Log message:
(geography/R-spatstat.model) Updated 3.3.3 to 3.3.4

        CHANGES IN spatstat.model VERSION 3.3-4

OVERVIEW

    o Improvements to documentation.

    o Bug fix affecting spatstat.linnet

BUG FIXES

    o internal code
    For point process models on a linear network (class 'lppm')
    involving covariates of class 'lintess', the internal structure
    of the fitted model was corrupted, leading to errors in calculating
    properties of the fitted model, such as predict.lppm.
    [Spotted by Andrea Gilardi.]
    [Bug fix requires changes in spatstat.model internal code]
    Fixed.

Log message:
(geography/R-spatstat.model)  Updated 3.2.8 to 3.3.3

        CHANGES IN spatstat.model VERSION 3.3-3

OVERVIEW

    o Bug fixes and minor improvements.

SIGNIFICANT USER-VISIBLE CHANGES

    o model.images.ppm
    Now recognises arguments passed to 'as.mask'
    to control the pixel raster for the images.

BUG FIXES

    o improve.kppm
    Crashed if NA's were present in the covariate values.
    Fixed.

    o model.matrix.ppm
    Crashed with a message about 'logical index too long',
    if NA's were present in the covariate values.
    Fixed.

        CHANGES IN spatstat.model VERSION 3.3-2

OVERVIEW

    o Tweaks to documentation.

    o Improved Palm diagnostic plot.

SIGNIFICANT USER-VISIBLE CHANGES

    o plot.palmdiag
    Improved placement of legend.

        CHANGES IN spatstat.model VERSION 3.3-1

OVERVIEW

    o Internal changes to satisfy CRAN package checker.

        CHANGES IN spatstat.model VERSION 3.3-0

OVERVIEW

    o Package now depends on 'spatstat.univar'.

    o Easier control over quadrature schemes.

    o More options in fitted.slrm

    o Bug fix in predict.ppm.

    o Internal improvements.

PACKAGE DEPENDENCE

    o spatstat.model now depends on the new package 'spatstat.univar'.

SIGNIFICANT USER-VISIBLE CHANGES

    o ppm.ppp
    New argument 'quad.args' is a list of arguments passed to 'quadscheme'
    to control the construction of the quadrature scheme.

    o fitted.slrm
    New argument 'type' allows calculation of fitted probabilities, intensities
    or link function values.

    o fitted.slrm
    New arguments 'dataonly' and 'leaveoneout' allow calculation of fitted
    values at the data points only, using leave-one-out calculation if desired.

BUG FIXES

    o predict.ppm
    Argument 'eps' was ignored in many cases.
    Fixed.

        CHANGES IN spatstat.model VERSION 3.2-11

OVERVIEW

    o Slightly accelerated.

SIGNIFICANT USER-VISIBLE CHANGES

    o spatstat.model package
    Some computations slightly accelerated.

        CHANGES IN spatstat.model VERSION 3.2-10

OVERVIEW

    o Internal bug fix.

        CHANGES IN spatstat.model VERSION 3.2-9

OVERVIEW

    o We thank Marta Luraschi for contributions.

    o Minor improvements and bug fixes.

SIGNIFICANT USER-VISIBLE CHANGES

    o vcov.kppm
    If any quadrature points have NA values for one of the covariates,
    these quadrature points are ignored in the variance calculation,
    with a warning.

    o vcov.kppm
    Minor change to formal arguments.

    o vcov.ppm
    Minor change to formal arguments.

BUG FIXES

    o vcov.kppm
    If any quadrature points had NA values for one of the covariates,
    the result was a matrix of NA values.
    Fixed.

Log message:
(geography/R-spatstat.model) import R-spatstat.model-3.2.8

Functionality for parametric statistical modelling and inference for
spatial data, mainly spatial point patterns, in the 'spatstat' family
of packages. (Excludes analysis of spatial data on a linear network,
which is covered by the separate package 'spatstat.linnet'.) Supports
parametric modelling, formal statistical inference, and model
validation. Parametric models include Poisson point processes, Cox
point processes, Neyman-Scott cluster processes, Gibbs point processes
and determinantal point processes. Models can be fitted to data using
maximum likelihood, maximum pseudolikelihood, maximum composite
likelihood and the method of minimum contrast. Fitted models can be
simulated and predicted. Formal inference includes hypothesis tests
(quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman
test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised
permutation test, segregation test, ANOVA tests of fitted models,
adjusted composite likelihood ratio test, envelope tests, Dao-Genton
test, balanced independent two-stage test), confidence intervals for
parameters, and prediction intervals for point counts. Model
validation techniques include leverage, influence, partial residuals,
added variable plots, diagnostic plots, pseudoscore residual plots,
model compensators and Q-Q plots.