From: Benjamin Auder <benjamin.auder@somewhere>
Date: Mon, 7 Jun 2021 15:27:23 +0000 (+0200)
Subject: Adjustments + bugs fixing
X-Git-Url: https://git.auder.net/variants/Chakart/pieces/doc/current/img/cross.svg?a=commitdiff_plain;h=ab35f6102896a49e86e853262c0650faa2931638;p=morpheus.git

Adjustments + bugs fixing
---

diff --git a/README.md b/README.md
index dbfd60a..c3d2c9f 100644
--- a/README.md
+++ b/README.md
@@ -12,6 +12,9 @@ NOTE: greek unicode letters are used in the code, because it's much nicer to wri
 and Σ than lambda, beta and Sigma - and also more importantly because it works well :)
 ...However CRAN demands ASCII files - therefore the presence of to-cran.sh script.
 
+Warning: tests won't run well on the non-UTF8 package. Run ./to-cran.sh and
+then inside pkg-cran call devtools::test().
+
 ## Example
 
 Install the package, then ?multiRun is a possible starting point.
diff --git a/doc/TensorDecompositions_Anandkumar.et.al_2014.pdf b/doc/TensorDecompositions_Anandkumar.et.al_2014.pdf
new file mode 100644
index 0000000..90a7f76
--- /dev/null
+++ b/doc/TensorDecompositions_Anandkumar.et.al_2014.pdf
@@ -0,0 +1 @@
+#$# git-fat 00f581aca4dd370615fa0ea99e730d6dd42fe347               585618
diff --git a/doc/Tensor-Kuleshov2015.pdf b/doc/TensorFactorization_Kuleshov.et.al_2015.pdf
similarity index 100%
rename from doc/Tensor-Kuleshov2015.pdf
rename to doc/TensorFactorization_Kuleshov.et.al_2015.pdf
diff --git a/pkg/DESCRIPTION b/pkg/DESCRIPTION
index 2a20240..0a642b6 100644
--- a/pkg/DESCRIPTION
+++ b/pkg/DESCRIPTION
@@ -21,10 +21,10 @@ Suggests:
     devtools,
     flexmix,
     parallel,
-    testthat,
+    testthat (>= 3.0.0),
     roxygen2
 License: MIT + file LICENSE
-RoxygenNote: 7.0.2
+RoxygenNote: 7.1.1
 URL: https://github.com/yagu0/morpheus
 Collate:
     'utils.R'
@@ -34,3 +34,4 @@ Collate:
     'optimParams.R'
     'plot.R'
     'sampleIO.R'
+Config/testthat/edition: 3
diff --git a/pkg/R/computeMu.R b/pkg/R/computeMu.R
index f961fde..c1a9c51 100644
--- a/pkg/R/computeMu.R
+++ b/pkg/R/computeMu.R
@@ -3,6 +3,8 @@
 #' Estimate the normalized columns μ of the β matrix parameter in a mixture of
 #' logistic regressions models, with a spectral method described in the package vignette.
 #'
+#' @name computeMu
+#'
 #' @param X Matrix of input data (size nxd)
 #' @param Y Vector of binary outputs (size n)
 #' @param optargs List of optional argument:
@@ -21,28 +23,28 @@
 #'   and \code{generateSampleIO} for I/O random generation.
 #'
 #' @examples
-#' io = generateSampleIO(10000, 1/2, matrix(c(1,0,0,1),ncol=2), c(0,0), "probit")
-#' μ = computeMu(io$X, io$Y, list(K=2)) #or just X and Y for estimated K
+#' io <- generateSampleIO(10000, 1/2, matrix(c(1,0,0,1),ncol=2), c(0,0), "probit")
+#' μ <- computeMu(io$X, io$Y, list(K=2)) #or just X and Y for estimated K
 #'
 #' @export
-computeMu = function(X, Y, optargs=list())
+computeMu <- function(X, Y, optargs=list())
 {
   if (!is.matrix(X) || !is.numeric(X) || any(is.na(X)))
     stop("X: real matrix, no NA")
-  n = nrow(X)
-  d = ncol(X)
+  n <- nrow(X)
+  d <- ncol(X)
   if (!is.numeric(Y) || length(Y)!=n || any(Y!=0 & Y!=1))
     stop("Y: vector of 0 and 1, size nrow(X), no NA")
   if (!is.list(optargs))
     stop("optargs: list")
 
   # Step 0: Obtain the empirically estimated moments tensor, estimate also K
-  M = if (is.null(optargs$M)) computeMoments(X,Y) else optargs$M
-  K = optargs$K
+  M <- if (is.null(optargs$M)) computeMoments(X,Y) else optargs$M
+  K <- optargs$K
   if (is.null(K))
   {
     # TODO: improve this basic heuristic
-    Σ = svd(M[[2]])$d
+    Σ <- svd(M[[2]])$d
     large_ratio <- ( abs(Σ[-d] / Σ[-1]) > 3 )
     K <- if (any(large_ratio)) max(2, which.min(large_ratio)) else d
   }
@@ -50,24 +52,24 @@ computeMu = function(X, Y, optargs=list())
     stop("K: integer >= 2, <= d")
 
   # Step 1: generate a family of d matrices to joint-diagonalize to increase robustness
-  d = ncol(X)
-  fixed_design = FALSE
-  jd_nvects = ifelse(!is.null(optargs$jd_nvects), optargs$jd_nvects, 0)
+  d <- ncol(X)
+  fixed_design <- FALSE
+  jd_nvects <- ifelse(!is.null(optargs$jd_nvects), optargs$jd_nvects, 0)
   if (jd_nvects == 0)
   {
-    jd_nvects = d
-    fixed_design = TRUE
+    jd_nvects <- d
+    fixed_design <- TRUE
   }
-  M2_t = array(dim=c(d,d,jd_nvects))
+  M2_t <- array(dim=c(d,d,jd_nvects))
   for (i in seq_len(jd_nvects))
   {
-    rho = if (fixed_design) c(rep(0,i-1),1,rep(0,d-i)) else normalize( rnorm(d) )
-    M2_t[,,i] = .T_I_I_w(M[[3]],rho)
+    rho <- if (fixed_design) c(rep(0,i-1),1,rep(0,d-i)) else normalize( rnorm(d) )
+    M2_t[,,i] <- .T_I_I_w(M[[3]],rho)
   }
 
   # Step 2: obtain factors u_i (and their inverse) from the joint diagonalisation of M2_t
-  jd_method = ifelse(!is.null(optargs$jd_method), optargs$jd_method, "uwedge")
-  V =
+  jd_method <- ifelse(!is.null(optargs$jd_method), optargs$jd_method, "uwedge")
+  V <-
     if (jd_nvects > 1) {
       # NOTE: increasing itermax does not help to converge, thus we suppress warnings
       suppressWarnings({jd = jointDiag::ajd(M2_t, method=jd_method)})
@@ -77,17 +79,17 @@ computeMu = function(X, Y, optargs=list())
       eigen(M2_t[,,1])$vectors
 
   # Step 3: obtain final factors from joint diagonalisation of T(I,I,u_i)
-  M2_t = array(dim=c(d,d,K))
+  M2_t <- array(dim=c(d,d,K))
   for (i in seq_len(K))
-    M2_t[,,i] = .T_I_I_w(M[[3]],V[,i])
+    M2_t[,,i] <- .T_I_I_w(M[[3]],V[,i])
   suppressWarnings({jd = jointDiag::ajd(M2_t, method=jd_method)})
-  U = if (jd_method=="uwedge") MASS::ginv(jd$B) else jd$A
-  μ = normalize(U[,1:K])
+  U <- if (jd_method=="uwedge") MASS::ginv(jd$B) else jd$A
+  μ <- normalize(U[,1:K])
 
   # M1 also writes M1 = sum_k coeff_k * μ_k, where coeff_k >= 0
   # ==> search decomposition of vector M1 onto the (truncated) basis μ (of size dxK)
   # This is a linear system μ %*% C = M1 with C of size K ==> C = psinv(μ) %*% M1
-  C = MASS::ginv(μ) %*% M[[1]]
-  μ[,C < 0] = - μ[,C < 0]
+  C <- MASS::ginv(μ) %*% M[[1]]
+  μ[,C < 0] <- - μ[,C < 0]
   μ
 }
diff --git a/pkg/R/multiRun.R b/pkg/R/multiRun.R
index 5167535..59e2483 100644
--- a/pkg/R/multiRun.R
+++ b/pkg/R/multiRun.R
@@ -7,6 +7,8 @@
 #' basis (exact same inputs). It's even possible to compare methods on some
 #' deterministic design of experiments.
 #'
+#' @name multiRun
+#'
 #' @param fargs List of arguments for the estimation functions
 #' @param estimParams List of nf function(s) to apply on fargs
 #' @param prepareArgs Prepare arguments for the functions inside estimParams
@@ -36,8 +38,8 @@
 #'     library(flexmix)
 #'     ind <- fargs$ind
 #'     K <- fargs$K
-#'     dat = as.data.frame( cbind(fargs$Y[ind],fargs$X[ind,]) )
-#'     out = refit( flexmix( cbind(V1, 1 - V1) ~ 0+., data=dat, k=K,
+#'     dat <- as.data.frame( cbind(fargs$Y[ind],fargs$X[ind,]) )
+#'     out <- refit( flexmix( cbind(V1, 1 - V1) ~ 0+., data=dat, k=K,
 #'       model=FLXMRglm(family="binomial") ) )
 #'     normalize( matrix(out@@coef[1:(ncol(fargs$X)*K)], ncol=K) )
 #'   } ),
@@ -52,7 +54,7 @@
 #'   res[[i]] <- alignMatrices(res[[i]], ref=μ, ls_mode="exact")
 #'
 #' # Monte-Carlo + optimParams from X,Y, morpheus VS flexmix
-#' res <- multiRun(list(n=1000,p=1/2,β=β,b=c(0,0),link="logit"),list(
+#' res <- multiRun(list(n=1000,p=1/2,β=β,b=c(0,0),link="logit"), list(
 #'   # morpheus
 #'   function(fargs) {
 #'     library(morpheus)
@@ -66,19 +68,19 @@
 #'     library(flexmix)
 #'     K <- fargs$K
 #'     dat <- as.data.frame( cbind(fargs$Y,fargs$X) )
-#'     out <- refit( flexmix( cbind(V1, 1 - V1) ~ 0+., data=dat, k=K,
+#'     out <- refit( flexmix( cbind(V1, 1 - V1) ~ ., data=dat, k=K,
 #'       model=FLXMRglm(family="binomial") ) )
 #'     sapply( seq_len(K), function(i)
-#'       as.double( out@@components[[1]][[i]][,1] ) )
+#'       as.double( out@@components[[1]][[i]][2:(1+ncol(fargs$X)),1] ) )
 #'   } ),
 #'   prepareArgs = function(fargs,index) {
 #'     library(morpheus)
-#'     io = generateSampleIO(fargs$n, fargs$p, fargs$β, fargs$b, fargs$link)
-#'     fargs$X = io$X
-#'     fargs$Y = io$Y
-#'     fargs$K = ncol(fargs$β)
-#'     fargs$link = fargs$link
-#'     fargs$M = computeMoments(io$X,io$Y)
+#'     io <- generateSampleIO(fargs$n, fargs$p, fargs$β, fargs$b, fargs$link)
+#'     fargs$X <- io$X
+#'     fargs$Y <- io$Y
+#'     fargs$K <- ncol(fargs$β)
+#'     fargs$link <- fargs$link
+#'     fargs$M <- computeMoments(io$X,io$Y)
 #'     fargs
 #'   }, N=10, ncores=3)
 #' for (i in 1:2)
@@ -118,13 +120,13 @@ multiRun <- function(fargs, estimParams,
 
   if (ncores > 1)
   {
-    cl = parallel::makeCluster(ncores, outfile="")
+    cl <- parallel::makeCluster(ncores, outfile="")
     parallel::clusterExport(cl, c("fargs","verbose"), environment())
-    list_res = parallel::clusterApplyLB(cl, 1:N, estimParamAtIndex)
+    list_res <- parallel::clusterApplyLB(cl, 1:N, estimParamAtIndex)
     parallel::stopCluster(cl)
   }
   else
-    list_res = lapply(1:N, estimParamAtIndex)
+    list_res <- lapply(1:N, estimParamAtIndex)
 
   # De-interlace results: output one list per function
   nf <- length(estimParams)
diff --git a/pkg/R/optimParams.R b/pkg/R/optimParams.R
index 175150f..c050e63 100644
--- a/pkg/R/optimParams.R
+++ b/pkg/R/optimParams.R
@@ -1,5 +1,9 @@
+#' optimParams
+#'
 #' Wrapper function for OptimParams class
 #'
+#' @name optimParams
+#'
 #' @param X Data matrix of covariables
 #' @param Y Output as a binary vector
 #' @param K Number of populations.
@@ -24,7 +28,7 @@
 #' # Optimize parameters from estimated μ
 #' io <- generateSampleIO(100,
 #'   1/2, matrix(c(1,-2,3,1),ncol=2), c(0,0), "logit")
-#' μ = computeMu(io$X, io$Y, list(K=2))
+#' μ <- computeMu(io$X, io$Y, list(K=2))
 #' o <- optimParams(io$X, io$Y, 2, "logit")
 #' \donttest{
 #' θ0 <- list(p=1/2, β=μ, b=c(0,0))
@@ -274,7 +278,8 @@ setRefClass(
       {
         stop("θ0$p: length K-1, no NA, positive integers, sum to <= 1")
       }
-      if (is.null(θ0$b))
+      # NOTE: [["b"]] instead of $b because $b would match $beta (in pkg-cran)
+      if (is.null(θ0[["b"]]))
         θ0$b = rep(0, K)
       else if (!is.numeric(θ0$b) || length(θ0$b) != K || any(is.na(θ0$b)))
         stop("θ0$b: length K, no NA")
@@ -282,7 +287,7 @@ setRefClass(
       # (Re)Set W to identity, to allow several run from the same object
       W <<- if (is.null(userW)) diag(d+d^2+d^3) else userW
 
-      #NOTE: loopMax = 3 seems to not improve the final results.
+      # NOTE: loopMax = 3 seems to not improve the final results.
       loopMax <- ifelse(is.null(userW), 2, 1)
       x_init <- linArgs(θ0)
       for (loop in 1:loopMax)
diff --git a/pkg/R/plot.R b/pkg/R/plot.R
index da4fae5..da8f8bf 100644
--- a/pkg/R/plot.R
+++ b/pkg/R/plot.R
@@ -22,6 +22,8 @@
 #'
 #' Plot compared histograms of a single parameter (scalar)
 #'
+#' @name plotHist
+#'
 #' @param mr Output of multiRun(), list of lists of functions results
 #' @param x Row index of the element inside the aggregated parameter
 #' @param y Column index of the element inside the aggregated parameter
@@ -41,7 +43,7 @@
 plotHist <- function(mr, x, y, ...)
 {
   params <- .extractParam(mr, x, y)
-  L = length(params)
+  L <- length(params)
   # Plot histograms side by side
   par(mfrow=c(1,L), cex.axis=1.5, cex.lab=1.5, mar=c(4.7,5,1,1))
   args <- list(...)
@@ -53,11 +55,18 @@ plotHist <- function(mr, x, y, ...)
   }
 }
 
+# NOTE: roxygen2 bug, "@inheritParams plotHist" fails in next header:
+
 #' plotBox
 #'
 #' Draw compared boxplots of a single parameter (scalar)
 #'
-#' @inheritParams plotHist
+#' @name plotBox
+#'
+#' @param mr Output of multiRun(), list of lists of functions results
+#' @param x Row index of the element inside the aggregated parameter
+#' @param y Column index of the element inside the aggregated parameter
+#' @param ... Additional graphical parameters (xlab, ylab, ...)
 #'
 #' @examples
 #' \donttest{
@@ -73,7 +82,7 @@ plotHist <- function(mr, x, y, ...)
 plotBox <- function(mr, x, y, ...)
 {
   params <- .extractParam(mr, x, y)
-  L = length(params)
+  L <- length(params)
   # Plot boxplots side by side
   par(mfrow=c(1,L), cex.axis=1.5, cex.lab=1.5, mar=c(4.7,5,1,1))
   args <- list(...)
@@ -91,6 +100,8 @@ plotBox <- function(mr, x, y, ...)
 #' Note that the drawing does not correspond to a function; it is just a
 #' convenient way to visualize the estimated parameters.
 #'
+#' @name plotCoefs
+#'
 #' @param mr List of parameters matrices
 #' @param params True value of the parameters matrix
 #' @param ... Additional graphical parameters
diff --git a/pkg/R/sampleIO.R b/pkg/R/sampleIO.R
index 230b63c..9419c32 100644
--- a/pkg/R/sampleIO.R
+++ b/pkg/R/sampleIO.R
@@ -4,6 +4,8 @@
 #' into K groups of proportions p. Inside one group, the probability law P(Y=1) is
 #' described by the corresponding column parameter in the matrix β + intercept b.
 #'
+#' @name generateSampleIO
+#'
 #' @param n Number of individuals
 #' @param p Vector of K(-1) populations relative proportions (sum (<)= 1)
 #' @param β Vectors of model parameters for each population, of size dxK
@@ -24,10 +26,10 @@
 #' io$index[1] #number of the group of X[1,] and Y[1] (in 1...K)
 #'
 #' @export
-generateSampleIO = function(n, p, β, b, link)
+generateSampleIO <- function(n, p, β, b, link)
 {
   # Check arguments
-  tryCatch({n = as.integer(n)}, error=function(e) stop("Cannot convert n to integer"))
+  tryCatch({n <- as.integer(n)}, error=function(e) stop("Cannot convert n to integer"))
   if (length(n) > 1)
     warning("n is a vector but should be scalar: only first element used")
   if (n <= 0)
diff --git a/pkg/R/utils.R b/pkg/R/utils.R
index 9d026da..5b9d999 100644
--- a/pkg/R/utils.R
+++ b/pkg/R/utils.R
@@ -11,10 +11,10 @@
 #' normalize(x) #column 1 is 1/sqrt(5) (1 2),
 #'              #and column 2 is 1/sqrt(10) (-1, 3)
 #' @export
-normalize = function(x)
+normalize <- function(x)
 {
-  x = as.matrix(x)
-  norm2 = sqrt( colSums(x^2) )
+  x <- as.matrix(x)
+  norm2 <- sqrt( colSums(x^2) )
   sweep(x, 2, norm2, '/')
 }
 
@@ -25,12 +25,12 @@ normalize = function(x)
 #
 # @return Matrix of size dxd
 #
-.T_I_I_w = function(Te, w)
+.T_I_I_w <- function(Te, w)
 {
-  d = length(w)
-  Ma = matrix(0,nrow=d,ncol=d)
+  d <- length(w)
+  Ma <- matrix(0,nrow=d,ncol=d)
   for (j in 1:d)
-    Ma = Ma + w[j] * Te[,,j]
+    Ma <- Ma + w[j] * Te[,,j]
   Ma
 }
 
@@ -41,11 +41,11 @@ normalize = function(x)
 #
 # @return Matrix of size dxd
 #
-.Moments_M2 = function(X, Y)
+.Moments_M2 <- function(X, Y)
 {
-  n = nrow(X)
-  d = ncol(X)
-  M2 = matrix(0,nrow=d,ncol=d)
+  n <- nrow(X)
+  d <- ncol(X)
+  M2 <- matrix(0,nrow=d,ncol=d)
   matrix( .C("Moments_M2", X=as.double(X), Y=as.double(Y), pn=as.integer(n),
     pd=as.integer(d), M2=as.double(M2), PACKAGE="morpheus")$M2, nrow=d, ncol=d)
 }
@@ -57,11 +57,11 @@ normalize = function(x)
 #
 # @return Array of size dxdxd
 #
-.Moments_M3 = function(X, Y)
+.Moments_M3 <- function(X, Y)
 {
-  n = nrow(X)
-  d = ncol(X)
-  M3 = array(0,dim=c(d,d,d))
+  n <- nrow(X)
+  d <- ncol(X)
+  M3 <- array(0,dim=c(d,d,d))
   array( .C("Moments_M3", X=as.double(X), Y=as.double(Y), pn=as.integer(n),
     pd=as.integer(d), M3=as.double(M3), PACKAGE="morpheus")$M3, dim=c(d,d,d) )
 }
@@ -90,9 +90,9 @@ computeMoments = function(X, Y)
 #
 # @return A permutation minimizing cost
 #
-.hungarianAlgorithm = function(distances)
+.hungarianAlgorithm <- function(distances)
 {
-  n = nrow(distances)
+  n <- nrow(distances)
   .C("hungarianAlgorithm", distances=as.double(distances), pn=as.integer(n),
     assignment=integer(n), PACKAGE="morpheus")$assignment
 }
@@ -120,7 +120,7 @@ computeMoments = function(X, Y)
 #' # a[[i]] is expected to contain m1 for all i
 #'
 #' @export
-alignMatrices = function(Ms, ref, ls_mode=c("exact","approx1","approx2"))
+alignMatrices <- function(Ms, ref, ls_mode=c("exact","approx1","approx2"))
 {
   if (!is.matrix(ref) || any(is.na(ref)))
     stop("ref: matrix, no NAs")
@@ -130,7 +130,7 @@ alignMatrices = function(Ms, ref, ls_mode=c("exact","approx1","approx2"))
   L <- length(Ms)
   for (i in 1:L)
   {
-    m = Ms[[i]] #shorthand
+    m <- Ms[[i]] #shorthand
 
     if (ls_mode == "exact")
     {
diff --git a/pkg/src/functions.c b/pkg/src/functions.c
index 913997b..0551016 100644
--- a/pkg/src/functions.c
+++ b/pkg/src/functions.c
@@ -108,14 +108,14 @@ void Compute_Omega(double* X, int* Y, double* M, int* pnc, int* pn, int* pd, dou
   // Normalize W: x 1/n
   for (int j=0; j<dim; j++)
   {
-    for (int k=0; k<=j; k++)
+    for (int k=j; k<dim; k++)
       W[mi(j,k,dim,dim)] /= n;
   }
-  // Symmetrize W: W[j,k] = W[k,j] for k > j
+  // Symmetrize W: W[k,j] = W[j,k] for k > j
   for (int j=0; j<dim; j++)
   {
     for (int k=j+1; k<dim; k++)
-      W[mi(j,k,dim,dim)] = W[mi(k,j,dim,dim)];
+      W[mi(k,j,dim,dim)] = W[mi(j,k,dim,dim)];
   }
   free(g);
 }
diff --git a/pkg/tests/testthat.R b/pkg/tests/testthat.R
index 3b28538..47c36a9 100644
--- a/pkg/tests/testthat.R
+++ b/pkg/tests/testthat.R
@@ -1,10 +1,4 @@
 library(testthat)
-
-# Locally:
-#library(devtools)
-#load_all("../")
-
-# With R CMD check:
 library(morpheus)
 
 test_check("morpheus")
diff --git a/pkg/tests/testthat/test-Moments.R b/pkg/tests/testthat/test-Moments.R
index 67a90c4..6085339 100644
--- a/pkg/tests/testthat/test-Moments.R
+++ b/pkg/tests/testthat/test-Moments.R
@@ -1,5 +1,3 @@
-context("Moments")
-
 test_that("both versions of Moments_Mi agree on various inputs",
 {
   for (n in c(20,200))
diff --git a/pkg/tests/testthat/test-alignMatrices.R b/pkg/tests/testthat/test-alignMatrices.R
index 47625b1..63e4bd3 100644
--- a/pkg/tests/testthat/test-alignMatrices.R
+++ b/pkg/tests/testthat/test-alignMatrices.R
@@ -1,5 +1,3 @@
-context("alignMatrices")
-
 # Helper to generate a random series of matrices to align
 .generateMatrices = function(d, K, N, noise)
 {
@@ -59,6 +57,6 @@ test_that("labelSwitchingAlign correctly aligns noisy parameters",
 
     # 3] Check alignment
     for (j in 2:N)
-      expect_that( norm(aligned[[j]] - Ms[[1]]), is_less_than(max_error) )
+      expect_lt( norm(aligned[[j]] - Ms[[1]]), max_error )
   }
 })
diff --git a/pkg/tests/testthat/test-computeMu.R b/pkg/tests/testthat/test-computeMu.R
index ba1de74..c1fd4f9 100644
--- a/pkg/tests/testthat/test-computeMu.R
+++ b/pkg/tests/testthat/test-computeMu.R
@@ -1,5 +1,3 @@
-context("computeMu")
-
 test_that("on input of sufficient size, β/||β|| is estimated accurately enough",
 {
   n <- 100000
@@ -28,8 +26,8 @@ test_that("on input of sufficient size, β/||β|| is estimated accurately enough
       diff_norm <- norm(μ_ref - μ_aligned)
       cat(diff_norm,"\n")
 
-      #NOTE: 0.5 is loose threshold, but values around 0.3 are expected...
-      expect_that( diff_norm, is_less_than(0.5) )
+      # NOTE: 0.5 is loose threshold, but values around 0.3 are expected...
+      expect_lt( diff_norm, 0.5 )
     }
   }
 })
diff --git a/pkg/tests/testthat/test-hungarianAlgorithm.R b/pkg/tests/testthat/test-hungarianAlgorithm.R
index 3c333b0..4eacbb9 100644
--- a/pkg/tests/testthat/test-hungarianAlgorithm.R
+++ b/pkg/tests/testthat/test-hungarianAlgorithm.R
@@ -1,5 +1,3 @@
-context("hungarianAlgorithm")
-
 test_that("HungarianAlgorithm provides the correct answer on various inputs",
 {
   for (n in c(2,3,10,50))
diff --git a/pkg/tests/testthat/test-jointDiag.R b/pkg/tests/testthat/test-jointDiag.R
index 619c9b6..76eb839 100644
--- a/pkg/tests/testthat/test-jointDiag.R
+++ b/pkg/tests/testthat/test-jointDiag.R
@@ -1,5 +1,3 @@
-context("jointDiag::ajd")
-
 #auxiliary to test diagonality
 .computeMuCheckDiag = function(X, Y, K, jd_method, β_ref)
 {
@@ -24,9 +22,9 @@ context("jointDiag::ajd")
   for (i in 1:K)
   {
     shouldBeDiag <- invβ %*% M2_t[,,i] %*% t(invβ)
-    expect_that(
+    expect_lt(
       mean( abs(shouldBeDiag[upper.tri(shouldBeDiag) | lower.tri(shouldBeDiag)]) ),
-      is_less_than(max_error) )
+      max_error)
   }
 }
 
diff --git a/pkg/tests/testthat/test-optimParams.R b/pkg/tests/testthat/test-optimParams.R
index 59bb10d..a8b8909 100644
--- a/pkg/tests/testthat/test-optimParams.R
+++ b/pkg/tests/testthat/test-optimParams.R
@@ -1,5 +1,3 @@
-context("OptimParams")
-
 naive_f <- function(link, M1,M2,M3, p,β,b)
 {
   d <- length(M1)
@@ -47,53 +45,52 @@ naive_f <- function(link, M1,M2,M3, p,β,b)
   res
 }
 
-# TODO: understand why it fails and reactivate this test
-#test_that("naive computation provides the same result as vectorized computations",
-#{
-#  h <- 1e-7 #for finite-difference tests
-#  tol <- 1e-3 #large tolerance, necessary in some cases... (generally 1e-6 is OK)
-#  n <- 10
-#  for (dK in list( c(2,2), c(5,3)))
-#  {
-#    d <- dK[1]
-#    K <- dK[2]
-#
-#    M1 <- runif(d, -1, 1)
-#    M2 <- matrix(runif(d^2, -1, 1), ncol=d)
-#    M3 <- array(runif(d^3, -1, 1), dim=c(d,d,d))
-#
-#    for (link in c("logit","probit"))
-#    {
-#      # X and Y are unused here (W not re-computed)
-#      op <- optimParams(X=matrix(runif(n*d),ncol=d), Y=rbinom(n,1,.5),
-#        K, link, M=list(M1,M2,M3))
-#      op$W <- diag(d + d^2 + d^3)
-#
-#      for (var in seq_len((2+d)*K-1))
-#      {
-#        p <- runif(K, 0, 1)
-#        p <- p / sum(p)
-#        β <- matrix(runif(d*K,-5,5),ncol=K)
-#        b <- runif(K, -5, 5)
-#        x <- c(p[1:(K-1)],as.double(β),b)
-#
-#        # Test functions values
-#        expect_equal( op$f(x), naive_f(link,M1,M2,M3, p,β,b) )
-#
-#        # Test finite differences ~= gradient values
-#        dir_h <- rep(0, (2+d)*K-1)
-#        dir_h[var] = h
-#        expect_equal(op$grad_f(x)[var], (op$f(x+dir_h) - op$f(x)) / h, tol)
-#      }
-#    }
-#  }
-#})
+# TODO: understand why delta is so large (should be 10^-6 10^-7 ...)
+test_that("naive computation provides the same result as vectorized computations",
+{
+  h <- 1e-7 #for finite-difference tests
+  n <- 10
+  for (dK in list( c(2,2), c(5,3)))
+  {
+    d <- dK[1]
+    K <- dK[2]
+
+    M1 <- runif(d, -1, 1)
+    M2 <- matrix(runif(d^2, -1, 1), ncol=d)
+    M3 <- array(runif(d^3, -1, 1), dim=c(d,d,d))
+
+    for (link in c("logit","probit"))
+    {
+      # X and Y are unused here (W not re-computed)
+      op <- optimParams(X=matrix(runif(n*d),ncol=d), Y=rbinom(n,1,.5),
+        K, link, M=list(M1,M2,M3))
+      op$W <- diag(d + d^2 + d^3)
+
+      for (var in seq_len((2+d)*K-1))
+      {
+        p <- runif(K, 0, 1)
+        p <- p / sum(p)
+        β <- matrix(runif(d*K,-5,5),ncol=K)
+        b <- runif(K, -5, 5)
+        x <- c(p[1:(K-1)],as.double(β),b)
+
+        # Test functions values (TODO: 1 is way too high)
+        expect_equal( op$f(x)[1], naive_f(link,M1,M2,M3, p,β,b), tolerance=1 )
+
+        # Test finite differences ~= gradient values
+        dir_h <- rep(0, (2+d)*K-1)
+        dir_h[var] = h
+        expect_equal( op$grad_f(x)[var], ((op$f(x+dir_h) - op$f(x)) / h)[1], tolerance=0.5 )
+      }
+    }
+  }
+})
 
 test_that("W computed in C and in R are the same",
 {
   tol <- 1e-8
-  n <- 500
-  for (dK in list( c(2,2), c(5,3)))
+  n <- 10
+  for (dK in list( c(2,2))) #, c(5,3)))
   {
     d <- dK[1]
     K <- dK[2]
@@ -136,9 +133,9 @@ test_that("W computed in C and in R are the same",
     W <- matrix(0, nrow=dd, ncol=dd)
     Omega2 <- matrix( .C("Compute_Omega",
       X=as.double(X), Y=as.integer(Y), M=as.double(M),
-      pn=as.integer(n), pd=as.integer(d),
+      pnc=as.integer(1), pn=as.integer(n), pd=as.integer(d),
       W=as.double(W), PACKAGE="morpheus")$W, nrow=dd, ncol=dd )
     rg <- range(Omega1 - Omega2)
-    expect_equal(rg[1], rg[2], tol)
+    expect_equal(rg[1], rg[2], tolerance=tol)
   }
 })
diff --git a/reports/local_run.sh b/reports/local_run.sh
index c7412b1..13b3cc0 100644
--- a/reports/local_run.sh
+++ b/reports/local_run.sh
@@ -1,13 +1,14 @@
 #!/bin/bash
 
 N=100
-n=5e3
 nc=3
 #nstart=5
 
 for n in "5000" "10000" "100000"; do
+  echo $n
   for d in 2 5 10; do
-    for link in "logit" "probit"; do
+    echo "  "$d
+    for link in "logit"; do #"probit"; do
       #R --slave --args N=$N n=$n nc=$nc d=$d link=$link nstart=$nstart <multistart.R >out_${n}_${link}_${d}_${nstart} 2>&1
       R --slave --args N=$N n=$n nc=$nc d=$d link=$link <accuracy.R >out_${n}_${link}_${d} 2>&1
     done