docs: update fix reference (#37)

Author: DiogoRibeiro7

.gitignore

@@ -50,3 +50,4 @@ dist/
 # Temporary
 *.log
 *.tmp
+.hypothesis/

CITATION.cff

@@ -5,7 +5,7 @@ message: "If you use this software, please cite it using the metadata below."
 preferred-citation:
   type: software
   title: "gen_surv"
-  version: "1.0.3"
+  version: "1.0.8"
   url: "https://github.com/DiogoRibeiro7/genSurvPy"
   authors:
     - family-names: Ribeiro

README.md

@@ -28,6 +28,8 @@ poetry install
 - Easy integration with `pandas` and `NumPy`
 - Suitable for benchmarking survival algorithms and teaching
 - Accelerated Failure Time (Log-Normal) model generator
+- Mixture cure and piecewise exponential models
+- Competing risks generators (constant and Weibull hazards)
 - Command-line interface powered by `Typer`
 
 ## 🧪 Example
@@ -36,7 +38,7 @@ poetry install
 from gen_surv import generate
 
 # CPHM
-generate(model="cphm", n=100, model_cens="uniform", cens_par=1.0, beta=0.5, covar=2.0)
+generate(model="cphm", n=100, model_cens="uniform", cens_par=1.0, beta=0.5, covariate_range=2.0)
 
 # AFT Log-Normal
 generate(model="aft_ln", n=100, beta=[0.5, -0.3], sigma=1.0, model_cens="exponential", cens_par=3.0)
@@ -54,6 +56,18 @@ generate(model="tdcm", n=100, dist="weibull", corr=0.5,
 generate(model="thmm", n=100, qmat=[[0, 0.2, 0], [0.1, 0, 0.1], [0, 0.3, 0]],
          emission_pars={"mu": [0.0, 1.0, 2.0], "sigma": [0.5, 0.5, 0.5]},
          p0=[1.0, 0.0, 0.0], model_cens="exponential", cens_par=3.0)
+
+# Mixture Cure
+generate(model="mixture_cure", n=100, cure_fraction=0.3, seed=42)
+
+# Piecewise Exponential
+generate(
+    model="piecewise_exponential",
+    n=100,
+    breakpoints=[1.0, 3.0],
+    hazard_rates=[0.2, 0.5, 1.0],
+    seed=0
+)
 ```
 
 ## ⌨️ Command-Line Usage
@@ -75,6 +89,12 @@ python -m gen_surv dataset aft_ln --n 100 > data.csv
 | `gen_tdcm()` | Time-Dependent Covariate Model |
 | `gen_thmm()` | Time-Homogeneous Markov Model |
 | `gen_aft_log_normal()` | Accelerated Failure Time Log-Normal |
+| `gen_aft_log_logistic()` | AFT model with log-logistic baseline |
+| `gen_competing_risks()` | Competing risks with constant hazards |
+| `gen_competing_risks_weibull()` | Competing risks with Weibull hazards |
+| `gen_mixture_cure()` | Mixture cure model |
+| `cure_fraction_estimate()` | Estimate cure fraction |
+| `gen_piecewise_exponential()` | Piecewise exponential model |
 | `sample_bivariate_distribution()` | Sample correlated Weibull or exponential times |
 | `runifcens()` | Generate uniform censoring times |
 | `rexpocens()` | Generate exponential censoring times |

docs/source/conf.py

@@ -12,7 +12,7 @@
 project = 'gen_surv'
 copyright = '2025, Diogo Ribeiro'
 author = 'Diogo Ribeiro'
-release = '1.0.3'
+release = '1.0.8'
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

docs/source/index.md

@@ -36,7 +36,7 @@ theory
 from gen_surv import generate
 
 # CPHM
-generate(model="cphm", n=100, model_cens="uniform", cens_par=1.0, beta=0.5, covar=2.0)
+generate(model="cphm", n=100, model_cens="uniform", cens_par=1.0, beta=0.5, covariate_range=2.0)
 
 # AFT Log-Normal
 generate(model="aft_ln", n=100, beta=[0.5, -0.3], sigma=1.0, model_cens="exponential", cens_par=3.0)

docs/source/theory.md

@@ -116,5 +116,24 @@ This model is especially useful when the proportional hazards assumption is not
 
 All models support censoring:
 
-- **Uniform:** \( C_i \\sim U(0, \\text{cens\\_par}) \)
-- **Exponential:** \( C_i \\sim \\text{Exp}(\\text{cens\\_par}) \)
+- **Uniform:** \( C_i \sim U(0, \text{cens\_par}) \)
+- **Exponential:** \( C_i \sim \text{Exp}(\text{cens\_par}) \)
+
+## 6. Competing Risks Models
+
+These models simulate multiple mutually exclusive event types. Each cause has its
+own hazard function, and the observed status indicates which event occurred
+(1, 2, ...). The package includes constant-hazard and Weibull-hazard versions.
+
+## 7. Mixture Cure Models
+
+Mixture cure models assume a proportion of subjects are immune to the event. The
+generator mixes a logistic cure component with an exponential hazard for the
+uncured, returning a ``cured`` indicator column alongside the usual time and
+status.
+
+## 8. Piecewise Exponential Model
+
+This model divides the time axis into intervals defined by user-supplied
+breakpoints. Each interval has its own hazard rate, allowing flexible hazard
+shapes over time.

docs/source/usage.md

@@ -20,7 +20,7 @@ Generate datasets directly in Python:
 from gen_surv import generate
 
 # Cox Proportional Hazards example
-generate(model="cphm", n=100, model_cens="uniform", cens_par=1.0, beta=0.5, covar=2.0)
+generate(model="cphm", n=100, model_cens="uniform", cens_par=1.0, beta=0.5, covariate_range=2.0)
 ```
 
 You can also generate data from the command line:

examples/run_cphm.py

@@ -10,7 +10,7 @@
     model_cens="uniform",
     cens_par=1.0,
     beta=0.5,
-    covar=2.0,
+    covariate_range=2.0,
     seed=42
 )
 

fix_recommendations.md

@@ -2,105 +2,45 @@
 
 ## Priority 1: Critical Fixes
 
-### 1. Fix `__init__.py` Import Issues
-
-Update `gen_surv/__init__.py` to include missing imports:
-
-```python
-# Add these imports
-from .aft import gen_aft_log_logistic
-from .competing_risks import gen_competing_risks, gen_competing_risks_weibull
-from .mixture import gen_mixture_cure, cure_fraction_estimate
-from .piecewise import gen_piecewise_exponential
-
-# Update __all__ to include:
-__all__ = [
-    # ... existing exports ...
-    "gen_aft_log_logistic",
-    "gen_competing_risks", 
-    "gen_competing_risks_weibull",
-    "gen_mixture_cure",
-    "cure_fraction_estimate", 
-    "gen_piecewise_exponential",
-]
-```
-
-### 2. Add Missing Validators
-
-Create validation functions in `validate.py`:
-
-```python
-def validate_gen_aft_weibull_inputs(n, beta, shape, scale, model_cens, cens_par):
-    # Add validation logic
-    
-def validate_gen_aft_log_logistic_inputs(n, beta, shape, scale, model_cens, cens_par):
-    # Add validation logic
-    
-def validate_competing_risks_inputs(n, n_risks, baseline_hazards, betas, model_cens, cens_par):
-    # Add validation logic
-```
-
-### 3. Update CLI Integration
-
-Modify `cli.py` to handle all available models:
-
-```python
-# Add support for competing_risks, mixture, and piecewise models
-# Update parameter handling for each model type
-```
+- [x] **Fix `__init__.py` Import Issues**
+  - Ensure missing imports for new generators are added and exported via `__all__`.
 
-## Priority 2: Version Consistency
-
-### Update Version Numbers
+- [x] **Add Missing Validators**
+  - Create validation helpers for AFT Weibull, AFT log-logistic, and competing risks generators.
 
-1. **CITATION.cff**: Change version from "1.0.3" to "1.0.8"
-2. **docs/source/conf.py**: Change release from '1.0.3' to '1.0.8'
+- [x] **Update CLI Integration**
+  - Support competing risks, mixture cure, and piecewise exponential models.
 
-## Priority 3: Testing and Documentation
+## Priority 2: Version Consistency
 
-### Add Missing Tests
+- [x] **Update Version Numbers**
+  - `CITATION.cff` and `docs/source/conf.py` now reference version 1.0.8.
 
-Create test files:
-- `tests/test_censoring.py`
-- `tests/test_mixture.py` 
-- `tests/test_piecewise.py`
-- `tests/test_summary.py`
-- `tests/test_visualization.py`
+## Priority 3: Testing and Documentation
 
-### Update Documentation
+- [x] **Add Missing Tests**
+  - Added tests for censoring helpers, mixture cure, piecewise exponential, summary, and visualization.
 
-1. Add competing risks, mixture, and piecewise models to `theory.md`
-2. Update examples in documentation to include new models
+- [x] **Update Documentation**
+  - Documented competing risks, mixture cure, and piecewise exponential models.
 
 ## Priority 4: Code Quality Improvements
 
-### Standardize Parameter Naming
-
-- Consistently use `covariate_cols` instead of mixing `covar` and `covariate_cols`
-- Standardize return column names across all models
+- [x] **Standardize Parameter Naming**
+  - Replaced the `covar` parameter with `covariate_range` and standardized return columns to `X0`.
 
-### Add Type Hints
-
-Complete type hints for all public functions, especially in:
-- `mixture.py`
-- `piecewise.py` 
-- `summary.py`
+- [x] **Add Type Hints**
+  - Completed type hints for public functions in `mixture.py`, `piecewise.py`, and `summary.py`.
 
 ## Verification Steps
+- [x] `python -c "from gen_surv import gen_aft_log_logistic, gen_competing_risks"`
+- [x] `python -m gen_surv dataset competing_risks --n 10`
+- [x] `pytest -q`
+- [x] `python scripts/check_version_match.py`
+- [x] `sphinx-build docs/source docs/build`
 
-After implementing fixes:
-
-1. **Test imports**: `python -c "from gen_surv import gen_aft_log_logistic, gen_competing_risks"`
-2. **Test CLI**: `python -m gen_surv dataset competing_risks --n 10`
-3. **Run full test suite**: `poetry run pytest`
-4. **Check version consistency**: `python scripts/check_version_match.py`
-5. **Build docs**: `poetry run sphinx-build docs/source docs/build`
+## Status
 
-## Impact Assessment
+All fix recommendations have been implemented in version 1.0.8.
 
-These fixes will:
-- ✅ Eliminate ImportError exceptions for users
-- ✅ Make all models accessible via public API
-- ✅ Ensure CLI works for all supported models
-- ✅ Improve user experience and API consistency
-- ✅ Maintain backward compatibility
+Verified as of commit `7daa3e1`.

gen_surv/__init__.py

@@ -13,7 +13,10 @@
 from .cmm import gen_cmm
 from .tdcm import gen_tdcm
 from .thmm import gen_thmm
-from .aft import gen_aft_log_normal, gen_aft_weibull
+from .aft import gen_aft_log_normal, gen_aft_weibull, gen_aft_log_logistic
+from .competing_risks import gen_competing_risks, gen_competing_risks_weibull
+from .mixture import gen_mixture_cure, cure_fraction_estimate
+from .piecewise import gen_piecewise_exponential
 
 # Helper functions
 from .bivariate import sample_bivariate_distribution
@@ -25,8 +28,9 @@
         plot_survival_curve,
         plot_hazard_comparison,
         plot_covariate_effect,
-        describe_survival
+        describe_survival,
     )
+
     _has_visualization = True
 except ImportError:
     _has_visualization = False
@@ -40,15 +44,19 @@
     # Main interface
     "generate",
     "__version__",
-    
     # Individual generators
     "gen_cphm",
     "gen_cmm",
     "gen_tdcm",
     "gen_thmm",
     "gen_aft_log_normal",
     "gen_aft_weibull",
-    
+    "gen_aft_log_logistic",
+    "gen_competing_risks",
+    "gen_competing_risks_weibull",
+    "gen_mixture_cure",
+    "cure_fraction_estimate",
+    "gen_piecewise_exponential",
     # Helpers
     "sample_bivariate_distribution",
     "runifcens",
@@ -57,10 +65,11 @@
 
 # Add visualization tools to __all__ if available
 if _has_visualization:
-    __all__.extend([
-        "plot_survival_curve",
-        "plot_hazard_comparison",
-        "plot_covariate_effect",
-        "describe_survival"
-    ])
-    
+    __all__.extend(
+        [
+            "plot_survival_curve",
+            "plot_hazard_comparison",
+            "plot_covariate_effect",
+            "describe_survival",
+        ]
+    )