chain 3 
+      Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
+      Chain 3 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62)
+      Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
+      Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
+      Chain 3 
+      Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
+      Chain 3 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62)
+      Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
+      Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
+      Chain 3 
+      Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
+      Chain 3 Exception: normal_id_glm_lpdf: Scale vector is inf, but must be positive finite! (in 'C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/model-12d437f47a61.stan', line 35, column 4 to column 62)
+      Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
+      Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
+      Chain 3 
     Output
       We fitted a Bayesian linear model (estimated using MCMC sampling with 4 chains
       of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
@@ -12,18 +47,18 @@
       is substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
       model:
       
-        - The effect of b Intercept (Median = 19.74, 95% CI [9.45, 32.02]) has a 99.83%
-      probability of being positive (> 0), 99.83% of being significant (> 0.30), and
-      99.67% of being large (> 1.81). The estimation successfully converged (Rhat =
-      1.000) but the indices are unreliable (ESS = 522)
-        - The effect of b qsec (Median = 0.92, 95% CI [0.34, 1.47]) has a 99.83%
-      probability of being positive (> 0), 98.17% of being significant (> 0.30), and
-      0.17% of being large (> 1.81). The estimation successfully converged (Rhat =
-      1.002) but the indices are unreliable (ESS = 521)
-        - The effect of b wt (Median = -5.09, 95% CI [-6.06, -4.09]) has a 100.00%
+        - The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67%
+      probability of being positive (> 0), 99.67% of being significant (> 0.30), and
+      99.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 343)
+        - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00%
+      probability of being positive (> 0), 99.17% of being significant (> 0.30), and
+      0.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 345)
+        - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00%
       probability of being negative (< 0), 100.00% of being significant (< -0.30),
       and 100.00% of being large (< -1.81). The estimation successfully converged
-      (Rhat = 0.997) but the indices are unreliable (ESS = 543)
+      (Rhat = 0.999) but the indices are unreliable (ESS = 586)
       
       Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
       framework, we report the median of the posterior distribution and its 95% CI
@@ -41,18 +76,18 @@
       substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
       model:
       
-        - The effect of b Intercept (Median = 19.74, 95% CI [9.45, 32.02]) has a 99.83%
-      probability of being positive (> 0), 99.83% of being significant (> 0.30), and
-      99.67% of being large (> 1.81). The estimation successfully converged (Rhat =
-      1.000) but the indices are unreliable (ESS = 522)
-        - The effect of b qsec (Median = 0.92, 95% CI [0.34, 1.47]) has a 99.83%
-      probability of being positive (> 0), 98.17% of being significant (> 0.30), and
-      0.17% of being large (> 1.81). The estimation successfully converged (Rhat =
-      1.002) but the indices are unreliable (ESS = 521)
-        - The effect of b wt (Median = -5.09, 95% CI [-6.06, -4.09]) has a 100.00%
+        - The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67%
+      probability of being positive (> 0), 99.67% of being significant (> 0.30), and
+      99.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 343)
+        - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00%
+      probability of being positive (> 0), 99.17% of being significant (> 0.30), and
+      0.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 345)
+        - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00%
       probability of being negative (< 0), 100.00% of being significant (< -0.30),
       and 100.00% of being large (< -1.81). The estimation successfully converged
-      (Rhat = 0.997) but the indices are unreliable (ESS = 543)
+      (Rhat = 0.999) but the indices are unreliable (ESS = 586)
       
       Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
       framework, we report the median of the posterior distribution and its 95% CI
@@ -70,18 +105,18 @@
       substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
       model:
       
-        - The effect of b Intercept (Median = 19.74, 95% CI [9.45, 32.02]) has a 99.83%
-      probability of being positive (> 0), 99.83% of being significant (> 0.30), and
-      99.67% of being large (> 1.81). The estimation successfully converged (Rhat =
-      1.000) but the indices are unreliable (ESS = 522)
-        - The effect of b qsec (Median = 0.92, 95% CI [0.34, 1.47]) has a 99.83%
-      probability of being positive (> 0), 98.17% of being significant (> 0.30), and
-      0.17% of being large (> 1.81). The estimation successfully converged (Rhat =
-      1.002) but the indices are unreliable (ESS = 521)
-        - The effect of b wt (Median = -5.09, 95% CI [-6.06, -4.09]) has a 100.00%
+        - The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67%
+      probability of being positive (> 0), 99.67% of being significant (> 0.30), and
+      99.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 343)
+        - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00%
+      probability of being positive (> 0), 99.17% of being significant (> 0.30), and
+      0.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 345)
+        - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00%
       probability of being negative (< 0), 100.00% of being significant (< -0.30),
       and 100.00% of being large (< -1.81). The estimation successfully converged
-      (Rhat = 0.997) but the indices are unreliable (ESS = 543)
+      (Rhat = 0.999) but the indices are unreliable (ESS = 586)
       
       Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
       framework, we report the median of the posterior distribution and its 95% CI
@@ -99,18 +134,18 @@
       substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
       model:
       
-        - The effect of b Intercept (Median = 19.74, 95% CI [9.45, 32.02]) has a 99.83%
-      probability of being positive (> 0), 99.83% of being significant (> 0.30), and
-      99.67% of being large (> 1.81). The estimation successfully converged (Rhat =
-      1.000) but the indices are unreliable (ESS = 522)
-        - The effect of b qsec (Median = 0.92, 95% CI [0.34, 1.47]) has a 99.83%
-      probability of being positive (> 0), 98.17% of being significant (> 0.30), and
-      0.17% of being large (> 1.81). The estimation successfully converged (Rhat =
-      1.002) but the indices are unreliable (ESS = 521)
-        - The effect of b wt (Median = -5.09, 95% CI [-6.06, -4.09]) has a 100.00%
+        - The effect of b Intercept (Median = 19.23, 95% CI [6.80, 31.02]) has a 99.67%
+      probability of being positive (> 0), 99.67% of being significant (> 0.30), and
+      99.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 343)
+        - The effect of b qsec (Median = 0.95, 95% CI [0.41, 1.56]) has a 100.00%
+      probability of being positive (> 0), 99.17% of being significant (> 0.30), and
+      0.33% of being large (> 1.81). The estimation successfully converged (Rhat =
+      0.999) but the indices are unreliable (ESS = 345)
+        - The effect of b wt (Median = -5.02, 95% CI [-6.06, -4.09]) has a 100.00%
       probability of being negative (< 0), 100.00% of being significant (< -0.30),
       and 100.00% of being large (< -1.81). The estimation successfully converged
-      (Rhat = 0.997) but the indices are unreliable (ESS = 543)
+      (Rhat = 0.999) but the indices are unreliable (ESS = 586)
       
       Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT)
       framework, we report the median of the posterior distribution and its 95% CI

tests/testthat/_snaps/windows/report_performance.md

@@ -2,6 +2,10 @@
 
     Code
       report_performance(x5)
+    Message
+      VSCode WebView has restricted access to local file.
+      Opening in external browser...
+      Browsing file:///C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/file12d47a7090f_StanProgress.html
     Output
       The model's explanatory power is substantial (R2 = 0.62, 95% CI [0.53, 0.69],
       adj. R2 = 0.61)
@@ -10,27 +14,43 @@
 
     Code
       summary(report_performance(x5))
+    Message
+      VSCode WebView has restricted access to local file.
+      Opening in external browser...
+      Browsing file:///C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/file12d465985229_StanProgress.html
     Output
       [1] "The model's explanatory power is substantial (R2 = 0.62, adj. R2 = 0.61)"
 
 ---
 
     Code
       report_performance(x6)
+    Message
+      VSCode WebView has restricted access to local file.
+      Opening in external browser...
+      Browsing file:///C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/file12d4560a39d2_StanProgress.html
     Output
       The model's explanatory power is substantial (R2 = 0.54, 95% CI [0.27, 0.77])
 
 ---
 
     Code
       summary(report_performance(x6))
+    Message
+      VSCode WebView has restricted access to local file.
+      Opening in external browser...
+      Browsing file:///C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/file12d452f95d46_StanProgress.html
     Output
       [1] "The model's explanatory power is substantial (R2 = 0.54)"
 
 # report_performance Bayesian 2)
 
     Code
       report_performance(x7)
+    Message
+      VSCode WebView has restricted access to local file.
+      Opening in external browser...
+      Browsing file:///C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/file12d412fbc1_StanProgress.html
     Output
       The model's explanatory power is substantial (R2 = 0.83, 95% CI [0.79, 0.86],
       adj. R2 = 0.83) and the part related to the fixed effects alone (marginal R2)
@@ -40,6 +60,10 @@
 
     Code
       summary(report_performance(x7))
+    Message
+      VSCode WebView has restricted access to local file.
+      Opening in external browser...
+      Browsing file:///C:/Users/DL/AppData/Local/Temp/RtmpERRA9z/file12d426713848_StanProgress.html
     Output
       [1] "The model's explanatory power is substantial (R2 = 0.83, adj. R2 = 0.83) and the part related to the fixed effects alone (marginal R2) is of 0.95"
 

tests/testthat/_snaps/windows/report_s.md

@@ -2,7 +2,7 @@
 
     Code
       report_s(s = 4.2)
-    Message <simpleMessage>
+    Message
       If the test hypothesis (parameter = 0) and all model assumptions were
         true, there is a 5.4% chance of observing this outcome. How weird is
         that? It's hardly more surprising than getting 4 heads in a row with
@@ -12,7 +12,7 @@
 
     Code
       report_s(p = 0.06)
-    Message <simpleMessage>
+    Message
       If the test hypothesis (parameter = 0) and all model assumptions were
         true, there is a 6% chance of observing this outcome. How weird is that?
         It's hardly more surprising than getting 4 heads in a row with fair coin