README.Rmd typos

README.Rmd

@@ -14,16 +14,16 @@ output: github_document
 # Overview
 The `stackr` package provides an easy way to combine predictions
 from individual time series or panel data models to an 
-ensemble. `stackr` stacks Models according to the Continuous Ranked Probability 
+ensemble. `stackr` stacks models according to the Continuous Ranked Probability 
 Score (CRPS) over k-step ahead predictions. It is therefore especially
-suited for timeseries and panel data. A function for 
+suited for time-series and panel data. A function for 
 leave-one-out CRPS may be added in the future. Predictions need to be 
 predictive distributions represented by predictive samples. Usually, these will 
 be sets of posterior predictive simulation draws generated by an MCMC 
 algorithm. 
 
 # Installation
-install using
+Install using
 
 ``` {r eval = FALSE}
 devtools::install_github("epiforecasts/stackr")
@@ -33,8 +33,8 @@ devtools::install_github("epiforecasts/stackr")
 Given some training data with true observed values as well as predictive samples
 generated from different models, `stackr` finds the optimal (in the sense of 
 minimizing expected cross-validation predictive error) weights to form an
-ensemble of these models. Using these weights, stackr can then provide
-samples from the optimal model mixture by drawing from the predictice samples
+ensemble of these models. Using these weights, `stackr` can then provide
+samples from the optimal model mixture by drawing from the predictive samples
 of those models in the correct proportion. This gives a mixture model
 solely based on predictive samples and is in this regard superior to other
 ensembling techniques like Bayesian Model Averaging. More information 
@@ -78,7 +78,7 @@ score_df[, mean(crps), by = model][, setnames(.SD, "V1", "CRPS")]
 
 # References
 - Using Stacking to Average Bayesian Predictive Distributions, Yuling Yao, Aki Vehtari, Daniel Simpson, and Andrew Gelman, 2018, Bayesian Analysis 13, Number 3, pp. 917–1003 DOI 10.1214/17-BA1091
-- Strictly Proper Scoring Rules, Prediction,and Estimation, 
+- Strictly Proper Scoring Rules, Prediction, and Estimation, 
 Tilmann Gneiting and Adrian E. Raftery, 2007, Journal of the American
 Statistical Association, Volume 102, 2007 - Issue 477 DOI 10.1198/016214506000001437
 - Comparing Bayes Model Averaging and Stacking When Model Approximation Error Cannot be Ignored, 

README.md

@@ -17,16 +17,16 @@ MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.or
 
 The `stackr` package provides an easy way to combine predictions from
 individual time series or panel data models to an ensemble. `stackr`
-stacks Models according to the Continuous Ranked Probability Score
+stacks models according to the Continuous Ranked Probability Score
 (CRPS) over k-step ahead predictions. It is therefore especially suited
-for timeseries and panel data. A function for leave-one-out CRPS may be
+for time-series and panel data. A function for leave-one-out CRPS may be
 added in the future. Predictions need to be predictive distributions
 represented by predictive samples. Usually, these will be sets of
 posterior predictive simulation draws generated by an MCMC algorithm.
 
 # Installation
 
-install using
+Install using
 
 ``` r
 devtools::install_github("epiforecasts/stackr")
@@ -37,12 +37,13 @@ devtools::install_github("epiforecasts/stackr")
 Given some training data with true observed values as well as predictive
 samples generated from different models, `stackr` finds the optimal (in
 the sense of minimizing expected cross-validation predictive error)
-weights to form an ensemble of these models. Using these weights, stackr
-can then provide samples from the optimal model mixture by drawing from
-the predictice samples of those models in the correct proportion. This
-gives a mixture model solely based on predictive samples and is in this
-regard superior to other ensembling techniques like Bayesian Model
-Averaging. More information can be found in the package vignette.
+weights to form an ensemble of these models. Using these weights,
+`stackr` can then provide samples from the optimal model mixture by
+drawing from the predictive samples of those models in the correct
+proportion. This gives a mixture model solely based on predictive
+samples and is in this regard superior to other ensembling techniques
+like Bayesian Model Averaging. More information can be found in the
+package vignette.
 
 Weights are generated using the `crps_weights` function. With these
 weights and predictive samples, the `mixture_from_samples` function can
@@ -87,7 +88,7 @@ score_df[, mean(crps), by = model][, setnames(.SD, "V1", "CRPS")]
 - Using Stacking to Average Bayesian Predictive Distributions, Yuling
  Yao, Aki Vehtari, Daniel Simpson, and Andrew Gelman, 2018, Bayesian
  Analysis 13, Number 3, pp. 917–1003 DOI 10.1214/17-BA1091
-- Strictly Proper Scoring Rules, Prediction,and Estimation, Tilmann
+- Strictly Proper Scoring Rules, Prediction, and Estimation, Tilmann
  Gneiting and Adrian E. Raftery, 2007, Journal of the American
  Statistical Association, Volume 102, 2007 - Issue 477 DOI
  10.1198/016214506000001437