86 vignette update for version release v 017 of tcplfit2

[gracezhihuizhao]

This PR covers the updates needed for the vignette listed in #86

• Example 1, added deceptions for arguments errfunc and poly2.biphasic mentioning their functionalities of specifying different error distributions and bi-phasic fitting.
• Update the plots in all examples with the new ggplot plotting functions.
• Moved the previous Example 3 which shows the use of the base R plotting functionalities to an appendix, and incorporated the introduction of plotting functionality into Example 1 and 2.
• Updated the previous Example 4 now Example 3 with more descriptive text. Demonstrate the the normalizing steps and calculations of BMAD and onesd with explicit codes instead of apply tcpl's methods.

[gracezhihuizhao]

Please double check the log-scaling for the hill and gain-loss are working.

@sedavid01 Hi Sarah, I looked into it and see where the problem is. When I generate a series of X's for plotting the line I do conc_plot = seq(min(conc), max(conc), length = 100) with conc in normal scale, and take log10(conc_plot) if log is used. However the X's are evenly spread in the normal scale does not mean they are evenly spread in the log scale. In fact like example 1 where the minimum conc is 0.03 and the maximum conc is 100, the sequence turned out to have most of the points fall between 1 and 100 and only one point falls between 0 and 1, and resulted in only one point being below 0 after taking log10, thus the linear line. So the calculation is technically not wrong but the execution and the plot can definitely be improved. I will make some updates to make sure the simulated X's are evenly taken over the range on both the normal and log10 scales.

[madison-feshuk]

@brown-jason @kpaulfriedman here is a pdf of vignettes combined. Let me know if additional edits are needed. Otherwise, package should be ready for a tech review. tcplfit2_vignettes_combined_26JUN2024.pdf