A pre-loaded example dataset in R

Main page: https://www.rdocumentation.org/packages/datasets/versions/3.6.2/topics/BOD

BOD
##   Time demand
## 1    1    8.3
## 2    2   10.3
## 3    3   19.0
## 4    4   16.0
## 5    5   15.6
## 6    7   19.8
require(stats)
# simplest form of fitting a first-order model to these data
fm1 <- nls(demand ~ A*(1-exp(-exp(lrc)*Time)), data = BOD,
   start = c(A = 20, lrc = log(.35)))
coef(fm1)
##          A        lrc 
## 19.1425770 -0.6328215
fm1
## Nonlinear regression model
##   model: demand ~ A * (1 - exp(-exp(lrc) * Time))
##    data: BOD
##       A     lrc 
## 19.1426 -0.6328 
##  residual sum-of-squares: 25.99
## 
## Number of iterations to convergence: 4 
## Achieved convergence tolerance: 3.588e-07
# using the plinear algorithm
fm2 <- nls(demand ~ (1-exp(-exp(lrc)*Time)), data = BOD,
   start = c(lrc = log(.35)), algorithm = "plinear", trace = TRUE)
## 32.94622 :  -1.049822 22.126001
## 25.99248 :  -0.6257161 19.1031883
## 25.99027 :  -0.6327039 19.1419223
## 25.99027 :  -0.6328192 19.1425644
# using a self-starting model
fm3 <- nls(demand ~ SSasympOrig(Time, A, lrc), data = BOD)
summary(fm3)
## 
## Formula: demand ~ SSasympOrig(Time, A, lrc)
## 
## Parameters:
##     Estimate Std. Error t value Pr(>|t|)   
## A    19.1426     2.4959   7.670  0.00155 **
## lrc  -0.6328     0.3824  -1.655  0.17328   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.549 on 4 degrees of freedom
## 
## Number of iterations to convergence: 0 
## Achieved convergence tolerance: 6.473e-07