please_microbiome, a long-format data frame of genus-level
relative abundances from the pediatric study of Lewis et al. (2015),
reconstructed from the public chvlyl/PLEASE repository following the
filtering and recoding conventions of the ZIBR R package
(Chen and Li, 2016, https://CRAN.R-project.org/package=ZIBR). The
data cover 3186 post-baseline observations from 59 subjects
(47 anti-TNF, 12 EEN) and 18 bacterial genera, with the week-0
abundance of each genus stored as a subject-level covariate. The
dataset is documented in R/please_microbiome.R and reproduced by
data-raw/please_microbiome.R.Reject simultaneous formula and legacy arguments for either component or for the subject grouping variable, with explicit error messages.
Removed the unused internal model_name argument and the redundant
NA in the random-effect boundary check.
Clarified that separate component optimization maximizes the joint likelihood under independent random effects and no shared parameters.
Removed installation and export checks for the package itself from
zavr(). The positive density now calls dNVASIM() directly, and
dependency checks and version reporting cover external packages only.
Standardized longitudinal fixed-effect notation as gamma for the
discrete component and beta for the continuous component. Renamed
the discrete coefficient prefix from alpha_ to gamma_ in coef()
and vcov() and aligned printed headings and documentation. Code
that selects discrete coefficients by name must use gamma_.
Added zavr(), a zero-augmented normal-kernel Vasicek regression for
longitudinal responses in [0, 1). A logistic component models presence,
a Vasicek component models the conditional mean of positive responses, and
each component has an independent subject-specific Gaussian random
intercept.
Added zabr(), a zero-augmented beta random-intercept model following
Chen and Li (2016), to provide a directly comparable beta positive
component under the same computational interface.
Added one-sided formula interfaces for the presence and positive components
and a random = ~ 1 | subject interface. The earlier character-vector
arguments remain temporarily available with deprecation warnings.
Added non-adaptive Gauss--Hermite integration, multiple starting values,
component-wise and joint likelihood-ratio tests, Hessian-based covariance
estimates, optimization diagnostics, and print(), coef(), vcov(),
logLik(), nobs(), and BIC() methods.
Joint AIC and BIC are calculated from the complete two-part likelihood.
BIC uses the number of subjects in its penalty, following the
PROC NLMIXED convention. Component-wise criteria are retained as
diagnostics: AIC values are additive, whereas BIC values are not generally
additive because the positive component uses only subjects with at least
one positive response.
Added documentation, references, examples, dependency declarations, S3 registrations, and integration tests for the new longitudinal interfaces.
tau to quantile in
NVASIQ, LVASIQ, and HVASIQ, including their d, p, q, and r
functions and compiled interfaces.tau object. The fixed level is now passed directly, for example as
NVASIQ(quantile = 0.25), stored in the family definition, and embedded as
a numeric literal in the derivative, deviance, initial-value, residual, and
conditional-moment components of the family object.vasicek_envelope() to recover the fixed level from the fitted
model and use it in bootstrap simulation and refitting. Models fitted at
different quantile levels can therefore coexist safely in one session.tau unchanged as the estimated conditional probability at one
among nonzero observations in ZOANVASIM.bodyfat
responses, compares the three quantile kernels, illustrates the zero-, one-,
and zero-and-one-augmented families with transport, trees, and aep,
and documents conditional and marginal fitted quantities and simulated
residual envelopes.HVASIQ family.bodyfat documentation: it contains 10 variables, and its five
response variables are proportions in (0, 1), not values on a 0--100 scale.tau by p in the NVASIM quantile
formula and corrected the description of the bivariate-normal CDF.ZOANVASIM example based on the boundary-valued
inappropriate-stay proportion in aep.print.vasicek_envelope() S3 method and tests for the
print and plot interfaces.ZOANVASIM at nu = 0 or tau = 0 are
nested limiting cases in the closure of its open parameter space.d, p, and q
functions with their implemented behavior.LVASIQ, expanded the package-level references, and documented all four
included datasets in the package overview and README.HVASIQ() GAMLSS family for conditional quantile regression
with a hyperbolic-secant kernel and a user-specified fixed quantile level.dHVASIQ(), pHVASIQ(), qHVASIQ(), and
rHVASIQ() functions, including stable log-probability and tail
calculations.mu and sigma.vasicek_envelope() to fitted HVASIQ models.d/p/q/rZANVASIM(),
d/p/q/rOANVASIM(), and d/p/q/rZOANVASIM(). The established numbered
names remain available. This correspondence allows likelihood-based
methods such as vcov.gamlss() to locate each family's density function.ZANVASIM(), a zero-augmented normal-kernel Vasicek mean family,
with nu modeling the probability at zero.OANVASIM(), a one-augmented normal-kernel Vasicek mean family,
with nu modeling the probability at one.ZOANVASIM(), a zero-and-one-augmented normal-kernel Vasicek mean
family. In this family, nu is the probability at zero and tau is
the conditional probability at one among nonzero observations.d0NVASIM(), p0NVASIM(), q0NVASIM(),
r0NVASIM(), d1NVASIM(), p1NVASIM(), q1NVASIM(),
r1NVASIM(), d01NVASIM(), p01NVASIM(), q01NVASIM(), and
r01NVASIM() functions.vasicek_envelope() for parametric-bootstrap simulated envelopes
based on fitted gamlss objects.vasicek_envelope objects. The
documentation now distinguishes these full quantile--quantile plots from
half-normal plots and explains the finite-sample simulated mean curve.subset must be supplied explicitly or handled
by a custom refit function.dpqr-0NvasicekM.R,
dpqr-1NvasicekM.R, and dpqr-01NvasicekM.R.ZANVASIM, OANVASIM, and ZOANVASIM.x and q and distinguished the boundary
behavior of the zero-augmented, one-augmented, and zero-and-one-augmented
distributions.tau parameter in ZOANVASIM from the fixed
quantile level used by NVASIQ and LVASIQ.OANVASIM and BEOI using the
one-inflated accuracy1 response from the ReadingSkills data.VASIM to NVASIM.VASIQ to NVASIQ.LVASIQ for the logistic-kernel quantile distribution functions.N or L) and parameterization (M or Q) explicitly.NVASIQ() does not accept tau as an argument. For GAMLSS fitting,
tau must be defined as a scalar variable in the global environment.dNVASIQ(), pNVASIQ(), qNVASIQ(), and
rNVASIQ() continue to accept tau explicitly.LVASIQ() GAMLSS family for conditional quantile regression
with the logistic-kernel Vasicek distribution.mu and sigma; model fitting does not use numerical differentiation.qVASIM().log.p handling in all quantile functions.variance
components of VASIM() and VASIQ().VASIQ() and
removed dependence on a global tau object.mu in VASIQ().dpqr-vasicekmean.R and
dpqr-vasicekquant.R.variance = function(mu, sigma) in the VASIM family.variance = function(mu, sigma) in the VASIQ family.