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‎inst/NEWS.Rd

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\name{NEWS}
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\alias{NEWS}
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\title{Recent changes to the fda package}
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\description{
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\itemize{
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\item{Changes in version fda_6.0.3 2022-05-02:}{
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\itemize{
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\item{Landmark registration:}{Landmark registration using function
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\code{landmarkreg} can no longer be done by using
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function \code{smooth.basis} instead of function \code{smooth.morph}. The
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warping function must be strictly monotonic, and we have found that using
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\code{smooth.basis} too often violates this monotonicity constraint. Function
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\code{smooth.morph} ensures monotonicity and in most applications takes negligible
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computer time to do so.
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}
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\item{PACE in fda:}{
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Function \code{pcaPACE} arries out a functional PCA with regularization from the
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estimate of the covariance surface.
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Function \code{scoresPACE} estimates functional Principal Component
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scores through Conditional Expectation (PACE).
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}
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\item{Further changes to \code{smooth.morph} and \code{landmarkreg:}}{
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\code{Smooth.morph} estimates a warping function when the target of the fit by
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registration is a functional data object. This function has been extended
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to work when the target for the fit and the fitted functions have different
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ranges or domains. The warping also maps each boundary into its target
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boundary. Simiarly \code{landmarkreg} uses a small number of discrete
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values to define the warping, and how has an extra argument, \code{x0lim},
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that defines the range of the target domain. Since it defaults to the
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range of the warped domain, it continues to work if not used and the
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domains have the same range.
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}
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\item{Surprisal smoothing:}{This function works with multinomial data that
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evolve over a continuum, such as the value of a latent variable in
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psychometrics. A multinomial observation consists of a set of
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probabilities that are in the open interval (0,1) and sum to one.
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The surprisal value S(P_m) corresponding to a probabity P_m is
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-log_M(P_m), where M is the number of probabities and is the base of
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the logarithm. The inverse function is P(S_m) = M^(-S_m).
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\section{Changes in fda version 6.0.5 (2022-07-02)}{
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\itemize{
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\item{Landmark registration using function
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\code{landmarkreg} can no longer be done by using
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function \code{smooth.basis} instead of function \code{smooth.morph}.
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The warping function must be strictly monotonic, and we have found
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that using\code{smooth.basis} too often violates this monotonicity
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constraint. Function \code{smooth.morph} ensures monotonicity and in most
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in most applications takes negligible computer time to do so.
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}
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\item{Function \code{pcaPACE} carries out a functional PCA with
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regularization from the estimate of the covariance surface.
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Function \code{scoresPACE} estimates functional Principal Component
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scores through Conditional Expectation (PACE).
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}
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\item{\code{Smooth.morph} estimates a warping function when the target of
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the fit by registration is a functional data object. This function has
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been extended to work when the target for the fit and the fitted
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functions have different ranges or domains. The warping also maps each
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boundary into its target boundary. Similarly \code{landmarkreg} uses a
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small number of discrete values to define the warping, and how has an
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extra argument, \code{x0lim}, that defines the range of the target domain.
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Since it defaults to the range of the warped domain, it continues to work
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if not used and the domains have the same range.
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}
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\item{This function works with multinomial data that
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evolve over a continuum, such as the value of a latent variable in
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psychometrics. A multinomial observation consists of a set of
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probabilities that are in the open interval (0,1) and sum to one.
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The surprisal value S(P_m) corresponding to a probabity P_m is
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-log_M(P_m), where M is the number of probabities and is the base of
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the logarithm. The inverse function is P(S_m) = M^(-S_m).
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Surprisal is also known as "self-information" in the field of information
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theory. It has the characteristics of a true metric: Surprisals can be
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added, multiplied by positive numbers, and the difference between two
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surprisal values mean the same thing everywhere along the information.
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continuum. The unit of the metric is called the "M-bit", the
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generalization of the familiar "bit" or "2-bit" for binary data.
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The metric property is not possessed by so-called latent
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variables because they can be arbitrarily monotonically transformed.
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Surprisal is also known as "self-information" in the field of information
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theory. It has the characteristics of a true metric: Surprisals can be
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added, multiplied by positive numbers, and the difference between two
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surprisal values mean the same thing everywhere along the information.
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continuum. The unit of the metric is called the "M-bit", the
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generalization of the familiar "bit" or "2-bit" for binary data.
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The metric property is not possessed by so-called latent
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variables because they can be arbitrarily monotonically transformed.
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Smoothing surprisal data is much easier and faster than smoothing
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probabilities since surprisal values are only constrained to be
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non-negative and are otherwise unbounded.
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Smoothing surprisal data is much easier and faster than smoothing
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probabilities since surprisal values are only constrained to be
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non-negative and are otherwise unbounded.
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The function \code{smooth.surp} estimates smooth curves which fit a set of
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surprisal values and which also satisfy the constraint that their
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probability versions sum to one.
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}
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\item{Improvements in iterative optimisation:}{
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Many functions in the fda package optimize a fitting criterion
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iteratively. Function \code{smooth.monotone} is an example.
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The optimisation algorithm used was a rather early design,
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and many improvements have since been made. In most of our
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optimisations, we have switched to the algorithm to be found
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in Press, Teukolsky, Vetterling and Flannery Numerical Recipes
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volumes. We have noticed a bit improvement in speed, are in
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the process of upgrading all of our optimisers using this
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approach.
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}
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}
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The function \code{smooth.surp} estimates smooth curves which fit a set
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of surprisal values and which also satisfy the constraint that their
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probability versions sum to one.
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}
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\item{Many functions in the fda package optimize a fitting criterion
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iteratively. Function \code{smooth.monotone} is an example.
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The optimisation algorithm used was a rather early design,
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and many improvements have since been made. In most of our
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optimisations, we have switched to the algorithm to be found
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in Press, Teukolsky, Vetterling and Flannery Numerical Recipes
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volumes. We have noticed a bit improvement in speed, are in
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the process of upgrading all of our optimisers using this
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approach.
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}
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}
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}

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