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Fixed the output of predFit() in situations whenever
standard errors and confidence/predictions bands are both
requested.
New data sets bladder (a repeated measures data set)
and whisky.
Fixed typos in documentation throughout the package.
Updated URLs throughout the package.
Better y-axis limits when x-axis is on log scale (e.g., using log = “x”). Fixes issue #27.
Better default y-axis label when using plotFit() on
a model with transformed response. For example, calling
plotFit(fit) where
fit <- lm(sqrt(dist) ~ speed, data = cars) will have a
default y-axis label of "sqrt(dist)".
plotFit() has been completely re-written using much
less code.
predFit() (and hence plotFit()) now
works for "nls" objects fit using the Golub-Pereyra
algorithm (i.e., algorithm = "plinear"); however,
confidence/prediction bands are still not available.
New introductory vignette.
Multiple predictor variables are allowed for "lm"
and "glm" objects.
All non-base package functions are now imported.
The generic function predFit() is now exported. This
function is used by investr to obtain predictions, and
hence, inverse predictions. For example, predFit() can be
used to obtain prediction intervals for nonlinear least-squares fits
(i.e., models of class "nls").
Improved tests and test coverage.
plotFit() gained methods for "rlm" and
"lqs" objects from package MASS.
invest() now accepts objects of class
"glm" (experimental).
Functions calibrate() and invest() now
return an object of class "invest".
Cleaned up documentation.
Added AnyNA() function for those using older
versions of R.
Cleaned up examples.
Added bootstrap option to
invest().
Updated citation file.
Minor code changes.
plotFit() should now plot models with transformed
responses correctly.
Fixed error causing invest() to fail because of a
missing data argument.
Added more tests.
invest() now accepts objects of class
"lme" (experimental).
A few minor bug fixes and code improvements.
Added more tests.
A few minor bug fixes.
Slightly better documentation.
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They may not be fully stable and should be used with caution. We make no claims about them.