diff --git a/.flake8 b/.flake8
new file mode 100644
index 0000000..905c8bb
--- /dev/null
+++ b/.flake8
@@ -0,0 +1,2 @@
+[flake8]
+max-line-length = 160
\ No newline at end of file
diff --git a/.github/workflows/python-build.yml b/.github/workflows/python-build.yml
index 4324e26..547b8cd 100644
--- a/.github/workflows/python-build.yml
+++ b/.github/workflows/python-build.yml
@@ -8,7 +8,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ 3.6, 3.7, 3.8, 3.9 ]
+        python-version: [ 3.7, 3.8, 3.9, "3.10" ]
 
     steps:
     - uses: actions/checkout@v2
diff --git a/.gitignore b/.gitignore
index 78a71c8..bfcaa21 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,6 +1,8 @@
 # Standard Python ignores
 # -----------------------
 
+Pipfile.lock
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
diff --git a/Pipfile.lock b/Pipfile.lock
deleted file mode 100644
index 37495ed..0000000
--- a/Pipfile.lock
+++ /dev/null
@@ -1,728 +0,0 @@
-{
-    "_meta": {
-        "hash": {
-            "sha256": "14fabe58d787a63d8216f5de193376d1d2f7c63d3725dfdf7aa35c56c81eeb74"
-        },
-        "pipfile-spec": 6,
-        "requires": {},
-        "sources": [
-            {
-                "name": "pypi",
-                "url": "https://pypi.org/simple",
-                "verify_ssl": true
-            }
-        ]
-    },
-    "default": {
-        "numpy": {
-            "hashes": [
-                "sha256:17e5226674f6ea79e14e3b91bfbc153fdf3ac13f5cc54ee7bc8fdbe820a32da0",
-                "sha256:2bd879d3ca4b6f39b7770829f73278b7c5e248c91d538aab1e506c628353e47f",
-                "sha256:4f41f5bf20d9a521f8cab3a34557cd77b6f205ab2116651f12959714494268b0",
-                "sha256:5593f67e66dea4e237f5af998d31a43e447786b2154ba1ad833676c788f37cde",
-                "sha256:5e28cd64624dc2354a349152599e55308eb6ca95a13ce6a7d5679ebff2962913",
-                "sha256:633679a472934b1c20a12ed0c9a6c9eb167fbb4cb89031939bfd03dd9dbc62b8",
-                "sha256:806970e69106556d1dd200e26647e9bee5e2b3f1814f9da104a943e8d548ca38",
-                "sha256:806cc25d5c43e240db709875e947076b2826f47c2c340a5a2f36da5bb10c58d6",
-                "sha256:8247f01c4721479e482cc2f9f7d973f3f47810cbc8c65e38fd1bbd3141cc9842",
-                "sha256:8ebf7e194b89bc66b78475bd3624d92980fca4e5bb86dda08d677d786fefc414",
-                "sha256:8ecb818231afe5f0f568c81f12ce50f2b828ff2b27487520d85eb44c71313b9e",
-                "sha256:8f9d84a24889ebb4c641a9b99e54adb8cab50972f0166a3abc14c3b93163f074",
-                "sha256:909c56c4d4341ec8315291a105169d8aae732cfb4c250fbc375a1efb7a844f8f",
-                "sha256:9b83d48e464f393d46e8dd8171687394d39bc5abfe2978896b77dc2604e8635d",
-                "sha256:ac987b35df8c2a2eab495ee206658117e9ce867acf3ccb376a19e83070e69418",
-                "sha256:b78d00e48261fbbd04aa0d7427cf78d18401ee0abd89c7559bbf422e5b1c7d01",
-                "sha256:b8b97a8a87cadcd3f94659b4ef6ec056261fa1e1c3317f4193ac231d4df70215",
-                "sha256:bd5b7ccae24e3d8501ee5563e82febc1771e73bd268eef82a1e8d2b4d556ae66",
-                "sha256:bdc02c0235b261925102b1bd586579b7158e9d0d07ecb61148a1799214a4afd5",
-                "sha256:be6b350dfbc7f708d9d853663772a9310783ea58f6035eec649fb9c4371b5389",
-                "sha256:c403c81bb8ffb1c993d0165a11493fd4bf1353d258f6997b3ee288b0a48fce77",
-                "sha256:cf8c6aed12a935abf2e290860af8e77b26a042eb7f2582ff83dc7ed5f963340c",
-                "sha256:d98addfd3c8728ee8b2c49126f3c44c703e2b005d4a95998e2167af176a9e722",
-                "sha256:dc76bca1ca98f4b122114435f83f1fcf3c0fe48e4e6f660e07996abf2f53903c",
-                "sha256:dec198619b7dbd6db58603cd256e092bcadef22a796f778bf87f8592b468441d",
-                "sha256:df28dda02c9328e122661f399f7655cdcbcf22ea42daa3650a26bce08a187450",
-                "sha256:e603ca1fb47b913942f3e660a15e55a9ebca906857edfea476ae5f0fe9b457d5",
-                "sha256:ecfdd68d334a6b97472ed032b5b37a30d8217c097acfff15e8452c710e775524"
-            ],
-            "markers": "platform_machine != 'aarch64' and platform_machine != 'arm64' and python_version < '3.10'",
-            "version": "==1.23.2"
-        },
-        "pandas": {
-            "hashes": [
-                "sha256:07238a58d7cbc8a004855ade7b75bbd22c0db4b0ffccc721556bab8a095515f6",
-                "sha256:0daf876dba6c622154b2e6741f29e87161f844e64f84801554f879d27ba63c0d",
-                "sha256:16ad23db55efcc93fa878f7837267973b61ea85d244fc5ff0ccbcfa5638706c5",
-                "sha256:1d9382f72a4f0e93909feece6fef5500e838ce1c355a581b3d8f259839f2ea76",
-                "sha256:24ea75f47bbd5574675dae21d51779a4948715416413b30614c1e8b480909f81",
-                "sha256:2893e923472a5e090c2d5e8db83e8f907364ec048572084c7d10ef93546be6d1",
-                "sha256:2ff7788468e75917574f080cd4681b27e1a7bf36461fe968b49a87b5a54d007c",
-                "sha256:41fc406e374590a3d492325b889a2686b31e7a7780bec83db2512988550dadbf",
-                "sha256:48350592665ea3cbcd07efc8c12ff12d89be09cd47231c7925e3b8afada9d50d",
-                "sha256:605d572126eb4ab2eadf5c59d5d69f0608df2bf7bcad5c5880a47a20a0699e3e",
-                "sha256:6dfbf16b1ea4f4d0ee11084d9c026340514d1d30270eaa82a9f1297b6c8ecbf0",
-                "sha256:6f803320c9da732cc79210d7e8cc5c8019aad512589c910c66529eb1b1818230",
-                "sha256:721a3dd2f06ef942f83a819c0f3f6a648b2830b191a72bbe9451bcd49c3bd42e",
-                "sha256:755679c49460bd0d2f837ab99f0a26948e68fa0718b7e42afbabd074d945bf84",
-                "sha256:78b00429161ccb0da252229bcda8010b445c4bf924e721265bec5a6e96a92e92",
-                "sha256:958a0588149190c22cdebbc0797e01972950c927a11a900fe6c2296f207b1d6f",
-                "sha256:a3924692160e3d847e18702bb048dc38e0e13411d2b503fecb1adf0fcf950ba4",
-                "sha256:d51674ed8e2551ef7773820ef5dab9322be0828629f2cbf8d1fc31a0c4fed640",
-                "sha256:d5ebc990bd34f4ac3c73a2724c2dcc9ee7bf1ce6cf08e87bb25c6ad33507e318",
-                "sha256:d6c0106415ff1a10c326c49bc5dd9ea8b9897a6ca0c8688eb9c30ddec49535ef",
-                "sha256:e48fbb64165cda451c06a0f9e4c7a16b534fcabd32546d531b3c240ce2844112"
-            ],
-            "index": "pypi",
-            "version": "==1.4.3"
-        },
-        "python-dateutil": {
-            "hashes": [
-                "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86",
-                "sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"
-            ],
-            "version": "==2.8.2"
-        },
-        "pytz": {
-            "hashes": [
-                "sha256:220f481bdafa09c3955dfbdddb7b57780e9a94f5127e35456a48589b9e0c0197",
-                "sha256:cea221417204f2d1a2aa03ddae3e867921971d0d76f14d87abb4414415bbdcf5"
-            ],
-            "version": "==2022.2.1"
-        },
-        "scipy": {
-            "hashes": [
-                "sha256:01c2015e132774feefe059d5354055fec6b751d7a7d70ad2cf5ce314e7426e2a",
-                "sha256:0424d1bbbfa51d5ddaa16d067fd593863c9f2fb7c6840c32f8a08a8832f8e7a4",
-                "sha256:10417935486b320d98536d732a58362e3d37e84add98c251e070c59a6bfe0863",
-                "sha256:12005d30894e4fe7b247f7233ba0801a341f887b62e2eb99034dd6f2a8a33ad6",
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-                "sha256:8f2232c9d9119ec356240255a715a289b3a33be828c3e4abac11fd052ce15b1e",
-                "sha256:97a1f1e51ea30782d7baa8d0c52f72c3f9f05cb609cf1b990664231c5102bccd",
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-                "sha256:bd490f77f35800d5620f4d9af669e372d9a88db1f76ef219e1609cc4ecdd1a24",
-                "sha256:c0dfd7d2429452e7e94904c6a3af63cbaa3cf51b348bd9d35b42db7e9ad42791",
-                "sha256:d3a326673ac5afa9ef5613a61626b9ec15c8f7222b4ecd1ce0fd8fcba7b83c59",
-                "sha256:e2004d2a3c397b26ca78e67c9d320153a1a9b71ae713ad33f4a3a3ab3d79cc65",
-                "sha256:e2ac088ea4aa61115b96b47f5f3d94b3fa29554340b6629cd2bfe6b0521ee33b",
-                "sha256:f7c3c578ff556333f3890c2df6c056955d53537bb176698359088108af73a58f",
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-            ],
-            "index": "pypi",
-            "version": "==1.9.0"
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-        "six": {
-            "hashes": [
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-                "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"
-            ],
-            "version": "==1.16.0"
-        },
-        "wheel": {
-            "hashes": [
-                "sha256:4bdcd7d840138086126cd09254dc6195fb4fc6f01c050a1d7236f2630db1d22a",
-                "sha256:e9a504e793efbca1b8e0e9cb979a249cf4a0a7b5b8c9e8b65a5e39d49529c1c4"
-            ],
-            "index": "pypi",
-            "version": "==0.37.1"
-        }
-    },
-    "develop": {
-        "astroid": {
-            "hashes": [
-                "sha256:86b0a340a512c65abf4368b80252754cda17c02cdbbd3f587dddf98112233e7b",
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-            "version": "==2.11.7"
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-        "bleach": {
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-                "sha256:ef3f72c9666bba2bab70d2a8b79f2c6d2c1a42a7f7e2b0ec83bb2f9e383950af"
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-        },
-        "zipp": {
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-                "sha256:05b45f1ee8f807d0cc928485ca40a07cb491cf092ff587c0df9cb1fd154848d2",
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diff --git a/README.md b/README.md
index 56cd8a8..d5d7773 100644
--- a/README.md
+++ b/README.md
@@ -136,7 +136,7 @@ The following association measures are currently implemented (v0.2.2):
   - **Liddell** (`liddell`)
   - **minimum sensitivity** (`min_sensitivity`)
   - [**log-ratio**](http://cass.lancs.ac.uk/log-ratio-an-informal-introduction/) (`log_ratio`)
-    - parameter: `disc`
+    - parameters: `disc`, `discounting`
   - **Dice coefficient** (`dice`)
 - information theory:
   - **mutual information** (`mutual_information`)
diff --git a/association_measures/measures.py b/association_measures/measures.py
index baf7c8a..5beff2d 100644
--- a/association_measures/measures.py
+++ b/association_measures/measures.py
@@ -4,7 +4,7 @@
 """
 
 import numpy as np
-from pandas import concat
+from pandas import concat, merge
 from scipy.stats import norm, beta
 from warnings import warn
 
@@ -47,8 +47,9 @@ def list_measures():
 
 def score(df, measures=None, f1=None, N=None, N1=None, N2=None,
           freq=True, per_million=True, digits=6, disc=.001,
-          signed=True, alpha=.001, correct='Bonferroni',
-          boundary='normal', vocab=None, one_sided=False):
+          discounting='Walter1975', signed=True, alpha=.001,
+          correct='Bonferroni', boundary='poisson', vocab=None,
+          one_sided=False):
     """Calculate a list of association measures on columns of df. Defaults
     to all available (and numerically stable) measures.
 
@@ -67,6 +68,7 @@ def score(df, measures=None, f1=None, N=None, N1=None, N2=None,
 
     Further keyword arguments will be passed to the respective measures:
     :param float disc: discounting (or smoothing) parameter for O11 == 0 (and O21 == 0)
+    :param str discounting: LR: discounting strategy (Walter1975 vs. Hardie2014)
     :param bool signed: enforce negative values for rows with O11 < E11?
     :param float alpha: CLR: significance level
     :param str boundary: CLR: exact CI boundary of [poisson] distribution or [normal] approximation?
@@ -93,14 +95,22 @@ def score(df, measures=None, f1=None, N=None, N1=None, N2=None,
     else:
         measures = [ams_all[k] for k in ams_all]
 
+    # reduce df to unique frequency signatures
+    vocab = len(df) if vocab is None else vocab
+    df_reduced = df.drop_duplicates(subset=list(freq_columns)).copy()
+
     # calculate measures
     for measure in measures:
-        df[measure.__name__] = measure(
-            df, disc=disc, signed=signed, alpha=alpha,
+        df_reduced[measure.__name__] = measure(
+            df_reduced, disc=disc, discounting=discounting, signed=signed, alpha=alpha,
             correct=correct, boundary=boundary, vocab=vocab, one_sided=one_sided
         )
 
-    # frequency columns?
+    # join on frequency columns (NB: thanks to pandas API, we have to take care of index names ourselves)
+    index_names = ['index'] if df.index.names == [None] else df.index.names
+    df = merge(df.reset_index(), df_reduced, how='left', on=list(freq_columns)).set_index(index_names)
+
+    # keep frequency columns?
     if not freq:
         df = df.drop(freq_columns, axis=1)
     else:
@@ -263,20 +273,25 @@ def dice(df, **kwargs):
     return am
 
 
-def log_ratio(df, disc=.5, **kwargs):
+def log_ratio(df, disc=.5, discounting='Walter1975', **kwargs):
     """Calculate log-ratio, i.e. binary logarithm of relative risk
 
     :param DataFrame df: pd.DataFrame with columns O11, O21, R1, R2
     :param float disc: discounting (or smoothing) parameter for O11 == 0 and O21 == 0
+    :param str discounting: discounting according to Walter1975 or Hardie2014?
     :return: log-ratio
     :rtype: pd.Series
     """
 
-    # questionable discounting according to Hardie (2014)
-    O11_disc = df['O11'].where(df['O11'] != 0, disc)
-    O21_disc = df['O21'].where(df['O21'] != 0, disc)
+    if discounting == 'Walter1975':
+        # mathematically sensible discounting according to Walter (1975)
+        am = np.log2(((df['O11'] + disc) / (df['R1'] + disc)) / ((df['O21'] + disc) / (df['R2'] + disc)))
 
-    am = np.log2((O11_disc / O21_disc) / (df['R1'] / df['R2']))
+    elif discounting == 'Hardie2014':
+        # questionable discounting according to Hardie (2014)
+        O11_disc = df['O11'].where(df['O11'] != 0, disc)
+        O21_disc = df['O21'].where(df['O21'] != 0, disc)
+        am = np.log2((O11_disc / O21_disc) / (df['R1'] / df['R2']))
 
     return am
 
@@ -335,34 +350,6 @@ def binomial_likelihood(df, **kwargs):
 # CONSERVATIVE ESTIMATES #
 ##########################
 
-def get_poisson_ci_boundary(alpha, O11, R1, O21, R2):
-    """
-    Get the lower (if O11 / R1 >= O21 / R2) or upper (else) bound of
-    the CI of a Poisson distribution
-
-    :param float alpha: sig. level
-    :param int O11:
-    :param int R1:
-    :param int O21:
-    :param int R2:
-    """
-
-    if O11 == O21 == 0:
-        return 0
-
-    if (O11 / R1) >= (O21 / R2):
-        lower = beta.ppf(alpha, O11, O21 + 1)
-        boundary = max(np.log2((R2 / R1) * lower / (1 - lower)), 0)
-    else:
-        upper = beta.ppf(1 - alpha, O11 + 1, O21)
-        boundary = min(np.log2((R2 / R1) * upper / (1 - upper)), 0)
-
-    return boundary
-
-
-BOUNDARY = np.vectorize(get_poisson_ci_boundary, otypes=[float])
-
-
 def conservative_log_ratio(df, disc=.5, alpha=.001, boundary='normal',
                            correct='Bonferroni', vocab=None,
                            one_sided=False, **kwargs):
@@ -407,7 +394,21 @@ def conservative_log_ratio(df, disc=.5, alpha=.001, boundary='normal',
 
     # Poisson approximation (Evert 2022)
     if boundary == 'poisson':
-        clrr = BOUNDARY(alpha, df['O11'], df['R1'], df['O21'], df['R2'])
+
+        # only calculate where_lower
+        lower = beta.ppf(alpha, df['O11'], df['O21'] + 1)
+        lower_boundary = np.log2((df['R2'] / df['R1']) * lower / (1 - lower)).clip(lower=0)
+
+        # only calculate where_upper
+        upper = beta.ppf(1 - alpha, df['O11'] + 1, df['O21'])
+        upper_boundary = np.log2((df['R2'] / df['R1']) * upper / (1 - upper)).clip(upper=0)
+
+        # combine, set to 0 where (df['O11'] == 0) & (df['O12'] == 0)
+        clrr = lower_boundary.where(
+            (df['O11'] / df['R1']) >= (df['O21'] / df['R2']),
+            upper_boundary
+        )
+        clrr = clrr.where(~((df['O11'] == 0) & (df['O12'] == 0)), 0).fillna(0)
 
     # Normal approximation (Hardie 2014)
     elif boundary == 'normal':
diff --git a/association_measures/version.py b/association_measures/version.py
index c3ee62f..c55c3a9 100644
--- a/association_measures/version.py
+++ b/association_measures/version.py
@@ -2,5 +2,5 @@
 Association measures are mathematical formulae that interpret cooccurrence frequency data.
 """
 
-VERSION = (0, 2, 6)
+VERSION = (0, 2, 7)
 __version__ = '.'.join(map(str, VERSION))
diff --git a/performance.md b/performance.md
new file mode 100644
index 0000000..83b529d
--- /dev/null
+++ b/performance.md
@@ -0,0 +1,47 @@
+# Performance
+- performance is calculated on a Lenovo X1 Carbon (10th generation, i7)
+- input data are 24,167 observations from [brown.csv](tests/data/brown.csv)
+- NB: dataframe contains 4241 duplicated frequency signatures (for which calculation will only be run once since v0.2.7)
+- for each measure, we report time needed for 1000 scorings of the whole dataframe
+
+## v0.2.7
+- major performance improvement regarding conservative log-ratio with Poisson boundary (factor 50)
+```
+settings: iterations=1000, df_size=24167
+-  0.0871 :: contingency_table
+-  1.5258 :: expected_frequencies
+-  0.1507 :: z_score
+-  0.2899 :: t_score
+-  1.7406 :: log_likelihood
+-  0.6125 :: simple_ll
+-  1.2981 :: min_sensitivity
+-  0.2584 :: liddell
+-  0.2491 :: dice
+-  0.4460 :: log_ratio
+-  4.5788 :: binomial_likelihood
+-  2.1891 :: conservative_log_ratio
+- 29.8616 :: conservative_log_ratio_poisson
+-  0.3702 :: mutual_information
+-  0.4314 :: local_mutual_information
+```
+
+## v0.2.6
+```
+Calculate contingency_table (iterations=1000, df_size=24168):         0.0873531000688672
+Calculate expected_frequencies (iterations=1000, df_size=24168):      1.5203204130521044
+Calculate z_score (iterations=1000, df_size=24168):                   0.14853612298611552
+Calculate t_score (iterations=1000, df_size=24168):                   0.2881241790018976
+Calculate log_likelihood (iterations=1000, df_size=24168):            1.7284309939714149
+Calculate simple_ll (iterations=1000, df_size=24168):                 0.6111006899736822
+Calculate min_sensitivity (iterations=1000, df_size=24168):           1.3227944150567055
+Calculate liddell (iterations=1000, df_size=24168):                   0.25499376200605184
+Calculate dice (iterations=1000, df_size=24168):                      0.2465739679755643
+Calculate log_ratio (iterations=1000, df_size=24168):                 0.751795751042664
+Calculate binomial_likelihood (iterations=1000, df_size=24168):       4.606213430990465
+Calculate conservative_log_ratio (iterations=1000, df_size=24168):    2.2395021530101076
+Calculate mutual_information (iterations=1000, df_size=24168):        0.3618475969415158
+Calculate local_mutual_information (iterations=1000, df_size=24168):  0.41407940594945103
+```
+additionally:
+- conservative log ratio with Poisson boundary: ~1.5s for 1 iteration
+- hypergeometric likelihood: ~2.5s for 1 iteration
diff --git a/performance.py b/performance.py
old mode 100755
new mode 100644
index 73b699a..23d9ebc
--- a/performance.py
+++ b/performance.py
@@ -70,7 +70,7 @@
         'code': 'am.log_ratio(df)'
     },
     # likelihood measures
-    # ~2.5s for a ~25,000 rows on 8 threads
+    # ~2.5s for 1x ~25,000
     # {
     #     'name': 'hypergeometric_likelihood',
     #     'code': 'am.hypergeometric_likelihood(df)'
@@ -84,11 +84,10 @@
         'name': 'conservative_log_ratio',
         'code': 'am.conservative_log_ratio(df)'
     },
-    # ~1.5s for a ~25,000 rows on 8 threads
-    # {
-    #     'name': 'conservative_log_ratio_poisson',
-    #     'code': 'am.conservative_log_ratio(df, boundary="poisson")'
-    # },
+    {
+        'name': 'conservative_log_ratio_poisson',
+        'code': 'am.conservative_log_ratio(df, boundary="poisson")'
+    },
     # information theory
     {
         'name': 'mutual_information',
@@ -100,8 +99,9 @@
     },
 ]
 
+size = 24167
+print(f'settings: iterations={iterations}, df_size={size}')
 for code in codes:
     res = timeit.timeit(setup=setup, stmt=code['code'], number=iterations)
-    print('Calculate {func} (iterations={iter}, df_size={size}): {res}'.format(
-        iter=iterations, size=24168, res=res, func=code['name']
-    ))
+    func = code['name']
+    print(f'- {res:7.4f} :: {func}')
diff --git a/setup.py b/setup.py
index d3886ac..0305884 100644
--- a/setup.py
+++ b/setup.py
@@ -1,7 +1,5 @@
 #!/usr/bin/env python3
 
-
-import io
 import os
 import sys
 from setuptools import find_packages, Command
@@ -101,10 +99,10 @@ def run(self):
         'License :: OSI Approved :: MIT License',
         'Programming Language :: Python',
         'Programming Language :: Python :: 3',
-        'Programming Language :: Python :: 3.6',
         'Programming Language :: Python :: 3.7',
         'Programming Language :: Python :: 3.8',
         'Programming Language :: Python :: 3.9',
+        'Programming Language :: Python :: 3.10',
         'Programming Language :: Cython',
     ],
 )
diff --git a/tests/test_measures.py b/tests/test_measures.py
index 06cd623..81666ef 100644
--- a/tests/test_measures.py
+++ b/tests/test_measures.py
@@ -145,7 +145,7 @@ def test_t_score_invalid(invalid_dataframe):
 def test_t_score_zero(zero_dataframe):
     df = zero_dataframe
     df_ams = am.score(df, ['t_score'], disc=.5)
-    df_ams['t_score'][0] == 15.532438056926377
+    df_ams['t_score'][0] == 15.532438
 
 
 ##########
@@ -173,7 +173,7 @@ def test_z_score_nan(invalid_dataframe):
 def test_z_score_zero(zero_dataframe):
     df = zero_dataframe
     df_ams = am.score(df, ['z_score'])
-    df_ams['z_score'].iloc[0] == 16.675431342469118
+    df_ams['z_score'].iloc[0] == 16.675431
 
 
 #################
@@ -234,7 +234,7 @@ def test_simple_ll_zero(zero_dataframe):
 def test_hypergeometric_likelihood(fixed_dataframe):
     df = fq.expected_frequencies(fixed_dataframe, observed=True)
     df_ams = am.hypergeometric_likelihood(df)
-    assert df_ams[0] == 5.776904234533874e-14
+    assert round(df_ams[0], 20) == 5.776904e-14
 
 
 @pytest.mark.choose
@@ -264,7 +264,7 @@ def test_hypergeometric_likelihood_zero(zero_dataframe):
 def test_binomial_likelihood(fixed_dataframe):
     df = fq.expected_frequencies(fixed_dataframe, observed=True)
     df_ams = am.binomial_likelihood(df)
-    assert df_ams[0] == 7.006035693977206e-08
+    assert round(df_ams[0], 14) == 7.006036e-08
 
 
 @pytest.mark.choose
@@ -275,7 +275,7 @@ def test_binomial_likelihood_brown(brown_dataframe):
     df = df.join(fq.expected_frequencies(df), rsuffix='_')
     df = df.head(100)
     df['binomial_likelihood'] = am.binomial_likelihood(df)
-    assert df['binomial_likelihood'][0] == 0.00810143610212444
+    assert round(df['binomial_likelihood'][0], 6) == 0.008101
 
 
 @pytest.mark.choose
@@ -306,7 +306,7 @@ def test_binomial_likelihood_zero(zero_dataframe):
 def test_log_ratio(fixed_dataframe):
 
     df = fixed_dataframe
-    df_ams = am.score(df, ['log_ratio'], disc=.5)
+    df_ams = am.score(df, ['log_ratio'], disc=.5, discounting='Hardie2014')
     assert df_ams['log_ratio'][0] == 7.491853
 
 
@@ -324,7 +324,7 @@ def test_log_ratio_invalid(invalid_dataframe):
 def test_log_ratio_zero(zero_dataframe):
 
     df = zero_dataframe
-    df_ams = am.score(df, ['log_ratio'], disc=.5)
+    df_ams = am.score(df, ['log_ratio'], disc=.5, discounting='Hardie2014')
     assert df_ams['log_ratio'][0] == 12.03645
 
 
@@ -336,7 +336,7 @@ def test_log_ratio_zero(zero_dataframe):
 def test_conservative_log_ratio(fixed_dataframe):
 
     df = fixed_dataframe
-    df_ams = am.score(df, ['log_ratio', 'conservative_log_ratio'], disc=.5, alpha=.01)
+    df_ams = am.score(df, ['log_ratio', 'conservative_log_ratio'], boundary='normal', disc=.5, alpha=.01)
     assert (abs(df_ams['log_ratio']) >= abs(df_ams['conservative_log_ratio'])).all()
     assert df_ams['conservative_log_ratio'].iloc[0] == 0.796936
 
@@ -346,7 +346,7 @@ def test_conservative_log_ratio_zero(zero_dataframe):
 
     df = zero_dataframe
     df_ams = am.score(df, ['log_ratio', 'conservative_log_ratio'])
-    assert((abs(df_ams['log_ratio']) >= abs(df_ams['conservative_log_ratio'])).all())
+    assert (abs(df_ams['log_ratio']) >= abs(df_ams['conservative_log_ratio'])).all()
 
 
 @pytest.mark.conservative_log_ratio
@@ -354,7 +354,7 @@ def test_conservative_log_ratio_zero_poisson(zero_dataframe):
 
     df = zero_dataframe
     df_ams = am.score(df, ['log_ratio', 'conservative_log_ratio'], boundary='poisson')
-    assert((abs(df_ams['log_ratio']) >= abs(df_ams['conservative_log_ratio'])).all())
+    assert (abs(df_ams['log_ratio']) >= abs(df_ams['conservative_log_ratio'])).all()
 
 
 @pytest.mark.conservative_log_ratio
@@ -362,18 +362,18 @@ def test_conservative_log_ratio_zero_poisson_sig(zero_dataframe_sig):
 
     df = zero_dataframe_sig
     df_ams = am.score(df, ['log_ratio', 'conservative_log_ratio'], boundary='poisson')
-    assert((abs(df_ams['log_ratio']) >= abs(df_ams['conservative_log_ratio'])).all())
+    assert (abs(df_ams['log_ratio']) >= abs(df_ams['conservative_log_ratio'])).all()
 
 
 @pytest.mark.conservative_log_ratio
 def test_conservative_log_ratio_one_sided(fixed_dataframe):
 
     df = fq.expected_frequencies(fixed_dataframe, observed=True)
-    df_ams = am.score(df, ['conservative_log_ratio'])
+    df_ams = am.score(df, ['conservative_log_ratio'], boundary='normal')
     df_am = am.conservative_log_ratio(df, one_sided=True)
     df_am.name = 'clr_one_sided'
     df_ams = df_ams.join(df_am)
-    assert((abs(df_ams['conservative_log_ratio']) <= abs(df_ams['clr_one_sided'])).all())
+    assert (abs(df_ams['conservative_log_ratio']) <= abs(df_ams['clr_one_sided'])).all()
 
 
 @pytest.mark.conservative_log_ratio
@@ -381,10 +381,10 @@ def test_conservative_log_ratio_boundaries(brown_dataframe):
 
     df = brown_dataframe
     df_ams = am.score(df, ['conservative_log_ratio'])
-    df_am = am.score(df, ['conservative_log_ratio'], boundary="poisson")['conservative_log_ratio']
-    df_am.name = 'clr_poisson'
+    df_am = am.score(df, ['conservative_log_ratio'], boundary="normal")['conservative_log_ratio']
+    df_am.name = 'clr_normal'
     df_ams = df_ams.join(df_am)
-    assert (df_ams['conservative_log_ratio'] == 0).sum() < (df_ams['clr_poisson'] == 0).sum()
+    assert (df_ams['clr_normal'] == 0).sum() < (df_ams['conservative_log_ratio'] == 0).sum()
 
 
 ###################
@@ -445,7 +445,8 @@ def test_measures_ucs_gold(ucs_dataframe):
 def test_measures_log_ratio_gold(log_ratio_dataframe):
 
     df = log_ratio_dataframe
-    df = df.join(am.score(df, ['log_ratio', 'conservative_log_ratio'], disc=.5, alpha=.01, freq=False))
+    df = df.join(am.score(df, ['log_ratio', 'conservative_log_ratio'], boundary='normal',
+                          discounting='Hardie2014', disc=.5, alpha=.01, freq=False))
 
     for r, assoc in [('lr', 'log_ratio'),
                      ('clr', 'conservative_log_ratio')]:
@@ -454,11 +455,11 @@ def test_measures_log_ratio_gold(log_ratio_dataframe):
 
 
 @pytest.mark.gold
-def test_measures_lrc(log_ratio_dataframe):
+def test_measures_lrc_gold(log_ratio_dataframe):
 
     # original implementation with normal approximation
     df = log_ratio_dataframe
-    df = df.join(am.score(df, ['conservative_log_ratio'], alpha=.05, freq=False))
+    df = df.join(am.score(df, ['conservative_log_ratio'], boundary='normal', alpha=.05, freq=False))
     assert df['conservative_log_ratio'].equals(round(df['lrc.normal'], 6))
 
     # implementation with poisson approximation