diff --git a/.coveragerc b/.coveragerc
index 5413503a..5ee79611 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -3,8 +3,8 @@ branch = True
 omit =
     */pycm/__main__.py
     */pycm/__init__.py
-	*/pycm/pycm_profile.py
-	*/pycm/pycm_test.py
+	*/pycm/profile.py
+	*/pycm/basic_test.py
 [report]
 # Regexes for lines to exclude from consideration
 exclude_lines =
diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md
index 3efe6d3a..8942bee4 100644
--- a/.github/CONTRIBUTING.md
+++ b/.github/CONTRIBUTING.md
@@ -1,4 +1,6 @@
-# Contribution			
+# Contribution
+
+**Last Update: 2024-10-08**			
 
 Changes and improvements are more than welcome! ❤️ Feel free to fork and open a pull request.		
 
@@ -26,18 +28,18 @@ Please consider the following :
 
 ## Class statistic 
 
-1. Add new functions to `pycm_class_func.py`
-2. Update `CLASS_PARAMS` dictionary in `pycm_param.py`
-3. Update `class_statistics` function in `pycm_class_func.py`
+1. Add new functions to `class_funcs.py`
+2. Update `CLASS_PARAMS` dictionary in `params.py`
+3. Update `class_statistics` function in `class_funcs.py`
 	- Call statistic function and store result in `result` dictionary
-4. Update `PARAMS_DESCRIPTION` dictionary in `pycm_param.py` by a short description
-	- If you don't want capitalization, update `CAPITALIZE_FILTER` list in `pycm_param.py` (*Optional*)
+4. Update `PARAMS_DESCRIPTION` dictionary in `params.py` by a short description
+	- If you don't want capitalization, update `CAPITALIZE_FILTER` list in `params.py` (*Optional*)
 5. Update `References` section in `Document.ipynb` (`IEEE` format)
 6. Add description to `Class Statistics` section in `Document.ipynb`
 	- Cite reference
 	- Update table of contents
 	- Use `LaTeX` for formula
-7. Update `PARAMS_LINK` dictionary in `pycm_param.py` by document tag (without `#`)
+7. Update `PARAMS_LINK` dictionary in `params.py` by document tag (without `#`)
 8. Add tests to `overall_test.py` and `function_test.py` in `TEST` folder
 	- If you have any verified test add them to `verified_test.py`
 9.  Run `autopep8.bat`/`autopep8.sh` (*Optional*, need to install latest version of `autopep8` package)
@@ -46,9 +48,9 @@ Please consider the following :
 
 ## Overall statistic 
 
-1. Add new functions to `pycm_overall_func.py`
-2. Update `OVERALL_PARAMS` dictionary in `pycm_param.py`
-3. Update `overall_statistics` function in `pycm_class_func.py`
+1. Add new functions to `overall_funcs.py`
+2. Update `OVERALL_PARAMS` dictionary in `params.py`
+3. Update `overall_statistics` function in `class_funcs.py`
 	- Call statistic function and store result in a variable
 	- Add this variable to output
 4. Update `References` section in `Document.ipynb` (`IEEE` format)
@@ -56,7 +58,7 @@ Please consider the following :
 	- Cite reference
 	- Update table of contents
 	- Use `LaTeX` for formula
-6. Update `PARAMS_LINK` dictionary in `pycm_param.py` by document tag (without `#`)
+6. Update `PARAMS_LINK` dictionary in `params.py` by document tag (without `#`)
 7. Add tests to `overall_test.py` and `function_test.py` in `TEST` folder
 	- If you have any verified test add them to `verified_test.py`
 8. Run `autopep8.bat`/`autopep8.sh` (*Optional*, need to install latest version of `autopep8` package)
@@ -64,18 +66,18 @@ Please consider the following :
 
 ## Interpretation
 
-1. Add new interpretation table as a function to `pycm_interpret.py`
-2. Add a score dictionary to `pycm_param.py`
+1. Add new interpretation table as a function to `interpret.py`
+2. Add a score dictionary to `params.py`
 	- Example : ```PLRI_SCORE = {"Good": 4, "Fair": 3, "Poor": 2, "Negligible": 1, "None": "None"}```
-3. Add a color dictionary to `BENCHMARK_COLOR` in `pycm_param.py`
+3. Add a color dictionary to `BENCHMARK_COLOR` in `params.py`
 	- Example : 
 		```"PLRI": {"Negligible": "Red","Poor": "Orange","Fair": "Yellow","Good": "Green","None": "White"}```
 4. If interpretation table is for a class statistic:
 	- Step 2-7 [class statistic](#class-statistic)
-	- Update `CLASS_BENCHMARK_SCORE_DICT` in `pycm_param.py`
+	- Update `CLASS_BENCHMARK_SCORE_DICT` in `params.py`
 5. If interpretation table is for a overall statistic:
 	- Step 2-6 [overall statistic](#overall-statistic)
-	- Update `OVERALL_BENCHMARK_SCORE_DICT` in `pycm_param.py`
+	- Update `OVERALL_BENCHMARK_SCORE_DICT` in `params.py`
 6. Add tests to `compare_test.py`, `overall_test.py` and `function_test.py` in `TEST` folder
 	- If you have any verified test add them to `verified_test.py`
 7. Run `autopep8.bat`/`autopep8.sh` (*Optional*, need to install latest version of `autopep8` package)
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 29a6afa6..27a45a0d 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -68,4 +68,4 @@ jobs:
       if: matrix.python-version == env.TEST_PYTHON_VERSION && matrix.os == env.TEST_OS
     - name: cProfile
       run: |
-          python -m cProfile -s cumtime pycm/pycm_profile.py
+          python -m cProfile -s cumtime pycm/profile.py
diff --git a/CHANGELOG.md b/CHANGELOG.md
index dd27bb8d..1fd0ed08 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -28,6 +28,22 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
 - Test system modified
 - `Python 3.12` added to `test.yml`
 - Warning and error messages updated
+- `pycm_util.py` renamed to `utils.py`
+- `pycm_test.py` renamed to `basic_test.py`
+- `pycm_profile.py` renamed to `profile.py`
+- `pycm_param.py` renamed to `params.py`
+- `pycm_overall_func.py` renamed to `overall_funcs.py`
+- `pycm_output.py` renamed to `output.py`
+- `pycm_obj.py` renamed to `cm.py`
+- `pycm_multilabel_cm.py` renamed to `multilabel_cm.py`
+- `pycm_interpret.py` renamed to `interpret.py`
+- `pycm_handler.py` renamed to `handlers.py`
+- `pycm_error.py` renamed to `errors.py`
+- `pycm_distance.py` renamed to `distance.py`
+- `pycm_curve.py` renamed to `curve.py`
+- `pycm_compare.py` renamed to `compare.py`
+- `pycm_class_func.py` renamed to `class_funcs.py`
+- `pycm_ci.py` renamed to `ci.py`
 ## [4.0] - 2023-06-07
 ### Added
 - `pycmMultiLabelError` class
diff --git a/Otherfiles/RELEASE.md b/Otherfiles/RELEASE.md
index f94d34de..f919c337 100644
--- a/Otherfiles/RELEASE.md
+++ b/Otherfiles/RELEASE.md
@@ -9,7 +9,7 @@
 	2. `README.md`
 	3. `Otherfiles/version_check.py`
 	4. `Otherfiles/meta.yaml`
-	5. `pycm/pycm_param.py`
+	5. `pycm/params.py`
 	6. `Document/Document.ipynb`
 3. Update `CHANGELOG.md`
 	1. Add a new header under `Unreleased` section (Example: `## [0.1] - 2022-08-17`)
diff --git a/Otherfiles/version_check.py b/Otherfiles/version_check.py
index c87ded80..33072794 100644
--- a/Otherfiles/version_check.py
+++ b/Otherfiles/version_check.py
@@ -36,7 +36,7 @@
     os.path.join("Document", "Example1_files", "cm2.html"): HTML_ITEMS,
     os.path.join("Document", "Example1_files", "cm3.html"): HTML_ITEMS,
     os.path.join("Otherfiles", "test.html"): HTML_ITEMS,
-    os.path.join("pycm", "pycm_param.py"): PARAMS_ITEMS,
+    os.path.join("pycm", "params.py"): PARAMS_ITEMS,
     os.path.join(".github", "ISSUE_TEMPLATE", "bug_report.yml"): ISSUE_TEMPLATE_ITEMS,
 }
 
diff --git a/Test/error_test.py b/Test/error_test.py
index 7afd2db9..9cf46483 100644
--- a/Test/error_test.py
+++ b/Test/error_test.py
@@ -8,310 +8,310 @@
 >>> cm_2 = ConfusionMatrix(y_actu, 2)
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmVectorError: Input vectors must be provided as a list or a NumPy array.
+pycm.errors.pycmVectorError: Input vectors must be provided as a list or a NumPy array.
 >>> cm_3 = ConfusionMatrix(y_actu, [1, 2])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmVectorError: Input vectors must have the same length.
+pycm.errors.pycmVectorError: Input vectors must have the same length.
 >>> cm_4 = ConfusionMatrix([], [])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmVectorError: Input vectors must not be empty.
+pycm.errors.pycmVectorError: Input vectors must not be empty.
 >>> cm_5 = ConfusionMatrix([1, 1, 1, ], [1, 1, 1, 1])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmVectorError: Input vectors must have the same length.
+pycm.errors.pycmVectorError: Input vectors must have the same length.
 >>> cm_6 = ConfusionMatrix(matrix={0: {0: 2, 1: 50, 2: 6}, 1: {0: 5, 1: 50, 2: 3}, 2: {0: 1, 1: 7, 2: 50}})
 >>> cm_6.position()
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmVectorError: This option is only available in vector mode.
+pycm.errors.pycmVectorError: This option is only available in vector mode.
 >>> cm = ConfusionMatrix([1, 2, 3, 4], [1, 2, 3, 4], classes=[1, 2, 2, 2])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmVectorError: `classes` must contain unique labels with no duplicates.
+pycm.errors.pycmVectorError: `classes` must contain unique labels with no duplicates.
 >>> cm3=ConfusionMatrix(matrix={})
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: Invalid input confusion matrix format.
+pycm.errors.pycmMatrixError: Invalid input confusion matrix format.
 >>> cm_4=ConfusionMatrix(matrix={1: {1: 2, "1": 2}, "1": {1: 2, "1": 3}})
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: All input matrix classes must be of the same type.
+pycm.errors.pycmMatrixError: All input matrix classes must be of the same type.
 >>> cm_5=ConfusionMatrix(matrix={1: {1: 2}})
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: The number of classes must be at least 2.
+pycm.errors.pycmMatrixError: The number of classes must be at least 2.
 >>> cm = ConfusionMatrix([1, 2, 3, 4], [1, 2, 3, 4], classes=[1])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: The number of classes must be at least 2.
+pycm.errors.pycmMatrixError: The number of classes must be at least 2.
 >>> cm = ConfusionMatrix([1, 1, 1, 1], [1, 2, 1, 1], classes=[])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: The number of classes must be at least 2.
+pycm.errors.pycmMatrixError: The number of classes must be at least 2.
 >>> y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
 >>> y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]
 >>> cm = ConfusionMatrix(y_actu, y_pred)
 >>> cm.distance(metric = 2)
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: `metric` type must be DistanceType.
+pycm.errors.pycmMatrixError: `metric` type must be DistanceType.
 >>> cm.relabel([1, 2, 3])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: Invalid mapping format.
+pycm.errors.pycmMatrixError: Invalid mapping format.
 >>> cm.relabel({1: "L1", 2: "L2"})
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: Invalid mapping class names.
+pycm.errors.pycmMatrixError: Invalid mapping class names.
 >>> cm.relabel({0: "L1", 1: "L2", 2: "L2"})
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: Invalid mapping class names.
+pycm.errors.pycmMatrixError: Invalid mapping class names.
 >>> cp = Compare([cm, cm])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCompareError: Input must be provided as a dictionary.
+pycm.errors.pycmCompareError: Input must be provided as a dictionary.
 >>> cp = Compare({"cm1": cm})
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCompareError: At least 2 confusion matrices are required for comparison.
+pycm.errors.pycmCompareError: At least 2 confusion matrices are required for comparison.
 >>> cp = Compare({"cm1": cm, "cm2": []})
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCompareError: Input must be a dictionary containing pycm.ConfusionMatrix objects.
+pycm.errors.pycmCompareError: Input must be a dictionary containing pycm.ConfusionMatrix objects.
 >>> cm2 = ConfusionMatrix(matrix={"Class1": {"Class1": 9, "Class2": 3, "Class3": 0}, "Class2": {"Class1": 3, "Class2": 5, "Class3": 1}, "Class3": {"Class1": 1, "Class2": 1, "Class3": 4}})
 >>> cp = Compare({"cm1": cm, "cm2": cm2})
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCompareError: All ConfusionMatrix objects must have the same domain (same sample size and number of classes).
+pycm.errors.pycmCompareError: All ConfusionMatrix objects must have the same domain (same sample size and number of classes).
 >>> cm = ConfusionMatrix(matrix={1: {1: 9, 2: 3}, 2: {1: 3, 2: 5}}, classes=[1, 2, 3])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: The specified classes are not a subset of the matrix's classes.
+pycm.errors.pycmMatrixError: The specified classes are not a subset of the matrix's classes.
 >>> y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 1]
 >>> cm3 = ConfusionMatrix(y_actu, y_pred)
 >>> cp = Compare({"cm1": cm, "cm2": cm3}, by_class=True, class_weight={1: 1, 2: 1})
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCompareError: `class_weight` must be a dictionary and specified for all classes.
+pycm.errors.pycmCompareError: `class_weight` must be a dictionary and specified for all classes.
 >>> cp = Compare({"cm1": cm, "cm2": cm3}, by_class=True, class_weight=[])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCompareError: `class_weight` must be a dictionary and specified for all classes.
+pycm.errors.pycmCompareError: `class_weight` must be a dictionary and specified for all classes.
 >>> cp = Compare({"cm1": cm, "cm2": cm3}, by_class=True, class_benchmark_weight=[])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCompareError: `class_benchmark_weight` must be a dictionary and specified for all class benchmarks.
+pycm.errors.pycmCompareError: `class_benchmark_weight` must be a dictionary and specified for all class benchmarks.
 >>> cp = Compare({"cm1": cm, "cm2": cm3}, by_class=True, overall_benchmark_weight=[])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCompareError: `overall_benchmark_weight` must be a dictionary and specified for all overall benchmarks.
+pycm.errors.pycmCompareError: `overall_benchmark_weight` must be a dictionary and specified for all overall benchmarks.
 >>> cm1 = ConfusionMatrix([1, 1, 1, 0], [1, 0, 1, 1], metrics_off=True)
 >>> cm2 = ConfusionMatrix([1, 1, 1, 0], [1, 0, 1, 1], metrics_off=False)
 >>> cp = Compare({"cm1":cm1, "cm2":cm2})
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCompareError: Comparison cannot be performed when `metrics_off=True` in any matrix.
+pycm.errors.pycmCompareError: Comparison cannot be performed when `metrics_off=True` in any matrix.
 >>> cm.CI("MCC")
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCIError: Confidence interval calculation for this parameter is not supported in this version of pycm.
+pycm.errors.pycmCIError: Confidence interval calculation for this parameter is not supported in this version of pycm.
      Supported parameters are: TPR, TNR, PPV, NPV, ACC, PLR, NLR, FPR, FNR, AUC, PRE, Kappa, Overall ACC
 >>> cm.CI(2)
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCIError: Input must be provided as a string.
+pycm.errors.pycmCIError: Input must be provided as a string.
 >>> cm.average("AUCC")
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmAverageError: Invalid parameter!
+pycm.errors.pycmAverageError: Invalid parameter!
 >>> cm.weighted_average("AUCC")
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmAverageError: Invalid parameter!
+pycm.errors.pycmAverageError: Invalid parameter!
 >>> cm.weighted_average("AUC", weight=2)
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmAverageError: `weight` must be a dictionary and specified for all classes.
+pycm.errors.pycmAverageError: `weight` must be a dictionary and specified for all classes.
 >>> cm.weighted_average("AUC", weight={1: 23})
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmAverageError: `weight` must be a dictionary and specified for all classes.
+pycm.errors.pycmAverageError: `weight` must be a dictionary and specified for all classes.
 >>> cm.combine(1)
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: Input must be an instance of pycm.ConfusionMatrix.
+pycm.errors.pycmMatrixError: Input must be an instance of pycm.ConfusionMatrix.
 >>> cm = ConfusionMatrix([1, 0, 2, 0], [1, 1, 2, 1])
 >>> cm.brier_score()
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmVectorError: This option is only available in binary probability mode.
+pycm.errors.pycmVectorError: This option is only available in binary probability mode.
 >>> cm.log_loss()
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmVectorError: This option is only available in binary probability mode.
+pycm.errors.pycmVectorError: This option is only available in binary probability mode.
 >>> cm = ConfusionMatrix(["ham", "spam", "ham", "ham"], [0.1, 0.4, 0.25, 1], threshold=lambda x : "ham")
 >>> cm.brier_score()
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmVectorError: Actual vector contains strings; `pos_class` must be explicitly specified.
+pycm.errors.pycmVectorError: Actual vector contains strings; `pos_class` must be explicitly specified.
 >>> cm.log_loss()
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmVectorError: Actual vector contains strings; `pos_class` must be explicitly specified.
+pycm.errors.pycmVectorError: Actual vector contains strings; `pos_class` must be explicitly specified.
 >>> matrix = [[1, 2, 3], [4, 6, 1], [1, 2, 3]]
 >>> cm = ConfusionMatrix(matrix=matrix, classes=["L1", "L1", "L3", "L2"])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: `classes` must contain unique labels with no duplicates.
+pycm.errors.pycmMatrixError: `classes` must contain unique labels with no duplicates.
 >>> cm = ConfusionMatrix(matrix=matrix, classes=[1, 2])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: The length of the classes does not match the length of the array.
+pycm.errors.pycmMatrixError: The length of the classes does not match the length of the array.
 >>> crv = Curve([1, 2, 2, 1], {1, 2, 2, 1}, classes=[1, 2])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCurveError: Input vectors must be provided as a list or a NumPy array.
+pycm.errors.pycmCurveError: Input vectors must be provided as a list or a NumPy array.
 >>> crv = Curve({1, 2, 2, 1}, [1, 2, 2, 1], classes=[1, 2])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCurveError: Input vectors must be provided as a list or a NumPy array.
+pycm.errors.pycmCurveError: Input vectors must be provided as a list or a NumPy array.
 >>> crv = Curve([1, 2, 2, 1], [[0.1, 0.9]], classes=[1, 2])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCurveError: Input vectors must have the same length.
+pycm.errors.pycmCurveError: Input vectors must have the same length.
 >>> crv = Curve([1, 2, 2, 1], [[0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.2, 0.9]], classes=[1, 2])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCurveError: The sum of the probability values must equal 1.
+pycm.errors.pycmCurveError: The sum of the probability values must equal 1.
 >>> crv = Curve([1, 2, 2, 1], [[0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.1, 0.9]], classes={1, 2})
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCurveError: `classes` must be provided as a list.
+pycm.errors.pycmCurveError: `classes` must be provided as a list.
 >>> crv = Curve([1, 2, 2, 1], [[0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.1, 0.9]], classes=[1, 2, 3])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCurveError: `classes` does not match the actual vector.
+pycm.errors.pycmCurveError: `classes` does not match the actual vector.
 >>> crv = Curve([1, 1, 1, 1], [[0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.1, 0.9]], classes=[1])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCurveError: The number of classes must be at least 2.
+pycm.errors.pycmCurveError: The number of classes must be at least 2.
 >>> crv = Curve([1, 2, 2, 1], [[0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.2, "salam"]], classes=[1, 2])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCurveError: Probability vector elements must be numeric.
+pycm.errors.pycmCurveError: Probability vector elements must be numeric.
 >>> crv = Curve([1, 2, 2, 1], [[0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.2, 0.8]], classes=[1, 2], thresholds={1, 2})
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCurveError: `thresholds` must be provided as a list or a NumPy array.
+pycm.errors.pycmCurveError: `thresholds` must be provided as a list or a NumPy array.
 >>> crv = Curve([1, 2, 2, 1], [[0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.2, 0.8]], classes=[1, 2], thresholds=[0.1])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCurveError: The number of thresholds must be at least 2.
+pycm.errors.pycmCurveError: The number of thresholds must be at least 2.
 >>> crv = Curve([1, 2, 2, 1], [[0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.2, 0.8]], classes=[1, 2], thresholds=[0.1, "q"])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCurveError: `thresholds` must contain only numeric values.
+pycm.errors.pycmCurveError: `thresholds` must contain only numeric values.
 >>> crv = Curve([1, 2, 2, 1], [[0.1, 0.9], [0.1, 0.9], [0.1, 0.9], [0.2, 0.8]], classes=[1, 1, 2])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCurveError: `classes` must contain unique labels with no duplicates.
+pycm.errors.pycmCurveError: `classes` must contain unique labels with no duplicates.
 >>> crv = Curve([1, 2, 2, 1], [[0.1, 0.9], [0.1, 0.9], [0.1, 0.8, 0.1], [0.2, 0.8]], classes=[1, 2])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCurveError: All elements of the probability vector must have the same length and match the number of classes.
+pycm.errors.pycmCurveError: All elements of the probability vector must have the same length and match the number of classes.
 >>> crv = Curve([1, 2, 2, 1], [[1], [1], [1], [1]], classes=[1, 2])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCurveError: All elements of the probability vector must have the same length and match the number of classes.
+pycm.errors.pycmCurveError: All elements of the probability vector must have the same length and match the number of classes.
 >>> crv = Curve(actual_vector = np.array([1, 1, 2, 2]), probs = np.array([[0.1, 0.9], [0.4, 0.6], [0.35, 0.65], [0.8, 0.2]]), classes=[2, 1])
 >>> crv.area(method="trpz")
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmCurveError: The integral method must be either 'trapezoidal' or 'midpoint'.
+pycm.errors.pycmCurveError: The integral method must be either 'trapezoidal' or 'midpoint'.
 >>> cm = ConfusionMatrix(y_actu, y_pred, metrics_off=True)
 >>> cm.stat()
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
+pycm.errors.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
 >>> cm.sensitivity_index()
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
+pycm.errors.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
 >>> cm.IBA_alpha(0.2)
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
+pycm.errors.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
 >>> cm.NB()
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
+pycm.errors.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
 >>> cm.CI("Kappa")
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
+pycm.errors.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
 >>> cm.average("ACC")
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
+pycm.errors.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
 >>> cm.weighted_average("ACC")
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
+pycm.errors.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
 >>> cm.weighted_kappa()
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
+pycm.errors.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
 >>> cm.weighted_alpha()
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
+pycm.errors.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
 >>> cm.aickin_alpha()
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
+pycm.errors.pycmMatrixError: This method cannot be executed when `metrics_off=True`.
 >>> mlcm = MultiLabelCM([[0, 1], [1, 1]], [[1, 0], [1, 0]])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmVectorError: Failed to extract classes from input. Input vectors should be a list of sets with unified types.
+pycm.errors.pycmVectorError: Failed to extract classes from input. Input vectors should be a list of sets with unified types.
 >>> mlcm = MultiLabelCM([{'dog'}, {'cat', 'dog'}], ['cat', {'cat'}])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmVectorError: Failed to extract classes from input. Input vectors should be a list of sets with unified types.
+pycm.errors.pycmVectorError: Failed to extract classes from input. Input vectors should be a list of sets with unified types.
 >>> mlcm = MultiLabelCM(['dog', {'cat', 'dog'}], [{'cat'}, {'cat'}])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmVectorError: Failed to extract classes from input. Input vectors should be a list of sets with unified types.
+pycm.errors.pycmVectorError: Failed to extract classes from input. Input vectors should be a list of sets with unified types.
 >>> mlcm = MultiLabelCM([{'dog'}, {'cat', 'dog'}], [{'cat'}, {'cat'}])
 >>> mlcm.get_cm_by_class(1)
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMultiLabelError: The specified class name is not among the confusion matrix's classes.
+pycm.errors.pycmMultiLabelError: The specified class name is not among the confusion matrix's classes.
 >>> mlcm.get_cm_by_sample(2)
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMultiLabelError: Index is out of range for the given vector.
+pycm.errors.pycmMultiLabelError: Index is out of range for the given vector.
 >>> mlcm = MultiLabelCM([{'dog'}, {'cat', 'dog'}], [{'cat'}, {'cat', 'bird'}], classes=['dog', 'cat'])
 >>> mlcm.get_cm_by_class('bird')
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmMultiLabelError: The specified class name is not among the confusion matrix's classes.
+pycm.errors.pycmMultiLabelError: The specified class name is not among the confusion matrix's classes.
 >>> mlcm = MultiLabelCM(2, [[1, 0], [1, 0]])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmVectorError: Input vectors must be provided as a list or a NumPy array.
+pycm.errors.pycmVectorError: Input vectors must be provided as a list or a NumPy array.
 >>> mlcm = MultiLabelCM([{1, 0}, {1, 0}, {1,1}], [{1, 0}, {1, 0}])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmVectorError: Input vectors must have the same length.
+pycm.errors.pycmVectorError: Input vectors must have the same length.
 >>> mlcm = MultiLabelCM([], [])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmVectorError: Input vectors must not be empty.
+pycm.errors.pycmVectorError: Input vectors must not be empty.
 >>> mlcm = MultiLabelCM([{1, 0}, {1, 0}], [{1, 0}, {1, 0}], classes=[1,0,1])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmVectorError: `classes` must contain unique labels with no duplicates.
+pycm.errors.pycmVectorError: `classes` must contain unique labels with no duplicates.
 """
diff --git a/Test/function_test.py b/Test/function_test.py
index 627bf2be..f0d6574f 100644
--- a/Test/function_test.py
+++ b/Test/function_test.py
@@ -2,7 +2,7 @@
 """
 >>> from math import isclose
 >>> from pycm import *
->>> from pycm.pycm_distance import DISTANCE_MAPPER
+>>> from pycm.distance import DISTANCE_MAPPER
 >>> import os
 >>> import json
 >>> import numpy as np
@@ -186,12 +186,12 @@
 ...
 >>> online_help(4)
 ...
->>> from pycm.pycm_output import *
->>> from pycm.pycm_overall_func import *
->>> from pycm.pycm_class_func import *
->>> from pycm.pycm_ci import *
->>> from pycm.pycm_interpret import *
->>> from pycm.pycm_util import *
+>>> from pycm.output import *
+>>> from pycm.overall_funcs import *
+>>> from pycm.class_funcs import *
+>>> from pycm.ci import *
+>>> from pycm.interpret import *
+>>> from pycm.utils import *
 >>> color_check("red")
 [255, 0, 0]
 >>> color_check((255,2,2))
diff --git a/Test/overall_test.py b/Test/overall_test.py
index 0908733d..c6ba18e1 100644
--- a/Test/overall_test.py
+++ b/Test/overall_test.py
@@ -1272,7 +1272,7 @@
 True
 >>> cm.imbalance
 True
->>> from pycm.pycm_param import IMBALANCED_RECOMMEND, MULTICLASS_RECOMMEND, BINARY_RECOMMEND
+>>> from pycm.params import IMBALANCED_RECOMMEND, MULTICLASS_RECOMMEND, BINARY_RECOMMEND
 >>> set(cm.recommended_list) == set(IMBALANCED_RECOMMEND)
 True
 >>> cm = ConfusionMatrix(matrix={1: {1: 60, 2: 9, 3: 1, 4: 0, 5: 0, 6: 0}, 2: {1: 23, 2: 48, 3: 0, 4: 2, 5: 2, 6: 1}, 3: {1: 11, 2: 5, 3: 1, 4: 0, 5: 0, 6: 0}, 4: {1: 0, 2: 2, 3: 0, 4: 7, 5: 1, 6: 3}, 5: {1: 2, 2: 1, 3: 0, 4: 0, 5: 4, 6: 2}, 6: {1: 1, 2: 2, 3: 0, 4: 2, 5: 1, 6: 23}}) # Verified Case
diff --git a/Test/plot_error_test.py b/Test/plot_error_test.py
index a3c66f95..b5f51822 100644
--- a/Test/plot_error_test.py
+++ b/Test/plot_error_test.py
@@ -18,14 +18,14 @@
 >>> ax = cm.plot()
 Traceback (most recent call last):
     ...
-pycm.pycm_error.pycmPlotError: Failed to import matplotlib module. Please install it using: `pip install matplotlib`.
+pycm.errors.pycmPlotError: Failed to import matplotlib module. Please install it using: `pip install matplotlib`.
 >>> ax = cm.plot(plot_lib='seaborn')
 Traceback (most recent call last):
     ...
-pycm.pycm_error.pycmPlotError: Failed to import matplotlib module. Please install it using: `pip install matplotlib`.
+pycm.errors.pycmPlotError: Failed to import matplotlib module. Please install it using: `pip install matplotlib`.
 >>> crv = Curve(actual_vector = np.array([1, 1, 2, 2]), probs = np.array([[0.1, 0.9], [0.4, 0.6], [0.35, 0.65], [0.8, 0.2]]), classes=[2, 1])
 >>> ax = crv.plot(classes=[1])
 Traceback (most recent call last):
     ...
-pycm.pycm_error.pycmPlotError: Failed to import matplotlib module. Please install it using: `pip install matplotlib`.
+pycm.errors.pycmPlotError: Failed to import matplotlib module. Please install it using: `pip install matplotlib`.
 """
diff --git a/Test/plot_test.py b/Test/plot_test.py
index 4957482b..284c89a6 100644
--- a/Test/plot_test.py
+++ b/Test/plot_test.py
@@ -207,11 +207,11 @@
 >>> crv.plot(colors=['c'])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmPlotError: The number of colors does not match the number of classes.
+pycm.errors.pycmPlotError: The number of colors does not match the number of classes.
 >>> crv.plot(markers=['*'])
 Traceback (most recent call last):
         ...
-pycm.pycm_error.pycmPlotError: The number of markers does not match the number of classes.
+pycm.errors.pycmPlotError: The number of markers does not match the number of classes.
 >>> ax = crv.plot(classes=[1])
 >>> ax.get_figure()._suptitle.get_text()
 'FPR per TPR'
diff --git a/Test/verified_test.py b/Test/verified_test.py
index 990660d0..c04e4c75 100644
--- a/Test/verified_test.py
+++ b/Test/verified_test.py
@@ -3,8 +3,8 @@
 >>> import numpy as np
 >>> from math import isclose
 >>> from pycm import *
->>> from pycm.pycm_overall_func import NIR_calc
->>> from pycm.pycm_ci import AUC_SE_calc, CI_calc
+>>> from pycm.overall_funcs import NIR_calc
+>>> from pycm.ci import AUC_SE_calc, CI_calc
 >>> ABS_TOL = 1e-12
 >>> REL_TOL = 0
 >>> assert isclose(NIR_calc({'Class2': 804, 'Class1': 196}, 1000), 0.804, abs_tol=ABS_TOL, rel_tol=REL_TOL) # Verified Case - (Caret package)
diff --git a/Test/warning_test.py b/Test/warning_test.py
index 619bd164..3f38f041 100644
--- a/Test/warning_test.py
+++ b/Test/warning_test.py
@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 """
 >>> from pycm import *
->>> from pycm.pycm_util import deprecated
+>>> from pycm.utils import deprecated
 >>> from pytest import warns
 >>> large_cm = ConfusionMatrix(list(range(10)) + [2, 3, 5], list(range(10)) + [1, 7, 2])
 >>> with warns(RuntimeWarning, match='Confusion matrix is high-dimensional and may not display properly. Consider using the `sparse` flag in printing functions, or save it as a CSV file for better visualization.'):
diff --git a/pycm/__init__.py b/pycm/__init__.py
index 71e855d0..4d1bcadb 100644
--- a/pycm/__init__.py
+++ b/pycm/__init__.py
@@ -1,11 +1,11 @@
 # -*- coding: utf-8 -*-
 """PyCM modules."""
-from .pycm_param import PYCM_VERSION, OVERALL_BENCHMARK_LIST, CLASS_BENCHMARK_LIST
-from .pycm_error import *
-from .pycm_output import pycm_help, online_help
-from .pycm_distance import DistanceType
-from .pycm_obj import ConfusionMatrix
-from .pycm_compare import Compare
-from .pycm_multilabel_cm import MultiLabelCM
-from .pycm_curve import Curve, ROCCurve, PRCurve
+from .params import PYCM_VERSION, OVERALL_BENCHMARK_LIST, CLASS_BENCHMARK_LIST
+from .errors import *
+from .output import pycm_help, online_help
+from .distance import DistanceType
+from .cm import ConfusionMatrix
+from .compare import Compare
+from .multilabel_cm import MultiLabelCM
+from .curve import Curve, ROCCurve, PRCurve
 __version__ = PYCM_VERSION
diff --git a/pycm/__main__.py b/pycm/__main__.py
index 51b32ae9..1ab1f492 100644
--- a/pycm/__main__.py
+++ b/pycm/__main__.py
@@ -3,8 +3,8 @@
 
 import doctest
 import sys
-from .pycm_obj import *
-from .pycm_output import *
+from .cm import *
+from .output import *
 from art import tprint
 
 
@@ -13,7 +13,7 @@
     if len(args) > 1:
         if args[1].upper() == "TEST":
             error_flag = doctest.testfile(
-                "pycm_test.py",
+                "basic_test.py",
                 optionflags=doctest.NORMALIZE_WHITESPACE | doctest.ELLIPSIS
                 | doctest.IGNORE_EXCEPTION_DETAIL,
                 verbose=False)[0]
diff --git a/pycm/pycm_test.py b/pycm/basic_test.py
similarity index 100%
rename from pycm/pycm_test.py
rename to pycm/basic_test.py
diff --git a/pycm/pycm_ci.py b/pycm/ci.py
similarity index 100%
rename from pycm/pycm_ci.py
rename to pycm/ci.py
diff --git a/pycm/pycm_class_func.py b/pycm/class_funcs.py
similarity index 99%
rename from pycm/pycm_class_func.py
rename to pycm/class_funcs.py
index e1f53449..d7b5fa08 100644
--- a/pycm/pycm_class_func.py
+++ b/pycm/class_funcs.py
@@ -2,9 +2,9 @@
 """Class statistics functions."""
 from __future__ import division
 import math
-from .pycm_util import normal_quantile
-from .pycm_interpret import *
-from .pycm_param import CLASS_PARAMS
+from .utils import normal_quantile
+from .interpret import *
+from .params import CLASS_PARAMS
 
 
 def sensitivity_index_calc(TPR, FPR):
diff --git a/pycm/pycm_obj.py b/pycm/cm.py
similarity index 98%
rename from pycm/pycm_obj.py
rename to pycm/cm.py
index b2f0d1eb..a82194e4 100644
--- a/pycm/pycm_obj.py
+++ b/pycm/cm.py
@@ -1,17 +1,17 @@
 # -*- coding: utf-8 -*-
 """ConfusionMatrix module."""
 from __future__ import division
-from .pycm_error import pycmVectorError, pycmMatrixError, pycmCIError, pycmAverageError, pycmPlotError
-from .pycm_handler import __class_stat_init__, __overall_stat_init__
-from .pycm_handler import __obj_assign_handler__, __obj_file_handler__, __obj_matrix_handler__, __obj_vector_handler__, __obj_array_handler__
-from .pycm_handler import __imbalancement_handler__
-from .pycm_class_func import F_calc, IBA_calc, TI_calc, NB_calc, sensitivity_index_calc
-from .pycm_overall_func import weighted_kappa_calc, weighted_alpha_calc, alpha2_calc, brier_score_calc, log_loss_calc
-from .pycm_distance import DistanceType, DISTANCE_MAPPER
-from .pycm_output import *
-from .pycm_util import *
-from .pycm_param import *
-from .pycm_ci import __CI_overall_handler__, __CI_class_handler__
+from .errors import pycmVectorError, pycmMatrixError, pycmCIError, pycmAverageError, pycmPlotError
+from .handlers import __class_stat_init__, __overall_stat_init__
+from .handlers import __obj_assign_handler__, __obj_file_handler__, __obj_matrix_handler__, __obj_vector_handler__, __obj_array_handler__
+from .handlers import __imbalancement_handler__
+from .class_funcs import F_calc, IBA_calc, TI_calc, NB_calc, sensitivity_index_calc
+from .overall_funcs import weighted_kappa_calc, weighted_alpha_calc, alpha2_calc, brier_score_calc, log_loss_calc
+from .distance import DistanceType, DISTANCE_MAPPER
+from .output import *
+from .utils import *
+from .params import *
+from .ci import __CI_overall_handler__, __CI_class_handler__
 import os
 import json
 import numpy
diff --git a/pycm/pycm_compare.py b/pycm/compare.py
similarity index 98%
rename from pycm/pycm_compare.py
rename to pycm/compare.py
index ba9655ea..2393d57f 100644
--- a/pycm/pycm_compare.py
+++ b/pycm/compare.py
@@ -1,11 +1,11 @@
 # -*- coding: utf-8 -*-
 """Compare module."""
 from __future__ import division
-from .pycm_error import pycmCompareError
-from .pycm_output import *
-from .pycm_util import *
-from .pycm_param import *
-from .pycm_obj import ConfusionMatrix
+from .errors import pycmCompareError
+from .output import *
+from .utils import *
+from .params import *
+from .cm import ConfusionMatrix
 import os
 import numpy
 from warnings import warn
diff --git a/pycm/pycm_curve.py b/pycm/curve.py
similarity index 98%
rename from pycm/pycm_curve.py
rename to pycm/curve.py
index 90689a22..e541b554 100644
--- a/pycm/pycm_curve.py
+++ b/pycm/curve.py
@@ -1,10 +1,10 @@
 # -*- coding: utf-8 -*-
 """Curve module."""
 from __future__ import division
-from .pycm_error import pycmCurveError, pycmPlotError
-from .pycm_util import threshold_func, thresholds_calc, isfloat
-from .pycm_param import *
-from .pycm_obj import ConfusionMatrix
+from .errors import pycmCurveError, pycmPlotError
+from .utils import threshold_func, thresholds_calc, isfloat
+from .params import *
+from .cm import ConfusionMatrix
 from warnings import warn
 import numpy
 
diff --git a/pycm/pycm_distance.py b/pycm/distance.py
similarity index 100%
rename from pycm/pycm_distance.py
rename to pycm/distance.py
diff --git a/pycm/pycm_error.py b/pycm/errors.py
similarity index 100%
rename from pycm/pycm_error.py
rename to pycm/errors.py
diff --git a/pycm/pycm_handler.py b/pycm/handlers.py
similarity index 97%
rename from pycm/pycm_handler.py
rename to pycm/handlers.py
index ffbcd167..d525bb40 100644
--- a/pycm/pycm_handler.py
+++ b/pycm/handlers.py
@@ -1,11 +1,11 @@
 # -*- coding: utf-8 -*-
 """ConfusionMatrix handlers."""
 from __future__ import division
-from .pycm_class_func import class_statistics
-from .pycm_error import pycmVectorError, pycmMatrixError
-from .pycm_overall_func import overall_statistics
-from .pycm_util import *
-from .pycm_param import *
+from .class_funcs import class_statistics
+from .errors import pycmVectorError, pycmMatrixError
+from .overall_funcs import overall_statistics
+from .utils import *
+from .params import *
 import json
 import types
 import numpy
diff --git a/pycm/pycm_interpret.py b/pycm/interpret.py
similarity index 100%
rename from pycm/pycm_interpret.py
rename to pycm/interpret.py
diff --git a/pycm/pycm_multilabel_cm.py b/pycm/multilabel_cm.py
similarity index 98%
rename from pycm/pycm_multilabel_cm.py
rename to pycm/multilabel_cm.py
index 461414ea..8626d1c8 100644
--- a/pycm/pycm_multilabel_cm.py
+++ b/pycm/multilabel_cm.py
@@ -1,9 +1,9 @@
 # -*- coding: utf-8 -*-
 """MultiLabelCM module."""
 from __future__ import division
-from .pycm_error import pycmVectorError, pycmMultiLabelError
-from .pycm_param import *
-from .pycm_obj import ConfusionMatrix
+from .errors import pycmVectorError, pycmMultiLabelError
+from .params import *
+from .cm import ConfusionMatrix
 import numpy
 
 
diff --git a/pycm/pycm_output.py b/pycm/output.py
similarity index 99%
rename from pycm/pycm_output.py
rename to pycm/output.py
index 274f6e2e..ed0e0285 100644
--- a/pycm/pycm_output.py
+++ b/pycm/output.py
@@ -2,8 +2,8 @@
 """Outputs functions."""
 from __future__ import division
 from functools import partial
-from .pycm_param import *
-from .pycm_util import rounder, sort_char_num
+from .params import *
+from .utils import rounder, sort_char_num
 import webbrowser
 
 
diff --git a/pycm/pycm_overall_func.py b/pycm/overall_funcs.py
similarity index 99%
rename from pycm/pycm_overall_func.py
rename to pycm/overall_funcs.py
index 5b4724d4..618fe029 100644
--- a/pycm/pycm_overall_func.py
+++ b/pycm/overall_funcs.py
@@ -4,9 +4,9 @@
 import math
 import operator as op
 from functools import reduce
-from .pycm_interpret import *
-from .pycm_ci import kappa_SE_calc, CI_calc, SE_calc
-from .pycm_util import complement
+from .interpret import *
+from .ci import kappa_SE_calc, CI_calc, SE_calc
+from .utils import complement
 
 
 def log_loss_calc(
diff --git a/pycm/pycm_param.py b/pycm/params.py
similarity index 100%
rename from pycm/pycm_param.py
rename to pycm/params.py
diff --git a/pycm/pycm_profile.py b/pycm/profile.py
similarity index 100%
rename from pycm/pycm_profile.py
rename to pycm/profile.py
diff --git a/pycm/pycm_util.py b/pycm/utils.py
similarity index 99%
rename from pycm/pycm_util.py
rename to pycm/utils.py
index c46ae290..a54a6115 100644
--- a/pycm/pycm_util.py
+++ b/pycm/utils.py
@@ -5,8 +5,8 @@
 import math
 import numpy
 import re
-from .pycm_param import *
-from .pycm_error import pycmMatrixError
+from .params import *
+from .errors import pycmMatrixError
 from warnings import warn
 from functools import wraps