Fixed problem with erfc() function in C++ code---added C++11 to Syste…

…mRequirements. Also, re-rendered all the vignettes successfully.

DESCRIPTION

@@ -2,7 +2,7 @@ Package: mashr
 Type: Package
 Encoding: UTF-8
 Title: Multivariate Adaptive Shrinkage 
-Version: 0.2-0
+Version: 0.2-1
 Date: 2017-10-07
 Author: Matthew Stephens, Sarah Urbut, Gao Wang, Peter Carbonetto
 Maintainer: Matthew Stephens <mstephens@uchicago.edu>
@@ -11,6 +11,7 @@ URL: http://github.com/stephenslab/mashr
 BugReports: http://github.com/stephenslab/mashr/issues
 License: BSD_3_clause + file LICENSE
 LazyData: true
+SystemRequirements: C++11
 Depends: R (>= 3.3.0),
     ashr (>= 2.1-19)
 Imports:

docs/intro_mash.html

@@ -168,9 +168,9 @@ <h1>Step 3: fit the model</h1>
 <pre><code>#  - Computing 2000 x 151 likelihood matrix.
 #  - Likelihood calculations took 0.04 seconds.
 #  - Fitting model with 151 mixture components.
-#  - Model fitting took 0.34 seconds.
+#  - Model fitting took 0.33 seconds.
 #  - Computing posterior matrices.
-#  - Computation allocated took 0.01 seconds.</code></pre>
+#  - Computation allocated took 0.02 seconds.</code></pre>
 <p>This can take a little time. What this does is to fit a mixture model to the data, estimating the mixture proportions. Specifically the model is that the true effects follow a mixture of multivariate normal distributions: <span class="math inline">\(B \sim \sum_k \sum_l \pi_{kl} N(0, \omega_l U_k)\)</span> where the <span class="math inline">\(\omega_l\)</span> are scaling factors set by the “grid” parameter in <code>mash</code> and the <span class="math inline">\(U_k\)</span> are the covariance matrices (here specified by <code>U.c</code>).</p>
 <div id="step-4-extract-posterior-summaries" class="section level2">
 <h2>Step 4: Extract Posterior Summaries</h2>
@@ -272,10 +272,10 @@ <h2>Session information.</h2>
 # [1] stats     graphics  grDevices utils     datasets  methods   base     
 # 
 # other attached packages:
-# [1] mashr_0.1-24  ashr_2.1-26   rmarkdown_1.6
+# [1] mashr_0.2-0   ashr_2.1-26   rmarkdown_1.6
 # 
 # loaded via a namespace (and not attached):
-#  [1] Rcpp_0.12.12      knitr_1.16        magrittr_1.5     
+#  [1] Rcpp_0.12.12      knitr_1.17        magrittr_1.5     
 #  [4] REBayes_0.85      MASS_7.3-47       doParallel_1.0.10
 #  [7] pscl_1.4.9        SQUAREM_2016.8-2  lattice_0.20-35  
 # [10] foreach_1.4.3     plyr_1.8.4        stringr_1.2.0    
@@ -284,7 +284,7 @@ <h2>Session information.</h2>
 # [19] assertthat_0.2.0  yaml_2.1.14       rprojroot_1.2    
 # [22] digest_0.6.12     Matrix_1.2-10     codetools_0.2-15 
 # [25] evaluate_0.10.1   stringi_1.1.5     compiler_3.4.1   
-# [28] Rmosek_7.1.3      backports_1.1.0   mvtnorm_1.0-6    
+# [28] Rmosek_8.0.69     backports_1.1.0   mvtnorm_1.0-6    
 # [31] truncnorm_1.0-7</code></pre>
 </div>
 </div>

docs/intro_mash_dd.html

@@ -187,27 +187,27 @@ <h2>Run mash</h2>
 <pre><code>#  - Computing 2000 x 91 likelihood matrix.
 #  - Likelihood calculations took 0.03 seconds.
 #  - Fitting model with 91 mixture components.
-#  - Model fitting took 0.18 seconds.
+#  - Model fitting took 0.13 seconds.
 #  - Computing posterior matrices.
-#  - Computation allocated took 0.00 seconds.</code></pre>
+#  - Computation allocated took 0.01 seconds.</code></pre>
 <pre class="r"><code>print(get_loglik(m.ed),digits = 10)</code></pre>
 <pre><code># [1] -16155.73391</code></pre>
 <p>From the fit we see that the fit using the data-driven covariances is not as good as when we used the canonical covariances (which was -16120.32; from <a href="intro_mash.html">the introductory vignette</a>). This is expected for this simulation because the simulation actually used the canonical covariances!</p>
 <p>In general we recommend running <code>mash</code> with both data-driven and canonical covariances. You could do this by combining the data-driven and canonical covariances as in this (unrun) code:</p>
 <pre class="r"><code>U.c = cov_canonical(data)  
 m   = mash(data, c(U.c,U.ed))</code></pre>
 <pre><code>#  - Computing 2000 x 241 likelihood matrix.
-#  - Likelihood calculations took 0.14 seconds.
+#  - Likelihood calculations took 0.11 seconds.
 #  - Fitting model with 241 mixture components.</code></pre>
 <pre><code># Warning in REBayes::KWDual(A, rep(1, k), normalize(w), control = control): estimated mixing distribution has some negative values:
 #              consider reducing rtol</code></pre>
 <pre><code># Warning in mixIP(matrix_lik = structure(c(0.493081683750309,
 # 0.849522254927991, : Optimization step yields mixture weights that are
 # either too small, or negative; weights have been corrected and renormalized
 # after the optimization.</code></pre>
-<pre><code>#  - Model fitting took 0.68 seconds.
+<pre><code>#  - Model fitting took 0.45 seconds.
 #  - Computing posterior matrices.
-#  - Computation allocated took 0.04 seconds.</code></pre>
+#  - Computation allocated took 0.07 seconds.</code></pre>
 <p>For an example with simulations that do not follow the standard canonical matrices see <a href="simulate_noncanon.html">here</a>.</p>
 </div>
 <div id="session-information." class="section level2">
@@ -228,10 +228,10 @@ <h2>Session information.</h2>
 # [1] stats     graphics  grDevices utils     datasets  methods   base     
 # 
 # other attached packages:
-# [1] mashr_0.1-24  ashr_2.1-26   rmarkdown_1.6
+# [1] mashr_0.2-0   ashr_2.1-26   rmarkdown_1.6
 # 
 # loaded via a namespace (and not attached):
-#  [1] Rcpp_0.12.12             knitr_1.16              
+#  [1] Rcpp_0.12.12             knitr_1.17              
 #  [3] magrittr_1.5             REBayes_0.85            
 #  [5] MASS_7.3-47              doParallel_1.0.10       
 #  [7] pscl_1.4.9               SQUAREM_2016.8-2        
@@ -245,7 +245,7 @@ <h2>Session information.</h2>
 # [23] digest_0.6.12            Matrix_1.2-10           
 # [25] codetools_0.2-15         evaluate_0.10.1         
 # [27] stringi_1.1.5            compiler_3.4.1          
-# [29] Rmosek_7.1.3             backports_1.1.0         
+# [29] Rmosek_8.0.69            backports_1.1.0         
 # [31] mvtnorm_1.0-6            truncnorm_1.0-7</code></pre>
 </div>
 

docs/simulate_noncanon.html

@@ -160,30 +160,30 @@ <h2>Simple simulation</h2>
 #  - Computing 2000 x 151 likelihood matrix.
 #  - Likelihood calculations took 0.04 seconds.
 #  - Fitting model with 151 mixture components.
-#  - Model fitting took 0.29 seconds.
+#  - Model fitting took 0.26 seconds.
 #  - Computing posterior matrices.
-#  - Computation allocated took 0.00 seconds.
+#  - Computation allocated took 0.01 seconds.
 m.ed   = mash(data, U.ed)
 #  - Computing 2000 x 91 likelihood matrix.
 #  - Likelihood calculations took 0.02 seconds.
 #  - Fitting model with 91 mixture components.
-#  - Model fitting took 0.16 seconds.
+#  - Model fitting took 0.13 seconds.
 #  - Computing posterior matrices.
 #  - Computation allocated took 0.01 seconds.
 m.c.ed = mash(data, c(U.c,U.ed))
 #  - Computing 2000 x 241 likelihood matrix.
 #  - Likelihood calculations took 0.06 seconds.
 #  - Fitting model with 241 mixture components.
-#  - Model fitting took 0.58 seconds.
+#  - Model fitting took 0.48 seconds.
 #  - Computing posterior matrices.
-#  - Computation allocated took 0.00 seconds.
+#  - Computation allocated took 0.01 seconds.
 m.true = mash(data, U.true)
 #  - Computing 2000 x 31 likelihood matrix.
 #  - Likelihood calculations took 0.01 seconds.
 #  - Fitting model with 31 mixture components.
-#  - Model fitting took 0.06 seconds.
+#  - Model fitting took 0.05 seconds.
 #  - Computing posterior matrices.
-#  - Computation allocated took 0.00 seconds.
+#  - Computation allocated took 0.01 seconds.
 
 print(get_loglik(m.c),digits = 10)
 # [1] -16169.17212

src/mash.cpp

@@ -4,6 +4,9 @@
 #include "mash.h"
 // [[Rcpp::depends(RcppArmadillo)]]
 
+// Enable C++11.
+// [[Rcpp::plugins(cpp11)]]
+
 // [[Rcpp::export]]
 Rcpp::List calc_lik_rcpp(Rcpp::NumericMatrix b_mat,
                          Rcpp::NumericMatrix s_mat,