diff --git a/man/DesignSpace.Rd b/man/DesignSpace.Rd
index 179d8ee..daebb2e 100644
--- a/man/DesignSpace.Rd
+++ b/man/DesignSpace.Rd
@@ -44,10 +44,11 @@ where M is the information matrix and C is a vector. Typically C will be a vecto
 parameter of interest. For example, if the columns of X in the design are an interdept, the treatment indicator, and then time 
 period indicators, the vector C may be `c(0,1,0,0,...)`, such that the objective function is the variance of that parameter. 
 If there are multiple designs in the design space, the C vectors do 
-not have to be the same as the columns of X in each design might differ.
+not have to be the same as the columns of X in each design might differ. All the algorithms included in this package are described in 
+Watson, Hemming, and Girling (2023) <arXiv:2303.07953> and Watson (2023) <arXiv:2303.12657>.
 
 If the experimental conditions are correlated with one another, then one of three combinatorial algorithms can be used, see 
-Watson and Pan, 2022 <arXiv:2207.09183>. The algorithms are: (i) local search, which starts from a random design of size m and then
+Watson and Pan, 2022 <doi:10.1007/s11222-023-10280-w>. The algorithms are: (i) local search, which starts from a random design of size m and then
 makes the best swap between an experimental condition in and out of the design until no variance improving swap can be made; 
 (ii) greedy search, which starts from a design of size p << n and then sequentially adds the best experimental condition until 
 it generates a design of size m; (iii) reverse greedy search, which starts from the complete set of N experimental conditions and 
@@ -61,6 +62,10 @@ The approximate algorithm will return weights in [0,1] for each unique experimen
 the "proportion of effort" to spend on each design condition. There are different ways to translate these weights into integer
 values, which are returned see \link[glmmrOptim]{apportion}. Use of the approximate optimal design algorithm can be disabled used `use_combin=TRUE`
 
+A weights algorithm for cases including when the observations are correlated are also included. This algorithm determines the 
+GLMM estimation weights that minimise the variance. The algorithm is described in Watson, Hemming, and Girling (2023) <arXiv:2303.07953>
+along with the other algoithms in this package.
+
 In some cases the optimal design will not be full rank with respect to the design matrix X of the design space. This will result
 in a non-positive definite information matrix, and an error. The program will indicate which columns of X are likely "empty" in the optimal
 design. The user can then optionally remove these columns in the algorithm using the `rm_cols` argument, which will delete the
@@ -175,7 +180,7 @@ des2 <- des$clone(deep=TRUE)
 des2$covariance <- Covariance$new(
   data = df,
   formula = ~ (1|gr(cl)*ar1(t)),
-  parameters = c(0.25,0.8)
+  parameters = c(0.05,0.8)
 )
 ds <- DesignSpace$new(des,des2)
 #weighted average assuming equal weights using local search
@@ -433,7 +438,8 @@ linked design objects. Defaults to FALSE.}
 \item{\code{algo}}{A vector of integers indicating the algorithm(s) to use. 1 = local search, 2 = greedy search, 3 = reverse greedy search.
 Declaring `algo = 1` for example will use the local search. Providing a vector such as `c(3,1)` will execute the algorithms in order,
 so this would run a reverse greedy search followed by a local search. Note that many combinations will be redundant. For example, running
-a greedy search after a local search will not have any effect.}
+a greedy search after a local search will not have any effect. One can also use a general weights algorithm called the 'girling' algorithm,
+setting `algo="girling"`.}
 
 \item{\code{use_combin}}{Logical. If the experimental conditions are uncorrelated, if this option is TRUE then the hill climbing 
 algorithm will be used, otherwise if it is FALSE, then a fast approximate alternative will be used. See Details}
@@ -494,7 +500,7 @@ des2 <- des$clone(deep=TRUE)
 des2$covariance <- Covariance$new(
   data = df,
   formula = ~ (1|gr(cl)*ar1(t)),
-  parameters = c(0.25,0.8)
+  parameters = c(0.05,0.8)
 )
 ds <- DesignSpace$new(des,des2)
 #weighted average assuming equal weights using local search
diff --git a/src/Makevars.win b/src/Makevars.win
index 32c175e..c32a21c 100644
--- a/src/Makevars.win
+++ b/src/Makevars.win
@@ -1,4 +1,4 @@
 CXX_STD = CXX17
-PKG_CPPFLAGS =	-I../inst/include/ 
+PKG_CPPFLAGS =	-I../inst/include/ -Wa,-mbig-obj
 PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS) 
 PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)