merge main

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: arf
 Title: Adversarial Random Forests
-Version: 0.1.3
-Date: 2023-02-06
+Version: 0.2.0
+Date: 2024-01-24
 Authors@R: 
     c(person(given = "Marvin N.",
              family = "Wright",
@@ -12,7 +12,15 @@ Authors@R:
              family = "Watson",
              role = c("aut"),
              email = "[email protected]",
-             comment = c(ORCID = "0000-0001-9632-2159")))
+             comment = c(ORCID = "0000-0001-9632-2159")), 
+      person(given = "Kristin",
+             family = "Blesch",
+             role = c("aut"),
+             comment = c(ORCID = "0000-0001-6241-3079")), 
+      person(given = "Jan",
+             family = "Kapar",
+             role = c("aut"), 
+             comment = c(ORCID = "0009-0000-6408-2840")))
 Maintainer: Marvin N. Wright <[email protected]>
 Description: Adversarial random forests (ARFs) recursively partition data into 
     fully factorized leaves, where features are jointly independent. The 
@@ -45,6 +53,7 @@ Suggests:
     mlbench,
     knitr,
     rmarkdown,
+    tibble,
     testthat (>= 3.0.0)
 Config/testthat/edition: 3
 VignetteBuilder: knitr

NEWS.md

@@ -1,3 +1,12 @@
+
+# arf 0.2.0
+* Vectorized adversarial resampling
+* Speed boost for compiling into a probabilistic circuit
+* Conditional densities and sampling
+* Bayesian solution for invariant continuous data within leaf nodes
+* New function for computing (conditional) expectations
+* Options for missing data
+
 # arf 0.1.3
 * Speed boost for the adversarial resampling step 
 * Early stopping option for adversarial training

README.md

@@ -41,11 +41,11 @@ To generate 100 synthetic samples:
 forge(psi, 100)
 ```
 
-### MAP estimation
-To estimate the most likely values for variables conditional on some evidence, say, `Species = "setosa"`:
+### Conditional expectations
+To estimate the mean of some variable(s), optionally conditioned on some event(s):
 ```R
 evi <- data.frame(Species = "setosa")
-map(psi, evidence = evi)
+expct(psi, query = "Sepal.Length", evidence = evi)
 ```
 
 For more detailed examples, see the package [vignette](https://bips-hb.github.io/arf/articles/vignette.html).

tests/testthat/test-arguments.R

@@ -0,0 +1,4 @@
+test_that("FORDE works with alpha>0", {
+  arf <- adversarial_rf(iris, parallel = FALSE)
+  expect_silent(forde(arf, iris, parallel = FALSE, alpha = 0.01))
+})

tests/testthat/test-return_types.R

@@ -19,7 +19,7 @@ test_that("FORDE categories sum to unity", {
   arf <- adversarial_rf(iris, num_trees = 2, verbose = FALSE, parallel = FALSE)
   psi <- forde(arf, iris, parallel = FALSE)
   tmp <- psi$cat[, sum(prob), by = f_idx]
-  expect_true(all.equal(tmp$V1, rep(1, times = tmp[, .N])))
+  expect_equal(tmp$V1, rep(1, times = tmp[, .N]))
 })
 
 test_that("Likelihood calculation returns vector of log-likelihoods", {
@@ -30,7 +30,7 @@ test_that("Likelihood calculation returns vector of log-likelihoods", {
   expect_type(loglik, "double")
   expect_length(loglik, nrow(iris))
   expect_true(all(!is.na(loglik)))
-  expect_true(all.equal(loglik, loglik2))
+  expect_equal(loglik, loglik2)
 })
 
 test_that("FORGE returns data frame when called with data frame", {