Correcting CRAN errors (2nd try)

Author: gvegayon

DESCRIPTION

@@ -3,8 +3,8 @@ Title: Statistical Inference and Prediction of Annotations in Phylogenetic Trees
 Authors@R: person("George", "Vega Yon", role=c("aut","cre"),
          email="g.vegayon@gmail.com", comment = c(ORCID = "0000-0002-3171-0844"))
 Version: 0.2-1
-Date: 2022-01-01
-Description: aphylo implements a parsimonious evolutionary model to analyze and
+Date: 2022-01-14
+Description: Implements a parsimonious evolutionary model to analyze and
   predict gene-functional annotations in phylogenetic trees as described in Vega
   Yon et al. (2021) <doi:10.1371/journal.pcbi.1007948>. With a focus on
   computational efficiency, aphylo makes it possible to estimate pooled

R/formulas.R

@@ -89,15 +89,15 @@ APHYLO_PARAM_NAMES <- c(
 #' The vector `APHYLO_PARAM_DEFAULT` lists the starting values for the parameters
 #' in the model. The current defaults are:
 #' 
-#' - `psi0`: \Sexpr[results=text]{as.list(aphylo::APHYLO_PARAM_DEFAULT)$psi0}
-#' - `psi1`: \Sexpr[results=text]{as.list(aphylo::APHYLO_PARAM_DEFAULT)$psi1}
-#' - `mu_d0`: \Sexpr[results=text]{as.list(aphylo::APHYLO_PARAM_DEFAULT)$mu_d0}
-#' - `mu_d1`: \Sexpr[results=text]{as.list(aphylo::APHYLO_PARAM_DEFAULT)$mu_d1}
-#' - `mu_s0`: \Sexpr[results=text]{as.list(aphylo::APHYLO_PARAM_DEFAULT)$mu_s0}
-#' - `mu_s1`: \Sexpr[results=text]{as.list(aphylo::APHYLO_PARAM_DEFAULT)$mu_s1}
-#' - `eta0`: \Sexpr[results=text]{as.list(aphylo::APHYLO_PARAM_DEFAULT)$eta0}
-#' - `eta1`: \Sexpr[results=text]{as.list(aphylo::APHYLO_PARAM_DEFAULT)$eta1}
-#' - `Pi`: \Sexpr[results=text]{as.list(aphylo::APHYLO_PARAM_DEFAULT)$Pi}
+#' - `psi0`: 0.10
+#' - `psi1`: 0.05
+#' - `mu_d0`: 0.90
+#' - `mu_d1`: 0.50
+#' - `mu_s0`: 0.10
+#' - `mu_s1`: 0.05
+#' - `eta0`: 1.00
+#' - `eta1`: 1.00
+#' - `Pi`: 0.50
 #' 
 APHYLO_PARAM_DEFAULT <- structure(
   .Data = c(.1, .05, .9, .5, .1, .05, 1.0, 1.0, .5),

inst/tinytest/test-predict-ans0.rds

@@ -24,7 +24,9 @@ ans0 <- c(
 )[with(atree$tree, c(tip.label, node.label))]
 
 # Computing brute-force and using the pre-order
-ans1 <- predict_brute_force(atree, psi, mu_d = mu, mu_s = mu, Pi)
+# ans1 <- predict_brute_force(atree, psi, mu_d = mu, mu_s = mu, Pi)
+# saveRDS(ans1, "inst/tinytest/test-predict-ans1.rds")
+ans1 <- readRDS("test-predict-ans1.rds")
 ans2 <- predict_pre_order(atree, psi, mu_d = mu, mu_s = mu, eta, Pi, loo = FALSE)
 
 
@@ -62,7 +64,9 @@ expect_equivalent(ans1$posterior, ans2[,1])
 set.seed(122331)
 atree <- raphylo(4, psi = psi, mu_d = mu, mu_s = mu, eta = eta, Pi = Pi)
 
-ans0 <- predict_brute_force(atree, psi, mu_d = mu, mu_s = mu, Pi)
+# ans0 <- predict_brute_force(atree, psi, mu_d = mu, mu_s = mu, Pi)
+# saveRDS(ans0, file = "inst/tinytest/test-predict-ans0.rds")
+ans0 <- readRDS("test-predict-ans0.rds")
 ans1 <- predict_pre_order(atree, psi, mu_d = mu, mu_s = mu, eta, Pi, loo = FALSE)
 
 expect_equivalent(ans0$posterior, ans1[,1])