This function finds outliers in pollen time-series and replaces them with background values
     
    
    Usage
    outliers_replacer(value, date, threshold = 5, sum_percent = 100)
 
    
    Arguments
- value
- pollen concentration values 
- date
- dates 
- threshold
- a number indicating how many times outlying value needs to be larger than the background to be replaced (default is 5) 
- sum_percent
- a sum_percent parameter 
 
    
    Value
    a new data.frame object with replaced outliers
     
    
    References
    Kasprzyk, I. and A. Walanus.: 2014. Gamma, Gaussian and Logistic Distribution Models for Airborne Pollen Grains and Fungal Spore Season Dynamics, Aerobiologia 30(4), 369-83.
     
    
    Examples
    
data(pollen_count)
df <- subset(pollen_count, site=='Shire')
new_df <- outliers_replacer(df$birch, df$date)
identical(df, new_df)
#> [1] FALSE
library('purrr')
new_pollen_count <- pollen_count %>% split(., .$site) %>%
       map_df(~outliers_replacer(value=.$hazel, date=.$date, threshold=4))