Added import for optimize.

DESCRIPTION

@@ -14,8 +14,8 @@ Description: Implements methods for variable selection in linear
     called "Iterative Bayesian Stepwise Selection" (IBSS), is simple
     and fast, allowing the SuSiE model be fit to large data sets
     (thousands of samples and hundreds of thousands of variables).
-Date: 2021-04-9
-Version: 0.11.0
+Date: 2021-04-13
+Version: 0.11.1
 Authors@R: c(person("Gao","Wang",role="aut",email="[email protected]"),
              person("Yuxin","Zou",role="aut"),
              person("Kaiqian","Zhang",role="aut"),

NAMESPACE

@@ -62,6 +62,7 @@ importFrom(stats,dnorm)
 importFrom(stats,lm)
 importFrom(stats,median)
 importFrom(stats,optim)
+importFrom(stats,optimize)
 importFrom(stats,pnorm)
 importFrom(stats,rmultinom)
 importFrom(stats,rnorm)

R/susie_rss.R

@@ -150,6 +150,8 @@ susie_rss = function (z, R, maf = NULL, maf_thresh = 0, z_ld_weight = 0,
 # assumption is that each b_l has exactly one non-zero element, with
 # all elements equally likely to be non-zero. The prior on the
 # non-zero element is N(0,var = prior_variance).
+#
+#' @importFrom stats optimize
 susie_rss_lambda = function(z, R, maf = NULL, maf_thresh = 0,
                             L = 10, lambda = 0,
                             prior_variance = 50, residual_variance = NULL,
@@ -299,7 +301,8 @@ susie_rss_lambda = function(z, R, maf = NULL, maf_thresh = 0,
         if (est_sigma2 > 1)
           est_sigma2 = 1
       } else {
-        est_sigma2 = optimize(Eloglik_rss, interval = c(1e-4, 1-lambda), R = R, z = z, s = s, maximum = TRUE)$maximum
+        est_sigma2 = optimize(Eloglik_rss, interval = c(1e-4, 1-lambda),
+                              R = R, z = z, s = s, maximum = TRUE)$maximum
         if(Eloglik_rss(est_sigma2, R, z, s) < Eloglik_rss(1-lambda, R, z, s)){
           est_sigma2 = 1-lambda
         }