Merge pull request #18 from nhejazi/fix-ci-minor

fix minor typos and CI problems

.travis.yml

@@ -1,7 +1,6 @@
 branches:
   only:
   - master
-  - devel
 
 env:
   global:
@@ -10,7 +9,6 @@ env:
 language: r
 sudo: required
 cache: packages
-
 warnings_are_errors: true
 r_build_args: '--no-manual'
 r_check_args: '--no-vignettes --no-manual'
@@ -24,20 +22,24 @@ before_install:
 
 r_packages:
   - devtools
+  - sessioninfo
   - covr
+  - data.table
 
 r_github_packages:
-  - r-lib/devtools
   - r-lib/sessioninfo
   - Rdatatable/data.table
   - jimhester/covr
-  - jeremyrcoyle/hal9001
+  - osofr/simcausal
   - osofr/condensier
-  - tlverse/sl3@devel
+  - tlverse/delayed
+  - tlverse/origami
+  - tlverse/hal9001
+  - tlverse/sl3
 
 after_success:
   ## coverage
-  - travis_wait 30 Rscript -e "covr::codecov(line_exclusions = list('R/plots.R', 'R/utils.R'))"
+  - travis_wait 30 Rscript -e "covr::codecov(line_exclusions = list('R/utils.R'))"
 
 on_failure:
   - "./travis-tool.sh dump_logs"

DESCRIPTION

@@ -45,6 +45,7 @@ Suggests:
     Rsolnp,
     nnls,
     hal9001,
+    xgboost,
     mma
 Remotes:
     github::osofr/simcausal@master,

README.Rmd

@@ -35,7 +35,7 @@ The `medshift` R package is designed to provide facilities for estimating a
 parameter that arises in a decomposition of the population intervention causal
 effect into the (in)direct effects under stochastic interventions in the setting
 of mediation analysis. `medshift` is designed as an implementation to accompany
-the methodology described @diaz2019causal.
+the methodology described in @diaz2019causal.
 
 ---
 
@@ -52,7 +52,7 @@ devtools::install_github("nhejazi/medshift")
 
 ## Example
 
-To illustrate how `medshift` may be used to estimation the effect of applying a
+To illustrate how `medshift` may be used to estimate the effect of applying a
 stochastic intervention to the treatment (`A`) while keeping the mediator(s)
 (`Z`) fixed, consider the following example:
 

README.md

@@ -28,7 +28,7 @@ The `medshift` R package is designed to provide facilities for
 estimating a parameter that arises in a decomposition of the population
 intervention causal effect into the (in)direct effects under stochastic
 interventions in the setting of mediation analysis. `medshift` is
-designed as an implementation to accompany the methodology described
+designed as an implementation to accompany the methodology described in
 Díaz and Hejazi (2019).
 
 -----
@@ -46,7 +46,7 @@ devtools::install_github("nhejazi/medshift")
 
 ## Example
 
-To illustrate how `medshift` may be used to estimation the effect of
+To illustrate how `medshift` may be used to estimate the effect of
 applying a stochastic intervention to the treatment (`A`) while keeping
 the mediator(s) (`Z`) fixed, consider the following example:
 

appveyor.yml

@@ -17,7 +17,6 @@ cache:
 branches:
   only:
     - master
-    - devel
 
 environment:
   global:
@@ -34,9 +33,12 @@ build_script:
   - travis-tool.sh install_github r-lib/sessioninfo
   - travis-tool.sh install_github Rdatatable/data.table
   - travis-tool.sh install_github jimhester/covr
-  - travis-tool.sh install_github jeremyrcoyle/hal9001
+  - travis-tool.sh install_github osofr/simcausal
   - travis-tool.sh install_github osofr/condensier
-  - travis-tool.sh install_github tlverse/sl3@devel
+  - travis-tool.sh install_github tlverse/delayed
+  - travis-tool.sh install_github tlverse/origami
+  - travis-tool.sh install_github tlverse/hal9001
+  - travis-tool.sh install_github tlverse/sl3
 
 test_script:
   - travis-tool.sh run_tests
@@ -46,7 +48,7 @@ on_failure:
   - appveyor PushArtifact failure.zip
 
 on_success:
-  - Rscript -e "covr::codecov(line_exclusions = list('R/plots.R', 'R/utils.R'))"
+  - Rscript -e "covr::codecov(line_exclusions = list('R/utils.R'))"
 
 artifacts:
   - path: '*.Rcheck\**\*.log'