#Purpose This package was developed for processing, modeling,
cross-validation, and analysis of water quality conditions (specifically
chl-a).

#Example analysis One useful analysis that can be done with this
package is evaluating trends in the timing of maximum chl-a
conditions.

```
library(RSAlgaeR)
#Load a record of water quality data
wqrecord <- readRDS('EstimatedRecord.rds')
#Use doy.max.trend function to find the location, value, and day of year when the maximum value occured
wqdoytrend <- doy.max.trend(data=wqrecord,date="ImageDate",value="Chla",location="StationID")
```

This function calculates the DOY when the maximum value occurs, where
this occured, and returns a list containing a dataframe of the annual
maxima information, summary of the model fit (DOY vs year) and a plot of
the DOY of maximum vs year.

#Installation instructions The package can be installed and loaded
using the following commands:

```
install.packages("RSAlgaeR")
library(RSAlgaeR)
```

#An overview that describes the main components of the package.

This includes formatting dates, removing negative values, cloud
pixels, etc. (See `sampleformatreflectancedata.R`

script for
example which uses `formatSRdata`

function)

#### 2. Create model variables.

Use the `create.model.vars`

function

#### 3. Parameterize model.

Use glm() to develop the final model, based on an appropriate season,
timewindow, and parameters.

(Optional) Use the `step.model`

function (stepwise
regression based on a user-specified timewindow) to explore performance
for various parameters, definitions of near coincident data and seasons.
Examine model performance using k-fold cross validation and exploring
the goodness of fit (`cv.model`

and `modresults`

functions)

#### 4. Apply model.

Apply the model to remotely sensed imagery for a user-specified
season using `apply.mod.seasonal`

#### 5. Plot estimates.

- With error bars:
`plotrecord.errors`

- With data used in calibration:
`plotrecord.cal`

- With field-sampled data:
`plotrecord`

#### 6. Explore trends.

Long term, linear changes in values/year can be explored using the
Theil-Sen Estimator, which is more robust than a simple OLS regression.
* Annually: `annualtrend.ts`

* Monthly:
`monthlytrend.ts`

#### 7. Explore
connections to local climate conditions.

- Immediate connections Overall, monthly, or by location:
`climate.factor.effect`

- Seasonal summaries of water quality and climate conditions:
`annual.summary.wq`

and
`annual.summary.climate`