From 58be72af6cb11495ed3d846d427e18a6a6d90143 Mon Sep 17 00:00:00 2001
From: getzze <getzze@gmail.com>
Date: Fri, 22 Oct 2021 16:48:21 +0100
Subject: [PATCH] Bump to 0.4.2

---
 .github/workflows/CI.yml | 2 +-
 HISTORY.md               | 4 ++++
 Project.toml             | 4 ++--
 src/univariate.jl        | 3 ++-
 4 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml
index abaff4d..c0423c8 100644
--- a/.github/workflows/CI.yml
+++ b/.github/workflows/CI.yml
@@ -10,7 +10,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.0'
+          - '1.1'
           - '1'  # latest stable 1.x release
           - 'nightly'
         os:
diff --git a/HISTORY.md b/HISTORY.md
index abe9ca1..9538aca 100644
--- a/HISTORY.md
+++ b/HISTORY.md
@@ -1,3 +1,7 @@
+v0.4.2 (2021-10-22)
+-------------------
+* Minimal compatibility set to julia 1.1 (because of Tulip.jl>=0.6)
+
 v0.4.1 (2021-09-19)
 -------------------
 * Correctly handle multidimensional arrays with univariate robust functions.
diff --git a/Project.toml b/Project.toml
index 640cce1..5eb3096 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "RobustModels"
 uuid = "d6ea1423-9682-4bbd-952f-b1577cbf8c98"
 authors = ["bertrand <bertrand.lacoste@gmail.com>"]
-version = "0.4.1"
+version = "0.4.2"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
@@ -20,7 +20,7 @@ StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
 Tulip = "6dd1b50a-3aae-11e9-10b5-ef983d2400fa"
 
 [compat]
-julia = "1.0"
+julia = "1.1"
 
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
diff --git a/src/univariate.jl b/src/univariate.jl
index 4b513f8..aa9922e 100644
--- a/src/univariate.jl
+++ b/src/univariate.jl
@@ -14,13 +14,14 @@ end
 function _model_from_univariate(
     est::AbstractEstimator,
     x::AbstractVector;
+    method=:cg, # use :cg instead of :chol to avoid PosDefException
     dims=nothing, # explicit, so it is not passed to `rlm`
     kwargs...,
 )
 
     check_l1loss(est)
     X = ones(eltype(x), (size(x, 1), 1))
-    m = rlm(X, x, est; kwargs...)
+    m = rlm(X, x, est; method=method, kwargs...)
 end
 
 function _mean(est::AbstractEstimator, x::AbstractVector; kwargs...)