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adrian-lison/EpiSewer: EpiSewer 0.0.1
No full text<p>This is an early development release of EpiSewer. Please be aware that the package is still in an experimental stage and future releases will likely introduce breaking changes.</p>
adrian-lison/generative-nowcasting-study: generative-nowcasting-study
No full text<p>This release incorporates changes made during a revision.</p>
epiforecasts/epinowcast: Epinowcast 0.1.0
No full textepinowcast 0.1.0
This is a major release focusing on improving the user experience, and preparing for future package extensions, with an increase in modularity, development of a flexible and full-featured formula interface, and hopefully future-proofing as far as possible. This prepares the ground for future model extensions which will allow a broad range of real-time infectious disease questions to be better answered. These extensions include:
Modelling missing data (#43).
Non-parametric modelling of delay and reference day logit hazard (#4).
Flexible expectation modelling (#5).
Forecasting beyond the horizon of the data (#3).
Known reporting structures (#33).
Renewal equation-based reproduction number estimation (potentially part of #5).
Latent infections (i.e as implemented in other packages such as EpiNow2, epidemia, etc.).
Convolution-based delay models (i.e hospitalisations and deaths) with partially reported data.
Additional observation models.
If interested in contributing to these features, or other aspects of package development (for example improving post-processing, the coverage of documentation, or contributing case studies) please see our contributing guide and/or just reach out. This is a community project that needs support from its users in order to provide improved tools for real-time infectious disease surveillance.
This release contains multiple breaking changes. If needing the old interface please install 0.0.7 from GitHub. For ease, we have stratified changes below into interface, package, documentation, and model changes. Note the package is still flagged as experimental but is in regular use by the authors.
@adrian-lison, @sbfnk, and @seabbs contributed to this release.
Interface
A fully featured and flexible formula interface has been added that allows the specification of fixed effects, lme4 random effects, and random walks. See #27 by @seabbs.
A major overhaul, as described in #57, to the interface of epinowcast() with a particular focus on improving the modularity of the model components (described as modules in the documentation). All of the package documentation and vignettes have been updated to reflect this new interface. See #112 by @seabbs.
Package
Renamed the package and updated the description to give more clarity about the problem space it focusses on. See #110 by @seabbs.
A new helper function enw_delay_metadata() has been added. This produces metadata about the delay distribution vector that may be helpful in future modelling. This prepares the way for #4 where this data frame will be combined with the reference metadata in order to build non-parametric hazard reference and delay-based models. In addition to adding this function, it has also been added to the output of enw_preprocess_data() in order to make the metadata readily available to end-users. See #80 by @seabbs.
Two new helper functions enw_filter_reference_dates() and enw_filter_report_dates() have been added. These replace enw_retrospective_data() but allow users to similarly construct retrospective data. Splitting these functions out into components also allows for additional use cases that were not previously possible. Note that by definition it is assumed that a report date for a given reference date must be equal or greater (i.e a report cannot happen before the event being reported occurs). See #82 by @sbfnk and @seabbs.
The internal grouping variables have been refactored to reduce the chance of clashes with columns in the data frames supplied by the user. There will also be an error thrown in case of a variable clash, making preprocessing safer. See #102 by @adrian-lison and @seabbs, which solves #99.
Support for preprocessing observations with missing reference dates has been added along with a new data object returned by enw_preprocess_data() that highlights this information to the user (alternatively can be accessed by users using enw_missing_reference()). In addition, these missing observations have been setup to be passed to stan in order to allow their use in modelling. This feature is in preparation of adding full support for missing observations (see #43). See
#106 by @adrian-lison and @seabbs.
The discretised reporting probability function has been extended to handle delays beyond the maximum delay in three different ways: ignore, add to maximum, or normalize. The nowcasting model uses "normalise" though work on this is ongoing. See #113 by @adrian-lison and #121 by @seabbs.
Fixed an issue (#105) with cmdstan 2.30.0 where passing optimisation flags to stanc_options by default was causing a compilation error by not passing these flags by default. See #117 by @sbfnk and @seabbs.
Addition of regression/integration tests against example data for epinowcast() and enw_preprocess_data() with convergence checking for several example nowcasting models. Lower level tests for model tools and model modules have also been added. See #112 by @seabbs.
Model
Added support for parametric exponential delay distributions (note that this is comparable to an intercept-only non-parametric hazard model) and potentially no parametric delay (though this will currently throw an error due to the lack of appropriate non-parametric hazard). See #84 by @seabbs.
Added support for a Poisson observation model though it is recommended that most users make use of the default negative binomial model. See #120 by @seabbs.
Updated the expectation random walk model to use a more efficient cumulative_sum implementation suggested by @adrian-lison in #98]. See #103 by @seabbs.
Aligned the implementation of the overdispersion prior with the prior choice recommendations from the stan wiki. See #111 by @adrian-lison.
Documentation
The model description has been updated to reflect the currently implemented model and to improve readability. The use of reference and report date nomenclature has also been standardised across the package. See #71 by @sbfnk and @seabbs.
Internals
Array declarations in the stan model have been updated. To maintain compatibility with expose_stan_fns() (which itself depends on rstan), additional functionality has been added to parse stan code in this function. See #74, #85, and #93 by @sbfnk and @seabbs.
Remove spurious warnings due to missing initial values for optional parameters. See #76 by @sbfnk and @seabbs.
Pull requests linked to this release
Feature: Add formula interface by @seabbs in https://github.com/epiforecasts/epinowcast/pull/27
ensure all parameters are initialised by @sbfnk in https://github.com/epiforecasts/epinowcast/pull/77
Feature: Add metadelay by @seabbs in https://github.com/epiforecasts/epinowcast/pull/80
future-proof array declarations by @sbfnk in https://github.com/epiforecasts/epinowcast/pull/75
Feature: exponential delays by @seabbs in https://github.com/epiforecasts/epinowcast/pull/84
compatibility with rstan for exposing functions by @sbfnk in https://github.com/epiforecasts/epinowcast/pull/85
Update contributing to include use of develop branch system by @seabbs in https://github.com/epiforecasts/epinowcast/pull/86
Bug safer regex by @seabbs in https://github.com/epiforecasts/epinowcast/pull/93
clarify use of enw_retrospective_data by @sbfnk in https://github.com/epiforecasts/epinowcast/pull/82
Feature cumulative sum expectation by @seabbs in https://github.com/epiforecasts/epinowcast/pull/103
Replace group with .group by @adrian-lison in https://github.com/epiforecasts/epinowcast/pull/102
model description clarifications by @sbfnk in https://github.com/epiforecasts/epinowcast/pull/71
Feature missing reference dates preprocessing by @seabbs in https://github.com/epiforecasts/epinowcast/pull/106
Feature: Update title, description, and citation by @seabbs in https://github.com/epiforecasts/epinowcast/pull/110
Change prior transformation from inv_sqrt to inv_square by @adrian-lison in https://github.com/epiforecasts/epinowcast/pull/111
Update discretised_reporting_prob by @adrian-lison in https://github.com/epiforecasts/epinowcast/pull/113
Fix compilation issue from cmdstan 2.30.0 by @seabbs in https://github.com/epiforecasts/epinowcast/pull/117
Roll back to excluding data beyond the maximum delay by @seabbs in https://github.com/epiforecasts/epinowcast/pull/121
Feature: Redesign interface by @seabbs in https://github.com/epiforecasts/epinowcast/pull/112
Feature: Add support for Poisson observation models by @seabbs in https://github.com/epiforecasts/epinowcast/pull/120
Release 0.1.0 by @seabbs in https://github.com/epiforecasts/epinowcast/pull/125
New Contributors
@sbfnk made their first contribution in https://github.com/epiforecasts/epinowcast/pull/77
Full Changelog: https://github.com/epiforecasts/epinowcast/compare/v0.0.7...v0.1.
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
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koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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