Update README.md

[ci skip]

Author: jgabry

README.md

@@ -14,14 +14,6 @@ __loo__ is an R package that allows users to compute efficient approximate
 leave-one-out cross-validation for fitted Bayesian models, as well as model
 weights that can be used to average predictive distributions.
 
-Leave-one-out cross-validation (LOO-CV, or LOO for short) and the widely
-applicable information criterion (WAIC) are methods for estimating pointwise
-out-of-sample prediction accuracy from a fitted Bayesian model using the
-log-likelihood evaluated at the posterior simulations of the parameter values.
-LOO and WAIC have various advantages over simpler estimates of predictive error
-such as AIC and DIC but are less used in practice because they involve
-additional computational steps.
-
 The __loo__ R package package implements the fast and stable computations 
 for approximate LOO-CV and WAIC from
 
@@ -31,28 +23,20 @@ _Statistics and Computing_. 27(5), 1413--1432.
 doi:10.1007/s11222-016-9696-4. [Online](https://link.springer.com/article/10.1007/s11222-016-9696-4), 
 [arXiv preprint arXiv:1507.04544](https://arxiv.org/abs/1507.04544).
 
-* Vehtari, A., Gelman, A., and Gabry, J. (2017). Pareto smoothed importance sampling. 
-[arXiv preprint arXiv:1507.02646](https://arxiv.org/abs/1507.02646).
-
-From existing posterior simulation draws, we compute approximate LOO-CV using
-Pareto smoothed importance sampling (PSIS), a new procedure for regularizing
-importance weights. As a byproduct of our calculations, we also obtain
-approximate standard errors for estimated predictive errors and for comparing
-predictive errors between two models.
-
-We recommend PSIS-LOO-CV instead of WAIC, because PSIS provides useful
-diagnostics and effective sample size and Monte Carlo standard error estimates.
-
-As of version `2.0.0`, the __loo__ package also provides methods for using
-stacking and other model weighting techiques to average Bayesian predictive
-distributions. For details on stacking and model weighting see:
-
 * Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. (2018). Using
 stacking to average Bayesian predictive distributions. In Bayesian
 Analysis, doi:10.1214/17-BA1091. 
 [Online](https://projecteuclid.org/euclid.ba/1516093227),
 [arXiv preprint arXiv:1704.02030](https://arxiv.org/abs/1704.02030).
 
+From existing posterior simulation draws, we compute approximate LOO-CV using
+Pareto smoothed importance sampling (PSIS), a new procedure for regularizing
+importance weights. As a byproduct of our calculations, we also obtain
+approximate standard errors for estimated predictive errors and for comparing
+predictive errors between two models. We recommend PSIS-LOO-CV instead of WAIC, 
+because PSIS provides useful diagnostics and effective sample size and Monte 
+Carlo standard error estimates.
+
 
 ### Resources
 
@@ -63,23 +47,25 @@ Analysis, doi:10.1214/17-BA1091.
 
 ### Installation
 
-* Install from CRAN:
+* Install the latest release from CRAN:
 
 ```r
 install.packages("loo")
 ```
 
-* Install from GitHub (requires __devtools__ package):
+* Install the latest development version from GitHub:
 
 ```r
-if (!require(devtools)) install.packages("devtools")
-devtools::install_github("stan-dev/loo")
+# install.packages("remotes")
+remotes::install_github("stan-dev/loo")
 ```
+
 We do _not_ recommend setting `build_vignettes=TRUE` when installing from GitHub
-because the vignettes take a long time to build and are always available
+because some of the vignettes take a long time to build and are always available
 online at [mc-stan.org/loo/articles/](https://mc-stan.org/loo/articles/).
 
 ### Python and Matlab/Octave Code
+
 Corresponding Python and Matlab/Octave code can be found at the
 [avehtari/PSIS](https://github.com/avehtari/PSIS) repository.