Merge pull request #97 from NSAPH-Software/release_ver0.2.3

Release ver0.2.3

.Rbuildignore

@@ -20,3 +20,5 @@ index.md
 ^CRAN-RELEASE$
 ^cran-comments\.md$
 ^CRAN-SUBMISSION$
+^CODE_OF_CONDUCT\.md$
+^paper/*$

.github/workflows/R-CMD-check.yaml

@@ -23,7 +23,7 @@ jobs:
       matrix:
         config:
           - {os: windows-latest, r: 'release'}
-          #- {os: macOS-latest, r: 'release'} #deactivate until snprintf issue resolves.
+          - {os: macOS-latest, r: 'release'}
           - {os: ubuntu-20.04, r: 'release', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
           #- {os: ubuntu-20.04, r: 'devel', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
 

.github/workflows/draft-pdf.yml

@@ -0,0 +1,23 @@
+on: [push]
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: paper/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v1
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: paper/paper.pdf

CODE_OF_CONDUCT.md

@@ -0,0 +1,126 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, caste, color, religion, or sexual
+identity and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the overall
+  community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or advances of
+  any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email address,
+  without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at nkhoshnevis@g.harvard.edu. 
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series of
+actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or permanent
+ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior, harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within the
+community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.1, available at
+<https://www.contributor-covenant.org/version/2/1/code_of_conduct.html>.
+
+Community Impact Guidelines were inspired by
+[Mozilla's code of conduct enforcement ladder][https://github.com/mozilla/inclusion].
+
+For answers to common questions about this code of conduct, see the FAQ at
+<https://www.contributor-covenant.org/faq>. Translations are available at <https://www.contributor-covenant.org/translations>.
+
+[homepage]: https://www.contributor-covenant.org

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: GPCERF
 Title: Gaussian Processes for Estimating Causal Exposure Response Curves
-Version: 0.2.2
+Version: 0.2.3
 Authors@R: c(
     person("Naeem", "Khoshnevis", email = "nkhoshnevis@g.harvard.edu",
            role=c("aut","cre"), 

NEWS.md

@@ -1,3 +1,16 @@
+# GPCERF 0.2.3 (2024-03-02)
+
+## Changed
+
+- `estimate_cerf_nngp` takes `outcome_col`, `treatment_col`, and `covariates_col` names as inputs.
+- `estimate_cerf_gp` takes `outcome_col`, `treatment_col`, and `covariates_col` names as inputs.
+
+## Added
+
+- `estimate_cerf_gp` and `estimate_cerf_nngp` have notes on selecting `w`.
+
+
+
 # GPCERF 0.2.2 (2024-02-16)
 
 ## Changed

R/compute_deriv_weights_gp.R

@@ -14,7 +14,7 @@
 #'     (e_gps_pred).
 #'     - Column 3: Standard deviation of  e_gps (e_gps_std)
 #'   used_params:
-#'     - dnorm_log: TRUE or FLASE
+#'     - dnorm_log: TRUE or FALSE
 #' @param hyperparam A vector of hyper-parameters in the GP model.
 #' @param kernel_fn The covariance function.
 #' @param kernel_deriv_fn The partial derivative of the covariance function.

R/compute_m_sigma.R

@@ -10,11 +10,9 @@
 #'   - First element: alpha
 #'   - Second element: beta
 #'   - Third element: g_sigma (gamma / sigma)
-#' @param data A  data.frame containing all data including outcome, exposure
-#' and covariates. In the following order:
-#'   - Column 1: Outcome (Y)
-#'   - Column 2: Exposure or treatment (w)
-#'   - Column 3~m: Confounders (C)
+#' @param outcome_data A  vector of outcome data.
+#' @param treatment_data A vector of treatment data.
+#' @param covariates_data A data frame of covariates data.
 #' @param w A vector of exposure levels at which the CERF is estimated.
 #' @param gps_m An S3 gps object including:
 #'   gps: A data.frame of GPS vectors.
@@ -37,7 +35,8 @@
 #'
 #' @keywords internal
 #'
-compute_m_sigma <- function(hyperparam, data, w, gps_m, tuning,
+compute_m_sigma <- function(hyperparam, outcome_data, treatment_data,
+                            covariates_data, w, gps_m, tuning,
                             kernel_fn = function(x) exp(-x ^ 2)) {
 
   param <- unlist(hyperparam)
@@ -57,7 +56,7 @@ compute_m_sigma <- function(hyperparam, data, w, gps_m, tuning,
   logger::log_trace("Running for tune parameters: ",
                     "alpha: {alpha}, beta: {beta}, g_sigma: {g_sigma} ...")
 
-  w_obs <- data[[2]]
+  w_obs <- treatment_data
 
   #TODO: Following the paper and alpha beta convention, first column should be
   # GPS scaled with alpha, and second column should be w scaled with beta.
@@ -80,7 +79,7 @@ compute_m_sigma <- function(hyperparam, data, w, gps_m, tuning,
 
   # Estimate noise
   if (!tuning) {
-    noise_est <- estimate_noise_gp(data = data,
+    noise_est <- estimate_noise_gp(data = outcome_data,
                                    sigma_obs = sigma_obs,
                                    inv_sigma_obs = inv_sigma_obs)
     logger::log_debug("Estimated noise: {noise_est} ")
@@ -112,16 +111,16 @@ compute_m_sigma <- function(hyperparam, data, w, gps_m, tuning,
     # est is the same as m in the paper.
 
     if (!tuning) {
-      est <- data$Y %*% weights_final
+      est <- outcome_data %*% weights_final
       pst_sd <- noise_est * weights_res$sd_scaled
       logger::log_trace("Posterior for w = {w_instance} ==> ",
                         "mu: {est}, var:{pst_sd}")
     } else {
       est <- NA
       pst_sd <- NA
     }
-    cov_balance_obj <- compute_w_corr(w = data[[2]],
-                                      covariate = data[, 3:ncol(data)],
+    cov_balance_obj <- compute_w_corr(w = treatment_data,
+                                      covariate = covariates_data,
                                       weight = weights_final)
     covariate_balance <- as.vector(cov_balance_obj$absolute_corr)
     c(covariate_balance, est, pst_sd)
@@ -141,17 +140,28 @@ compute_m_sigma <- function(hyperparam, data, w, gps_m, tuning,
 
   # compute original covariate balance of data
   if (!tuning) {
-    cov_balance_obj_org <- compute_w_corr(w = data[[2]],
-                                          covariate = data[, 3:ncol(data)],
-                                          weight = rep(1, nrow(data)))
+    cov_balance_obj_org <- compute_w_corr(w = treatment_data,
+                                          covariate = covariates_data,
+                                          weight = rep(1, nrow(covariates_data)))
     cb_org <- cov_balance_obj_org$absolute_corr
   } else {
     cb_org <- NA
   }
 
+  if (!is.matrix(col_all)){
+    # in case of one covariate col_all returns vector instead of matrix
+    row_name <- names(col_all)[1]
+    col_all <- matrix(col_all, nrow = 1)
+    rownames(col_all) <- row_name
+  }
 
-  col_all_w_average <- rowMeans(col_all, na.rm = TRUE)
 
+  if (nrow(col_all) == 1){
+    col_all_w_average <- mean(col_all, na.rm = TRUE)
+    names(col_all_w_average) <- rownames(col_all)
+  } else {
+    col_all_w_average <- rowMeans(col_all, na.rm = TRUE)
+  }
 
   list(cb = col_all_w_average,
        cb_org = cb_org,