update requirements and README

Author: james-leinas

examples/custom-code-AD/README.md

@@ -1,5 +1,29 @@
 # Custom Code in Automation Designer
 
+## 5-parameter-logistic
+
+Example snippet fits a 5-Parameter Logistic (5PL) curve to dose-response data to extract EC50, Hill slope, and asymmetry, with AIC comparison against a standard 4PL fit and an interactive plot with residuals subplot.
+
+- **[Input File](./snippets/5-parameter-logistic/docs/input/dose_response_5pl_data.xlsx)**
+- **[Custom Code Block](./snippets/5-parameter-logistic/dose_response_5pl.py)**
+- **[Output Files](./snippets/5-parameter-logistic/docs/output)**
+
+## kinetic-timecourse
+
+Example snippet fits one-phase association kinetic curves per compound to extract parameters (Emax, kobs, t½, AUC, R²), with t-test comparison at the inflection point and a multi-compound time-course plot with 95% CI ribbons.
+
+- **[Input File](./snippets/kinetic-timecourse/docs/input/timecourse_data.xlsx)**
+- **[Custom Code Block](./snippets/kinetic-timecourse/kinetics_timecourse.py)**
+- **[Output Files](./snippets/kinetic-timecourse/docs/output)**
+
+## multi-group-anova
+
+Example snippet performs one-way ANOVA to test for significant differences across treatment groups, followed by Tukey HSD post-hoc analysis for pairwise comparisons, with descriptive statistics and an annotated bar chart output.
+
+- **[Input File](./snippets/multi-group-anova/docs/input/multigroup_efficacy_data.xlsx)**
+- **[Custom Code Block](./snippets/multi-group-anova/multigroup_anova.py)**
+- **[Output Files](./snippets/multi-group-anova/docs/output)**
+
 ## plot-chromatogram
 
 Example snippet demonstrates plotting a multi-axis HPLC chromatogram (retention volume X Absorbance (mAU), Temperature (degC), pH) with custom code.

examples/custom-code-AD/requirements.txt

@@ -1,12 +1,15 @@
 allotropy==0.1.105
 biopython==1.86
+flowkit==0.1.0
 lmfit==1.3.4
+matplotlib==3.10.3
 numpy==2.2.4
 openpyxl==3.1.5
 pandas==2.2.3
 plotly==5.22.0
 pyarrow==19.0.1
-pydantic==2.12.5
+pydantic==1.10.21
+seaborn==0.13.2
 scikit-learn==1.6.1
 scipy==1.15.2
 statsmodels==0.14.4