add configure + missing srr documentation entries

.Rbuildignore

@@ -11,6 +11,7 @@
 ^README\.html$
 ^pkgdown$
 ^Makefile$
+^src/Makevars$
 ^codemeta\.json$
 ^CODE_OF_CONDUCT\.md$
 ^\.lintr$

cleanup

@@ -0,0 +1 @@
+rm -f src/Makevars configure.log

configure

@@ -0,0 +1,16 @@
+#!/bin/sh
+
+# Set the number of cores
+if [ -z "$PKG_NCORES" ]; then
+  PKG_NCORES=$(getconf _NPROCESSORS_ONLN)
+    if [ -z "$PKG_NCORES" ] || [ "$PKG_NCORES" -lt 1 ]; then
+      PKG_NCORES=1
+    elif [ "$PKG_NCORES" -gt 1 ]; then
+      PKG_NCORES=$((PKG_NCORES - 1))
+    fi
+fi
+
+# Write Makevars
+sed -e "s|@ncores@|$PKG_NCORES|" \
+  src/Makevars.in > src/Makevars
+

src/Makevars.in

@@ -0,0 +1,2 @@
+PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS) -DARMA_OPENMP_THREADS=@ncores@
+PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)

tests/testthat/test-errors.R

@@ -1,5 +1,7 @@
 #' srr_stats (tests)
 #' @srrstatsVerbose TRUE
+#' @srrstats {G5.2} *Appropriate error and warning behaviour of all functions
+#'  should be explicitly demonstrated through tests. In particular,*
 #' @srrstats {G5.2b} Here we cover errors as broadly as possible conditions,
 #'  and we write intentionally bad examples to compare the result with expected
 #'  values and inputs.

tests/testthat/test-feglm.R

@@ -8,10 +8,19 @@
 #' @srrstats {G5.4b} We determine correctess for GLMs by comparison, checking
 #'  the estimates versus base R and hardcoded values obtained with Alpaca
 #'  (Stammann, 2018).
-#' @srrstats {G5.8} **Edge condition tests** *to test that these conditions produce expected behaviour such as clear warnings or errors when confronted with data with extreme properties including but not limited to:*
-#' @srrstats {G5.8b} *Data of unsupported types (e.g., character or complex numbers in for functions designed only for numeric data)*
-#' @srrstats {RE7.2} Demonstrate that output objects retain aspects of input data such as row or case names (see **RE1.3**).
-#' @srrstats {RE7.3} Demonstrate and test expected behaviour when objects returned from regression software are submitted to the accessor methods of **RE4.2**--**RE4.7**.
+#' @srrstats {G5.8} **Edge condition tests** *to test that these conditions
+#'  produce expected behaviour such as clear warnings or errors when confronted with data with extreme properties including but not limited to:*
+#' @srrstats {G5.8b} *Data of unsupported types (e.g., character or complex
+#'  numbers in for functions designed only for numeric data)*
+#' @srrstats {G5.7} **Algorithm performance tests** *to test that implementation
+#'  performs as expected as properties of data change. For instance, a test may
+#'  show that parameters approach correct estimates within tolerance as data
+#'  size increases, or that convergence times decrease for higher convergence
+#'  thresholds.*
+#' @srrstats {RE7.2} Demonstrate that output objects retain aspects of input
+#'  data such as row or case names (see **RE1.3**).
+#' @srrstats {RE7.3} Demonstrate and test expected behaviour when objects
+#'  returned from regression software are submitted to the accessor methods of **RE4.2**--**RE4.7**.
 #' 
 #' @noRd
 NULL

tests/testthat/test-felm.R

@@ -1,6 +1,11 @@
 #' srr_stats (tests)
 #' @srrstatsVerbose TRUE
 #' @srrstats {G5.4b} See test-feglm.R
+#' @srrstats {G5.7} See test-feglm.R
+#' @srrstats {G5.9} **Noise susceptibility tests** *Packages should test for
+#'  expected stochastic behaviour, such as through the following conditions:*
+#' #' @srrstats {G5.9a} *Adding trivial noise (for example, at the scale of
+#'  `.Machine$double.eps`) to data does not meaningfully change results*
 #' @noRd
 NULL
 

tests/testthat/test-fenegbin.R

@@ -1,6 +1,7 @@
 #' srr_stats (tests)
 #' @srrstatsVerbose TRUE
 #' @srrstats {G5.4b} See test-feglm.R
+#' @srrstats {G5.7} See test-feglm.R
 #' @noRd
 NULL
 

tests/testthat/test-fepoisson.R

@@ -1,6 +1,11 @@
 #' srr_stats (tests)
 #' @srrstatsVerbose TRUE
 #' @srrstats {G5.4b} See test-feglm.R
+#' @srrstats {G5.7} See test-feglm.R
+#' @srrstats {G5.9} **Noise susceptibility tests** *Packages should test for
+#'  expected stochastic behaviour, such as through the following conditions:*
+#' #' @srrstats {G5.9a} *Adding trivial noise (for example, at the scale of
+#'  `.Machine$double.eps`) to data does not meaningfully change results*
 #' @noRd
 NULL
 
@@ -56,7 +61,9 @@ test_that("fepoisson is similar to fixest", {
 #'  the slopes do not change. See test-feglm.R.
 #' @srrstats {RE7.1a} Model fitting is at least as fast or (preferably) faster
 #'  than testing with equivalent noisy data (see RE2.4b).*
-#'
+#' @srrstats {G5.12} The commented time test takes around 1 minute, which is
+#'  too long for CRAN, but it shows that the algorithm speed is robust to noise
+#'  and it it marginally slower with noise.
 #' @noRd
 NULL
 
@@ -76,6 +83,18 @@ test_that("fepoisson estimation is the same adding noise to the data", {
   expect_equal(coef(m1), coef(m2))
   expect_equal(fixed_effects(m1), fixed_effects(m2))
 
+  # n <- 10e5
+
+  # set.seed(123)
+
+  # d <- data.frame(
+  #   x = rnorm(n),
+  #   y = rpois(n, 1),
+  #   f = factor(rep(1:10, 10e4))
+  # )
+
+  # d$y2 <- d$y + pmax(rnorm(nrow(d)), 0) * .Machine$double.eps
+
   # t1 <- rep(NA, 10)
   # t2 <- rep(NA, 10)
   # for (i in 1:10) {