added "weibull" as supported distributions (ie
distname="weibull" works for MLE)
misc print/summary cleanups
added reference to github pages for R (https://github.com/gmgeorg/LambertW) and Python (https://github.com/gmgeorg/pylambertw)
delta estimates
(https://github.com/gmgeorg/LambertW/issues/3)ks.test.t to ks_test_t to avoid
CRAN complaining about misuse of S3methodRd
files for Rmarkdown to
Suggests)WARNINGS (thanks to Kurt Hornik for
help on resolving Rcpp build errors)get_initial_tau() (happened
when mad(y) = 0 – which can happen for data with majority
of values fall on the same exact value)skewness.cpprewrote a couple of functions in C++ using the amazing (!)
Rcpp package. 3-4x speed up for W related
functions; also for IGMM and
MLE_LambertW
added bootstrap functions for users to easily check if a Lambert W x F distribution with finite mean and variance input makes sense:
bootstrap.LambertW_fitanalyze_convergenceadded use.mean.variance argument to distinguish
between mean-variance Lambert W x F distributions and general
location-scale Lambert W x F distributions. See also Goerg (2015b). See
the help file on these functions for references on why they were
included.
added more unit tests (moving code from “Examples” to unit tests)
theta argument in the dpqr function
becomes the recommended argument to specify the distribution.
alpha, beta, gamma,
delta now give warnings and will be deprecated in future
versions.gamma_Taylor for
better initial estimates.skewness() and
kurtosis() instead directly in
LambertW.moved from gsl to lamW R package: the Lambert W implementation is ~4x faster than for gsl. Needless to say that this will also speed up many computations in the LambertW package. Thank you Avraham Adler for the lamW package.
new functions:
deriv_xexp()normalize_by_tau()log_deriv_W()lots of performance improvements (not only due to lamW package). Leads to 2-3x faster estimation via IGMM or MLE overall.
added (first iteration) of unit tests using the testthat package
NAMESPACE following new
CRAN policiesfrom = 0 in plot.LambertW_fit for
scale families with all positive values.get_distname_family returns a third logical entry
non.negative to check whether a distribution is for
non-negative random variables (e.g., exponential or Gamma).loglik_penalty loses the “distname” argument, but gains
the “is.non.negative” argument.any(is.na(...)) with
anyNA(...) (small speedups)deriv_W faster and more precise using a log
transform first and using mathematial identities of the derivative of W,
its derivative, and logarithm.delta_GMM and
gamma_GMM (it’s about 30% faster than “nlm”)delta_GMM: for delta too large
(>1e100) the backtransformed data u would
become negligibly small and numerically a constant
(1e-100); thus kurtosis() estimate would be
NaN, which resulted in stop of nlm function in
delta_GMM. Added an NA check and returned
large value for objective function, for nlm() to search for
a better delta. backtransformedget_initial_theta: if initial estimates of
gamma are too extreme, then the backtransformed input data
for X contains NA. This caused an error in
estimate_beta(). Now NAs are removed before
passing x.init to estimate_beta()log_W(Inf) returned NA; fixed to return
Inf.qLambertW() didn’t compute correct quantiles for
non-negative distributions (e.g., "exp" or
"gamma") and type = "s"; replaced now with
closed form expressions.See also ?deprecated-function:
H(): use xexp() insteaddata input to Gaussianize() does not have
to have colnames; will be assigned by default if
colnames(data) = NULLmLambertW which ignored delta
values passed via thetaSeveral deprecated functions (see also
?deprecated-function):
normfit(): use test_normality() (or short
test_norm()) insteadgrid to test_normality (previously
known as normfit)Version 0.5 is a long awaited - big - update to the LambertW package. That’s why it’s a big bump from 0.2.9.9 to 0.5.
It has lots of improved code, bug fixes, more user friendly function (names) and implementation, more explicit error checking and meaningful error messages, etc.
Definitely check out the new manual - it has been reviewed very thoroughly.
W() (and related functions) gained a
branch argument (see also deprecated functions below).Gaussianize() gained several new arguments that allow
to do the inverse ‘’DeGaussianization’’ as well. See
?Gaussianize for details.check_beta()check_distname()check_tau()deriv_W_gamma()estimate_beta()get_distname_family()get_distnames()get_gamma_bounds()get_initial_tau()get_output() (due to popular demand)log_W()tau2theta()NEWS fileCITATION file. See citation information with
citation("LambertW")FALSE).theta as argument in functions instead of
alpha, beta, gamma, or
delta. Passing the elements as single arguments still
works, but using
theta = list(beta = ..., gamma = ..., delta = ..., alpha = ...)
is preferred. In future versions the alpha,
beta, gamma, and delta arguments
will be deprecated.normfit():
_ as separator in function names= to <-_ to
. (unless it _ helps understanding;
e.g.,mu_y reminds of mu with the y
subscript in LaTeX / pdf)get_initial_theta() instead of
starting_theta(); get_support() instead of
support())normfit is often called for
visual checks only, I made the normality tests optional. They are called
if the nortest package is available
(require(nortest) == TRUE); otherwise it just returns
NA. This is useful in case users do not have the
nortest package available in their R installation.qU() and pU(): incorrect
usage of standard deviation vs scale in t distribution
(dU() and thus log-likelihood was correct).ks.test.t now uses the scale parameter, rather than
standard deviation. This now allows to test also if degrees of freedom
< 2.MLE_LambertW changed the estimate.only
argument to return.estimate.only.Several deprecated functions (see also
?deprecated-function):
beta_names(): use get_beta_names()bounds_theta(): use
get_theta_bounds()d1W() and d1W_1(): use
deriv_W(..., branch).d1W_delta(), d1W_delta_alpha(): use
deriv_W_delta() and
deriv_W_delta_alpha().get.input(): use get_input()p_1(): use p_1m()params2theta(): use unflatten_theta()skewness_test(): use test_symmetry()starting_theta(): use
get_initial_theta()support(): use get_support()theta2params(): use flatten_theta()vec.norm(): use lp_norm()W_1(): use W(z, branch = -1); similarly
for W_gamma_1()W_2delta_alpha(): use
W_2delta_2alpha().W_gamma_1(): use
W_gamma(..., branch = -1).G() since it was never used. If you need it use
G_delta(z, delta = 0).MLE_LambertW_new() and
(MLE_LambertW_new.default()); MLE_LambertW now
works also for unbounded optimziation..default methods for IGMM and
MLE_LambertW. They just work one way on a numeric
vector.optim(..., hessian = TRUE) instead.im@gmge.orgget.input() had the wrong variable for
nu > 2 (u instead of uu)loglik_penalty() returned NA for
0/0 when computing inverse transformation. Replaced this
term with equivalent expression avoiding 0/0.im@gmge.org)
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