meta_analyze/__init__.py,sha256=NClDVn9TawqgxYoiXfO_9shCmrkRSI0GfPfrPLzDXRo,2611
meta_analyze/_version.py,sha256=tzyKZxjKttfSIiX7h4cocHDuzHZ96CNTxW8tByv-Ens,77
meta_analyze/api.py,sha256=GFFXBPmYqN8BRJtGYHlKoPHptBIBfgRxFTWa_W-Ci8E,14625
meta_analyze/binary_api.py,sha256=027raSIFBh9ddsLyH4LH2AEFaqzeTvcukj8cxPQr2OI,22309
meta_analyze/config.py,sha256=78ao8mevDflYAW_pvOdT1iuQTL-FFfTe8HHBVEF-nIU,1309
meta_analyze/continuous_api.py,sha256=f3cnU5SZ-aNblKQlpDVY8P-BsY2hXUZC2kEdxfs7ykM,12200
meta_analyze/data.py,sha256=EtTGmWtm9evVCHU3m_j-2OfTQ0N8CVRnyX_lzJHDsug,9241
meta_analyze/design_matrix.py,sha256=4W8F7ETGUjCFJ7xEZBHv825EEoAXnSW689My1siRLqQ,18411
meta_analyze/exceptions.py,sha256=h4PPm8mcxSyUQax0YrqgIhy7GCF7wj2uVL4BWmwWImA,666
meta_analyze/heterogeneity.py,sha256=JTIeuFyuOy4BzEM-suyvun9wC81mLG5ozuTVLnHxBzE,8951
meta_analyze/provenance.py,sha256=PrRubpJRn2muvJNun2juMKs-VV4QVT4T81BxvHNuKck,5858
meta_analyze/py.typed,sha256=AbpHGcgLb-kRsJGnwFEktk7uzpZOCcBY74-YBdrKVGs,1
meta_analyze/regression_api.py,sha256=5M7Y9PWkOzQbeZKRH6A9C_uck1boLsCeuul8eK1uhdU,16059
meta_analyze/regression_collinearity.py,sha256=rxgjWO3SXyeiuN9QDkpKvdfdrFzengEzDUO0q7so8tw,10367
meta_analyze/regression_contrasts.py,sha256=wRgo3HWZBXDtTcUp8szNQXYpaC3rYNQvEk3adJtca4k,12910
meta_analyze/regression_results.py,sha256=9EkWiubBwHE5QxmtuAl7E8QJMmyQ7kJesic-URt0LqQ,18648
meta_analyze/regression_sensitivity.py,sha256=t2V_xBwo9ikdrzuGHN_HXKK17I4J84c2h4md0rBe6_k,20750
meta_analyze/reporting.py,sha256=HVYJUNIilAJ9AoHoe1-GRLecQbT4XyY69xZsmbWMk1A,24058
meta_analyze/results.py,sha256=IKH3ywsLNWApqVuzUvrg9_PVJHjb-ze-jxsvwgk9tuY,22768
meta_analyze/sensitivity.py,sha256=KhVizFO8pVVzhPHvfGCk63sO9WlXEHLK8QgGrU3SZvg,21428
meta_analyze/subgroups.py,sha256=6osrJojLW8_CCDaXCxhabqecip383hqYDXryJ2TagEI,8122
meta_analyze/effect_sizes/__init__.py,sha256=tYlgVfGehiCdPjU9_1JlDUpgXO9rMyUCc98zcrLuPCA,415
meta_analyze/effect_sizes/binary.py,sha256=UBigzYKvomFZvq1cezUUm3301WfgjnS24zOBaiigxME,19305
meta_analyze/effect_sizes/continuous.py,sha256=Bw_njTAmoDzpuWSH2CXeBZc3c0pqAg25TL95VsAqPm8,10883
meta_analyze/estimators/__init__.py,sha256=Bid3GXsWfZw6RKgrZOXFH6iKXFtaDBXJjZneo1BkO70,791
meta_analyze/estimators/inverse_variance.py,sha256=0XvsxkU5POq04gCQMeMv2X28ZML1-YksG7lbaOluuLY,6646
meta_analyze/estimators/mantel_haenszel.py,sha256=h24eWTIQMDjG4_qjSWuw-dxHBnCj7SvgDh_Yays3-6M,6911
meta_analyze/estimators/meta_regression.py,sha256=uNWkMc0ZoqcCvuGDYNGVurCtn--H2MpqI8Rz7SYYFn0,17847
meta_analyze/estimators/peto.py,sha256=3KpBcfE0fOZpYaF7IqQqCz4XzfCf5XrdBxmbo0fbG6Y,4982
meta_analyze/estimators/tau2.py,sha256=kqqakr3GBQQ4RrEWQeWXHvFRgpgZuodKArTRiIZjI4Y,6254
meta_analyze/plotting/__init__.py,sha256=SQLBqSIv1AEXF8a32vQpJ7MXJsAHpxpJ_3_Rj60tVik,298
meta_analyze/plotting/_utils.py,sha256=BjpnZxyWAGlByXhjYIR3nmr5IzLCgLtR626woBy4jVo,2434
meta_analyze/plotting/forest.py,sha256=joY3z7fPPDmfcY1jON7RmcA26ItRlbh3rGVI9mNrGVA,6726
meta_analyze/plotting/funnel.py,sha256=Mdy1O5uVw4OZF8PvXiDyG7AMANz7vkef9ru-D61_k1Q,5577
meta_analyze/plotting/regression.py,sha256=UEKTEyMc0D1Oca3XJJVc61mrf2kpNmq6WVFokUXSECI,4266
meta_analyze/plotting/subgroup_forest.py,sha256=OzhrhxhZb0YqKy_Gn4fONgcIo26TI1Opm6_a-mfV_is,9465
pymetaanalysis-0.6.0.dist-info/METADATA,sha256=hNrYENX8cmLOPiIr1Lsi0kfmxPIzJ_QVXt9K8yC8EyA,11809
pymetaanalysis-0.6.0.dist-info/WHEEL,sha256=lCkmxWfQsSc9CfIClYeavTdQeEX2toPqufh9gI35EQA,87
pymetaanalysis-0.6.0.dist-info/licenses/LICENSE,sha256=CBE_u7LfEteKb_1My8s9obP0YkjJVzcWlgHSDiqa9ts,1084
pymetaanalysis-0.6.0.dist-info/RECORD,,
