Version update and further vignette optimization.

DESCRIPTION

@@ -14,8 +14,8 @@ Encoding: UTF-8
 LazyData: true
 ByteCompile: true
 Depends:
-    methods (>= 4.0.3),
-    R (>= 4.0.3),
+    methods (>= 4.0.0),
+    R (>= 4.0.0),
     Rcpp (>= 1.0.5)
 Imports:
     circular (>= 0.4.93),
@@ -28,8 +28,9 @@ Imports:
     rstan (>= 2.21.2),
     rstantools (>= 2.1.1),
     mcmcse (>= 1.4.1),
-    stats (>= 4.0.3),
-    utils (>= 4.0.3)
+    scales (>= 1.1.1),
+    stats (>= 4.0.0),
+    utils (>= 4.0.0)
 Suggests: 
     testthat (>= 3.0.0),
     rmarkdown (>= 2.5.0),

vignettes/adaptation_level.Rmd

@@ -50,7 +50,7 @@ group2_part2 <- data %>% filter(group == 2 & part == 2)
 Once the data is prepared we can fit the Bayesian models, the input data comes in the form of three vectors, $x$ stores indexes of the measurements, $y$ subject's responses and $s$ indexes of subjects. Note here that, due to vignette limitations, all fits are built using only one chain, using more chains in parallel is usually more efficient. Also to increase the building speed of vignettes we greatly reduced the amount of iterations, use an appropriate amount of iterations when executing actual analyses!
 
 ```{r, message=FALSE, warning=FALSE, results = 'hide'}
-# priors
+# prior
 beta_prior <- b_prior(family="normal", pars=c(0, 1))
 
 # attach priors to relevant parameters

vignettes/flanker.Rmd

@@ -56,14 +56,22 @@ control_rt$subject <- control_rt$subject - 21
 Now we are ready to fit the Bayesian reaction time model for both groups. The model function requires two parameters -- a vector of reaction times $t$ and the vector of subject indexes $s$. Note here that, due to vignette limitations, all fits are built using only one chain, using more chains in parallel is usually more efficient. Also to increase the building speed of vignettes we greatly reduced the amount of iterations, use an appropriate amount of iterations when executing actual analyses!
 
 ```{r, message=FALSE, warning=FALSE, results = 'hide'}
+# prior
+uniform_prior <- b_prior(family="uniform", pars=c(0, 3))
+
+# attach priors to relevant parameters
+priors <- list(c("mu_m", uniform_prior))
+
 # fit
 rt_control_fit <- b_reaction_time(t=control_rt$rt,
                                   s=control_rt$subject,
+                                  priors=priors,
                                   chains=1, iter=200, warmup=100)
 
 rt_test_fit <- b_reaction_time(t=test_rt$rt,
                                s=test_rt$subject,
-                                  chains=1, iter=200, warmup=100)
+                               priors=priors,
+                               chains=1, iter=200, warmup=100)
 ```
 
 Before we interpret the results, we check MCMC diagnostics and model fit.