Context | Using CAPA for anomalies | Using PELT for change points | CROPS for varaible penalties

No Anomaly (Baseline) | Anomaly

No Anomaly (Baseline) | Collective Anomalies | Anomaly in Mean and Variance | Anomaly in Mean | Anomaly in Variance | Point anomaly | [C_{P_{M}}\left(y_{t}\left| m_{t},s_{t},\hat{\mu}{k}\right.\right) -C{B}\left(y_{t}\left| m_{t},s_{t}\right.\right) | [C_{P_{V}}\left(y_{t}\left| m_{t},s_{t},\hat{\sigma}{k}\right.\right) -C{B}\left(y_{t}\left| m_{t},s_{t}\right.\right) | Relating this to the background cost we see that point anomalies may be accepted in the capa search when[f\left(z_{t},\gamma,\beta\right) =C_{P_{V}}\left(y_{t}\left| m_{t},s_{t},\hat{\sigma}{k},\gamma\right.\right) -C{B}\left(y_{t}\left| m_{t},s_{t}\right.\right)

No Anomaly (Baseline) | Anomaly in Rate

The solution order of HRUs and exchange of fluxes | Input Series | Miscellaneous Coding comments | Relating the vignette notation and code variables | Numerical solutions

Conceptual model | Notation | Finite Volume Formulation | Surface zone | Root zone | Unsaturated Zone | Saturated Zone | Numerical Solution | Downward Pass | Surface excess | Root Zone | Upward Pass | Surface Zone | Surface Zone Representations | Approximating Tank Models | Approximating Diffusion Wave Routing | Compound Channels | Available Formulations | Saturated Zone representations | Appendix A - Monotonicity of $\mathcal{H}\left(z\right)$

map | output_flux | hru | states | properties | sf, rz, uz,& sz | sf_flow_direction & sz_flow_direction | initialisation | precip & pet | class

The model structure | Setting map locations | Preparing input data | Altering parameters | Creating the dynatop Object | Running dynamic TOPMODEL | Mass balance

Implementation notes | Getting started | Adding catchment data | Getting and plotting catchment information | Filling sinks | Determining Ordering | Computing properties | Adding additional layer | Classifying into Hydrological Response Units | Generating a dynamic TOPMODEL

Usage | Implementation notes

Quantile regression | Baseline: No Anomaly | Collective anomaly | Point Anomaly

Linear regression | Sufficent statistics | Baseline: No Anomaly | Collective Anomalies | Anomaly in Regression parameters | Anomaly in Variance | Anomaly in regression parameters and variance | Since[\sum_{t \in T_{k}} \left( \hat{\mathbf{y}}{t} - \mathbf{X}{t} \theta_{k}\right)^{\prime} \mathbf{S}{t}^{-1} \left( \hat{\mathbf{y}}{t} - \mathbf{X}{t} \theta{k}\right) | Point anomaly

Univariate Gaussian | Anomaly in mean and varinace | Anomaly in Mean | Anomaly in Variance | No Anomaly (Baseline) | Point anomaly

Framework | CROPS | Point anomalies

Generating synthetic data | Estimation | Results