Overview | Table of Contents | General ideas | Fundamentals of calling AdhereR from Python 3 | The Python 3 wrapper: the adherer module | Making the adherer module visible to Python (aka installation) | Importing the adherer module and initializations | The class hierarchy | The CallAdhereRError exception class | The base class CMA0 | Class CMA1 and its daughter classes CMA2, CMA3 and CMA4 | Class CMA5 and its daughter classes CMA6, CMA7, CMA8 and CMA9 | Classes CMAPerEpisode and CMASlidingWindow | Examples of use | Basic usage | Importing adherer and checking autodetection | Export the test dataset from R and import it in Python | Compute and plot test CMA | Interactive plotting | From a Jupyter Notebook | Parallel processing (locally and on different machines) | Single thread on the local machine | R | Python 3 | Multi-threaded on the local machine | Parallel on remote machines over a network | Some caveats for over-the-network distributed computation | Appendix I: the communication protocol | Context | Protocol | PARAMETERS[^1] | COMMENTS | SPECIAL PARAMETERS[^2] | FUNCTIONS | PLOTTING | CMA1, CMA2, CMA3, CMA4 | CMA5, CMA6, CMA7, CMA8, CMA9 | CMA_per_episode | CMA_sliding_window | compute_event_int_gaps | compute_treatment_episodes | plot_interactive_cma | Appendix II: the Python 3 code | Notes

Definitions | Data preparation and example dataset | Visualization of patient records | Persistence -- treatment episodes | Adherence -- continuous multiple interval measures of medication availability/gaps (CMA) | The simple CMAs | CMA1 | CMA2 | CMA3 and CMA4 | CMA5 and CMA6 | CMA7 | CMA8 | CMA9 | The iterated CMAs | CMA per episode | Sliding-window CMA | Mapping events to episodes and sliding windows | Interactive plotting | Computing event duration from prescription, dispensing, and hospitalization data | Computing time to initiation | Defining medication groups | Working with very large databases | Using AdhereR from other programming languages and platforms | AdhereR is pipe (%>% or |>)-friendly | Modifying AdhereR plots a posteriori | Plot CMA7 for patients 5 and 8: | Access the plotting info: | Let's add a vertical line for patient 8 between the medication change: | Find the event where the medication changes: | Half-way between the events where medication changes: | Draw the line: | Put a star * next to the 4th event of patient 5: | Find the event: | Plot the star: | Add some random text over the figure:

Introduction | Overview | Launching the app | The App's User Interface (UI) | About the App | Cleanly exiting the App | The plotting area (and the messages) | Saving the current plot to file | Viewing, copying and using the R code that would produce the current plot | Computing the CMA for several patients | Setting parameters | General settings | Follow-up window (FUW) and Observation window (OW) | Carry over | Define episodes | Define sliding windows (SW) | Align patients | Duration & period | CMA estimates | Show dose | Legend | Aesthetics | Advanced | Selecting/changing the data source | Data already in memory | Load from file | Use an SQL database | Defining and using medication groups | References | Notes

Introduction | Relational databases | How about SAS and Stata | Finally, let's look at Hadoop and MapReduce! | Conclusions | References