diff --git a/Project.toml b/Project.toml
index 97a65aff..84b1b77a 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,6 +1,6 @@
 name = "Bijectors"
 uuid = "76274a88-744f-5084-9051-94815aaf08c4"
-version = "0.15.2"
+version = "0.15.3"
 
 [deps]
 ArgCheck = "dce04be8-c92d-5529-be00-80e4d2c0e197"
diff --git a/src/bijectors/corr.jl b/src/bijectors/corr.jl
index 93a5b089..c511de0b 100644
--- a/src/bijectors/corr.jl
+++ b/src/bijectors/corr.jl
@@ -2,7 +2,7 @@
     CorrBijector <: Bijector
 
 A bijector implementation of Stan's parametrization method for Correlation matrix:
-https://mc-stan.org/docs/2_23/reference-manual/correlation-matrix-transform-section.html
+https://mc-stan.org/docs/reference-manual/transforms.html#correlation-matrix-transform.section
 
 Basically, a unconstrained strictly upper triangular matrix `y` is transformed to 
 a correlation matrix by following readable but not that efficient form:
@@ -348,13 +348,12 @@ function _inv_link_chol_lkj(Y::AbstractMatrix)
     T = float(eltype(W))
     logJ = zero(T)
 
-    idx = 1
     @inbounds for j in 1:K
         log_remainder = zero(T)  # log of proportion of unit vector remaining
         for i in 1:(j - 1)
             z = tanh(Y[i, j])
             W[i, j] = z * exp(log_remainder)
-            log_remainder += log1p(-z^2) / 2
+            log_remainder -= LogExpFunctions.logcosh(Y[i, j])
             logJ += log_remainder
         end
         logJ += log_remainder
@@ -380,10 +379,10 @@ function _inv_link_chol_lkj(y::AbstractVector)
         log_remainder = zero(T)  # log of proportion of unit vector remaining
         for i in 1:(j - 1)
             z = tanh(y[idx])
-            idx += 1
             W[i, j] = z * exp(log_remainder)
-            log_remainder += log1p(-z^2) / 2
+            log_remainder -= LogExpFunctions.logcosh(y[idx])
             logJ += log_remainder
+            idx += 1
         end
         logJ += log_remainder
         W[j, j] = exp(log_remainder)
@@ -461,13 +460,8 @@ function _logabsdetjac_inv_corr(Y::AbstractMatrix)
     K = LinearAlgebra.checksquare(Y)
 
     result = float(zero(eltype(Y)))
-    for j in 2:K, i in 1:(j - 1)
-        @inbounds abs_y_i_j = abs(Y[i, j])
-        result +=
-            (K - i + 1) * (
-                IrrationalConstants.logtwo -
-                (abs_y_i_j + LogExpFunctions.log1pexp(-2 * abs_y_i_j))
-            )
+    @inbounds for j in 2:K, i in 1:(j - 1)
+        result -= (K - i + 1) * LogExpFunctions.logcosh(Y[i, j])
     end
     return result
 end
@@ -477,13 +471,8 @@ function _logabsdetjac_inv_corr(y::AbstractVector)
 
     result = float(zero(eltype(y)))
     for (i, y_i) in enumerate(y)
-        abs_y_i = abs(y_i)
         row_idx = vec_to_triu1_row_index(i)
-        result +=
-            (K - row_idx + 1) * (
-                IrrationalConstants.logtwo -
-                (abs_y_i + LogExpFunctions.log1pexp(-2 * abs_y_i))
-            )
+        result -= (K - row_idx + 1) * LogExpFunctions.logcosh(y_i)
     end
     return result
 end
@@ -496,10 +485,9 @@ function _logabsdetjac_inv_chol(y::AbstractVector)
     @inbounds for j in 2:K
         tmp = zero(result)
         for _ in 1:(j - 1)
-            z = tanh(y[idx])
-            logz = log(1 - z^2)
-            result += logz + (tmp / 2)
-            tmp += logz
+            logcoshy = LogExpFunctions.logcosh(y[idx])
+            tmp -= logcoshy
+            result += tmp - logcoshy
             idx += 1
         end
     end