Updated some references.

inst/REFERENCES.bib

@@ -44,10 +44,14 @@ @InProceedings{ScDe17e
 }
 
 @Article{ScHu23c,
-  author  = {Schmid, Christian S. and Hunter, David. R.},
-  title   = {Computing Pseudolikelihood Estimators for Exponential-Family Random Graph Models},
+  author = {Christian S. Schmid and David R. Hunter},
+  title = {Computing Pseudolikelihood Estimators for Exponential-Family Random Graph Models},
   journal = {Journal of Data Science},
-  year    = {2023},
+  volume = {21},
+  number = {2},
+  year = {2023},
+  pages = {295--309},
+  doi = {10.6339/23-JDS1094},
 }
 
 @Article{HuHu12i,
@@ -177,17 +181,17 @@ @Article{KrKu23l
   publisher = {Institute of Mathematical Statistics},
 }
 
-@Misc{VaFl15m,
-  author       = {Dootika Vats and James M. Flegal and Galin L. Jones},
-  title        = {Multivariate Output Analysis for Markov Chain Monte Carlo},
-  howpublished = {arXiv},
-  year         = {2015},
-  date         = {2015-12-24},
-  doi          = {10.48550/arXiv.1512.07713},
-  eprint       = {1512.07713v4},
-  eprintclass  = {math.ST},
-  eprinttype   = {arXiv},
-  keywords     = {math.ST, stat.CO, stat.TH},
+@article{VaFl15m,
+    author = {Vats, Dootika and Flegal, James M. and Jones, Galin L.},
+    title = {Multivariate output analysis for {Markov} chain {Monte} {Carlo}},
+    journal = {Biometrika},
+    volume = {106},
+    number = {2},
+    pages = {321-337},
+    year = {2019},
+    month = {04},
+    abstract = {Markov chain Monte Carlo produces a correlated sample which may be used for estimating expectations with respect to a target distribution. A fundamental question is: when should sampling stop so that we have good estimates of the desired quantities? The key to answering this question lies in assessing the Monte Carlo error through a multivariate Markov chain central limit theorem. The multivariate nature of this Monte Carlo error has been largely ignored in the literature. We present a multivariate framework for terminating a simulation in Markov chain Monte Carlo. We define a multivariate effective sample size, the estimation of which requires strongly consistent estimators of the covariance matrix in the Markov chain central limit theorem, a property we show for the multivariate batch means estimator. We then provide a lower bound on the number of minimum effective samples required for a desired level of precision. This lower bound does not depend on the underlying stochastic process and can be calculated a priori. This result is obtained by drawing a connection between terminating simulation via effective sample size and terminating simulation using a relative standard deviation fixed-volume sequential stopping rule, which we demonstrate is an asymptotically valid procedure. The finite-sample properties of the proposed method are demonstrated in a variety of examples.},
+    doi = {10.1093/biomet/asz002},
 }
 
 @Article{WaAt13a,