Sun, 13 Oct 2019

Package StratifiedMedicine updated to version 0.2.0 with previous version 0.1.3 dated 2019-09-04

Title: Stratified Medicine
Description: A toolkit for stratified medicine, subgroup identification, and precision medicine. Current tools include (1) filtering models (reduce covariate space), (2) patient-level estimate models (counterfactual patient-level quantities, for example the individual treatment effect), (3) subgroup identification models (find subsets of patients with similar treatment effects), and (4) parameter estimation and inference (for the overall population and discovered subgroups). These tools can directly feed into stratified medicine algorithms including PRISM (patient response identifiers for stratified medicine; Jemielita and Mehrotra 2019 (in progress)). PRISM is a flexible and general framework which accepts user-created models/functions. This package is in beta and will be continually updated.
Author: Thomas Jemielita [aut, cre]
Maintainer: Thomas Jemielita <thomasjemielita@gmail.com>

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Package cgam updated to version 1.15 with previous version 1.14 dated 2019-05-01

Title: Constrained Generalized Additive Model
Description: A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions. It can also fit a convex or concave regression surface with triangle splines without additivity assumptions. See Liao X, Meyer MC (2019)<doi: 10.18637/jss.v089.i05> for more details.
Author: Mary C. Meyer and Xiyue Liao
Maintainer: Xiyue Liao <liaoxiyue2011@gmail.com>

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Package EpiILMCT updated to version 1.1.4 with previous version 1.1.3 dated 2019-04-17

Title: Continuous Time Distance-Based and Network-Based Individual Level Models for Epidemics
Description: Provides tools for simulating from continuous-time individual level models of disease transmission, and carrying out infectious disease data analyses with the same models. The epidemic models considered are distance-based and/or contact network-based models within Susceptible-Infectious-Removed (SIR) or Susceptible-Infectious-Notified-Removed (SINR) compartmental frameworks.
Author: Waleed Almutiry [aut, cre], Rob Deardon [aut, ths], Vineetha Warriyar K. V. [ctb]
Maintainer: Waleed Almutiry <wkmtierie@qu.edu.sa>

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Package idiogramFISH updated to version 1.6.3 with previous version 1.6.1 dated 2019-10-04

Title: Idiograms with Marks and Karyotype Indices
Description: Plot idiograms of several karyotypes having a set of dataframes for chromosome data and optionally mark data. Includes also a function to plot holocentrics and its marks supporting micrometers and Mb. Marks can have square or dot form, its legend (label) can be drawn inline or to the right of karyotypes. It is possible to calculate chromosome indices by Levan et al. (1964) <doi:10.1111/j.1601-5223.1964.tb01953.x> , karyotype indices of Watanabe et al. (1999) <doi:10.1007/PL00013869> and Romero-Zarco (1986) <doi:10.2307/1221906> and classify chromosomes by morphology Guerra (1986) and Levan et al. (1964).
Author: Fernando Roa [aut, cre], Mariana PC Telles [ctb]
Maintainer: Fernando Roa <froao@unal.edu.co>

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Package wrapr updated to version 1.9.2 with previous version 1.9.1 dated 2019-10-06

Title: Wrap R Tools for Debugging and Parametric Programming
Description: Tools for writing and debugging R code. Provides: '%.>%' dot-pipe (an 'S3' configurable pipe), 'let()' (converts non-standard evaluation interfaces to parametric standard evaluation interfaces, inspired by 'gtools:strmacro()' and 'base::bquote()'), 'build_frame()'/'draw_frame()' ('data.frame' example tools), 'qc()' (quoting concatenate), ':=' (named map builder), and more.
Author: John Mount [aut, cre], Nina Zumel [aut], Win-Vector LLC [cph]
Maintainer: John Mount <jmount@win-vector.com>

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Package fields updated to version 9.9 with previous version 9.8-6 dated 2019-08-19

Title: Tools for Spatial Data
Description: For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development of this package was supported in part by the National Science Foundation Grant 1417857 and the National Center for Atmospheric Research. See the Fields URL for a vignette on using this package and some background on spatial statistics.
Author: Douglas Nychka [aut, cre], Reinhard Furrer [aut], John Paige [aut], Stephan Sain [aut], Florian Gerber [aut], Matthew Iverson [aut], University Corporation for Atmospheric Research [cph]
Maintainer: Douglas Nychka <douglasnychka@gmail.com>

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Package raceland updated to version 1.0.3 with previous version 1.0.0 dated 2019-09-04

Title: Pattern-Based Zoneless Method for Analysis and Visualization of Racial Topography
Description: Implements a computational framework for a pattern-based, zoneless analysis, and visualization of (ethno)racial topography. It is a reimagined approach for analyzing residential segregation and racial diversity based on the concept of 'landscape’ used in the domain of landscape ecology.
Author: Jakub Nowosad [aut, cre] (<https://orcid.org/0000-0002-1057-3721>), Anna Dmowska [aut], Tomasz Stepinski [aut]
Maintainer: Jakub Nowosad <nowosad.jakub@gmail.com>

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Package noweb updated to version 1.1-2 with previous version 1.0-4 dated 2013-03-27

Title: The 'noweb' System for R
Description: The noweb system for source code, implemented in R.
Author: Terry Therneau
Maintainer: Terry Therneau <terry.therneau@mayo.edu>

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Package glmmEP updated to version 1.0-3 with previous version 1.0-2 dated 2019-09-16

Title: Generalized Linear Mixed Model Analysis via Expectation Propagation
Description: Approximate frequentist inference for generalized linear mixed model analysis with expectation propagation used to circumvent the need for multivariate integration. In this version, the random effects can be any reasonable dimension. However, only probit mixed models with one level of nesting are supported. The methodology is described in Hall, Johnstone, Ormerod, Wand and Yu (2018) <arXiv:1805.08423v1>.
Author: Matt P. Wand [aut, cre], James C.F. Yu [aut]
Maintainer: Matt P. Wand <matt.wand@uts.edu.au>

Diff between glmmEP versions 1.0-2 dated 2019-09-16 and 1.0-3 dated 2019-10-13

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Package redland updated to version 1.0.17-11 with previous version 1.0.17-10 dated 2018-07-20

Title: RDF Library Bindings in R
Description: Provides methods to parse, query and serialize information stored in the Resource Description Framework (RDF). RDF is described at <http://www.w3.org/TR/rdf-primer>. This package supports RDF by implementing an R interface to the Redland RDF C library, described at <http://librdf.org/docs/api/index.html>. In brief, RDF provides a structured graph consisting of Statements composed of Subject, Predicate, and Object Nodes.
Author: Matthew B. Jones [aut, cre], Peter Slaughter [aut], Jeroen Ooms [aut], Carl Boettiger [aut], Scott Chamberlain [aut], David Beckett [cph], University of Bristol [cph], Regents of the University of California [cph]
Maintainer: Matthew B. Jones <jones@nceas.ucsb.edu>

Diff between redland versions 1.0.17-10 dated 2018-07-20 and 1.0.17-11 dated 2019-10-13

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 redland-1.0.17-11/redland/README.md                                   |    4 
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 41 files changed, 249 insertions(+), 794 deletions(-)

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Package interpret updated to version 0.1.22 with previous version 0.1.21 dated 2019-10-10

Title: Fit Interpretable Models and Explain Blackbox Machine Learning
Description: Machine Learning package for training interpretable models and explaining blackbox systems. Historically, the most intelligible models were not very accurate, and the most accurate models were not intelligible. Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM) which has both high accuracy and intelligibility. EBM uses machine learning techniques like bagging and boosting to breathe new life into traditional GAMs (Generalized Additive Models). This makes them as accurate as random forests and gradient boosted trees, and also enhances their intelligibility and editability. Details on the EBM algorithm can be found in the paper by Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad (2015, <doi:10.1145/2783258.2788613>).
Author: Samuel Jenkins [aut], Harsha Nori [aut], Paul Koch [aut], Rich Caruana [aut, cre], Microsoft Corporation [cph]
Maintainer: Rich Caruana <interpretml@outlook.com>

Diff between interpret versions 0.1.21 dated 2019-10-10 and 0.1.22 dated 2019-10-13

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New package fdaPDE with initial version 0.1-5
Package: fdaPDE
Version: 0.1-5
Date: 2019-10-12
Title: Functional Data Analysis and Partial Differential Equations; Statistical Analysis of Functional and Spatial Data, Based on Regression with Partial Differential Regularizations
Authors@R: c(person("Eardi", "Lila", role = c("aut", "cre"), email = "el425@cam.ac.uk"), person("Laura M.", "Sangalli", role = "aut", email = "laura.sangalli@polimi.it"), person("Jim", "Ramsay", role = "aut", email = " ramsay@psych.mcgill.ca"), person("Luca", "Formaggia", role = "aut", email = " luca.formaggia@polimi.it"))
Author: Eardi Lila [aut, cre], Laura M. Sangalli [aut], Jim Ramsay [aut], Luca Formaggia [aut]
Maintainer: Eardi Lila <el425@cam.ac.uk>
Depends: R (>= 3.0.0), stats, grDevices, graphics, rgl
LinkingTo: RcppEigen
Suggests: MASS
Description: An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization.
License: CC BY-NC-SA 4.0
Copyright: See the individual source files for copyrights information
NeedsCompilation: yes
SystemRequirements: C++11
RoxygenNote: 6.1.1
Packaged: 2019-10-13 14:21:44 UTC; eardi
Repository: CRAN
Date/Publication: 2019-10-13 14:50:02 UTC

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Package clues updated to version 0.6.1 with previous version 0.5.9 dated 2016-10-14

Title: Clustering Method Based on Local
Description: We developed the clues R package to provide functions for automatically estimating the number of clusters and getting the final cluster partition without any input parameter except the stopping rule for convergence. The package also provides functions to evaluate and compare the performances of partitions of a data set both numerically and graphically.
Author: Fang Chang [aut, ctb], Weiliang Qiu [aut, cre], Vincent Carey [aut, ctb], Ruben H. Zamar [aut, ctb], Ross Lazarus [aut, ctb], Xiaogang Wang [aut, ctb]
Maintainer: Weiliang Qiu <weiliang.qiu@gmail.com>

Diff between clues versions 0.5.9 dated 2016-10-14 and 0.6.1 dated 2019-10-13

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Package tmle updated to version 1.4.0 with previous version 1.3.0-2 dated 2019-02-20

Title: Targeted Maximum Likelihood Estimation
Description: Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of Biostatistics, 2(1), 2006. This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC). The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.
Author: Susan Gruber [aut, cre], Mark van der Laan [aut], Chris Kennedy [ctr]
Maintainer: Susan Gruber <sgruber@cal.berkeley.edu>

Diff between tmle versions 1.3.0-2 dated 2019-02-20 and 1.4.0 dated 2019-10-13

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New package Hmsc with initial version 3.0-2
Package: Hmsc
Title: Hierarchical Model of Species Communities
Type: Package
Version: 3.0-2
Authors@R: c(person(given = "Gleb", family="Tikhonov", role="aut"), person(given="Otso", family="Ovaskainen", email="otso.ovaskainen@helsinki.fi", comment = c(ORCID="0000-0001-9750-4421"), role=c("aut","cre")), person(given="Jari", family="Oksanen", role="aut"), person(given="Melinda", family=c("de", "Jonge"), role="aut"), person(given="Oystein", family="Opedal", role="aut"), person(given="Tad", family="Dallas", role="aut"))
Description: Hierarchical Modelling of Species Communities (HMSC) is a model-based approach for analyzing community ecological data. This package implements it in the Bayesian framework with Gibbs Markov chain Monte Carlo (MCMC) sampling.
License: GPL-3 | file LICENSE
URL: https://www.helsinki.fi/en/researchgroups/statistical-ecology/hmsc
BugReports: https://github.com/hmsc-r/HMSC/issues
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1.9000
Suggests: R.rsp, testthat, corrplot
VignetteBuilder: R.rsp
Depends: R (>= 3.0.2), coda
Imports: abind, ape, BayesLogit, fields, FNN, ggplot2, MASS, Matrix, MCMCpack, methods, mvtnorm, nnet, parallel, pdist, pROC, phytools, statmod, truncnorm
NeedsCompilation: no
Packaged: 2019-10-11 11:55:12 UTC; jarioksa
Author: Gleb Tikhonov [aut], Otso Ovaskainen [aut, cre] (<https://orcid.org/0000-0001-9750-4421>), Jari Oksanen [aut], Melinda de Jonge [aut], Oystein Opedal [aut], Tad Dallas [aut]
Maintainer: Otso Ovaskainen <otso.ovaskainen@helsinki.fi>
Repository: CRAN
Date/Publication: 2019-10-13 14:00:06 UTC

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Package BNSP updated to version 2.1.1 with previous version 2.1.0 dated 2019-05-24

Title: Bayesian Non- And Semi-Parametric Model Fitting
Description: MCMC algorithms & processing functions for non- and semi-parametric models: 1. Dirichlet process mixtures & 2. spike-slab for multivariate (and univariate) regression, with nonparametric models for the means, the variances and the correlation matrix.
Author: Georgios Papageorgiou
Maintainer: Georgios Papageorgiou <gpapageo@gmail.com>

Diff between BNSP versions 2.1.0 dated 2019-05-24 and 2.1.1 dated 2019-10-13

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 15 files changed, 1052 insertions(+), 850 deletions(-)

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New package treeHMM with initial version 0.1.0
Package: treeHMM
Type: Package
Title: Tree Structured Hidden Markov Model
Version: 0.1.0
Authors@R: c(person("Prajwal","Bende", role=c("aut","cre"),email="pbende@ualberta.ca"))
Author: Prajwal Bende [aut, cre]
Maintainer: Prajwal Bende <pbende@ualberta.ca>
Description: Used for Inference, Prediction and Parameter learning for tree structured Hidden Markov Model. The package propose a new architecture of Hidden Markov Model(HMM) known as Tree Structured HMM which could be used in various applications which involves graphs, trees etc.
License: GPL (>= 2.0.0)
Encoding: UTF-8
LazyData: true
Imports: Matrix, gtools, future, matrixStats, PRROC
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-10-11 13:02:11 UTC; Prajwal
Repository: CRAN
Date/Publication: 2019-10-13 11:10:02 UTC

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New package INLAMSM with initial version 0.1
Package: INLAMSM
Type: Package
Title: Multivariate Spatial Models with 'INLA'
Version: 0.1
Authors@R: c( person(given = "Francisco", family = "Palmí-Perales", email = "Francisco.Palmi@uclm.es", role = c("aut") ), person(given = "Virgilio", family = "Gómez-Rubio", email = "Virgilio.Gomez@uclm.es", role = c("aut", "cre") ), person(given = "Miguel A.", family = "Martinez-Beneito", email = "martinez_mig@gva.es", role = c("aut") ) )
Maintainer: Virgilio Gómez-Rubio <Virgilio.Gomez@uclm.es>
Description: Implementation of several multivariate areal latent effects for 'INLA' using the 'rgeneric' latent effect (Palmí-Perales et al., 2019, <arXiv:1909.10804>). The 'INLA' package can be downloaded from <http://www.r-inla.org>. In particular, the package includes latent effects ready to use for several multivariate spatial models: intrinsic CAR, proper CAR and the M-model (Botella-Rocamora et al., 2015, <doi:10.1002/sim.6423>).
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Depends: Matrix, MCMCpack
Suggests: INLA, spData, spdep, rgdal
Additional_repositories: https://inla.r-inla-download.org/R/stable/
NeedsCompilation: no
Packaged: 2019-10-11 11:57:29 UTC; virgil
Author: Francisco Palmí-Perales [aut], Virgilio Gómez-Rubio [aut, cre], Miguel A. Martinez-Beneito [aut]
Repository: CRAN
Date/Publication: 2019-10-13 11:00:02 UTC

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Package wyz.code.testthat updated to version 1.1.9 with previous version 1.1.6 dated 2019-08-28

Title: Wizardry Code Offensive Programming Test Generation
Description: Allows to generate automatically 'testthat' code files from offensive programming test cases. Generated test files are complete and ready to run. Using 'wyz.code.testthat' you will earn a lot of time, reduce the number of errors in test case production, be able to test immediately generated files without any need to view or modify them, and enter a zero time latency between code implementation and industrial testing. As with 'testthat', you may complete provided test cases according to your needs to push testing further, but this need is nearly void when using 'wyz.code.offensiveProgramming'. Refer to chapter 9 of Offensive Programming Book, Fabien GELINEAU (2019, ISBN:979-10-699-4075-8), to learn about details and get value from this package.
Author: Fabien Gelineau <neonira@gmail.com>
Maintainer: Fabien Gelineau <neonira@gmail.com>

Diff between wyz.code.testthat versions 1.1.6 dated 2019-08-28 and 1.1.9 dated 2019-10-13

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New package wyz.code.metaTesting with initial version 1.1.4
Package: wyz.code.metaTesting
Type: Package
Title: Wizardry Code Meta Testing
Version: 1.1.4
Author: Fabien Gelineau <neonira@gmail.com>
Maintainer: Fabien Gelineau <neonira@gmail.com>
Description: Test R any R function without having to provide parameter values. Values will be generated, based on semantic naming of parameters as introduced by package 'wyz.code.offensiveProgramming'. Generated tests can be saved and reused. Value generation logic can be completed with your own specific data types and generation schemes, to meet your requirements. Main benefits of 'wyz.code.metaTesting' is higher developer productivity, reduced time to production, and industrial inference testing. Refer to chapter 10 of Offensive Programming Book, Fabien GELINEAU (2019, ISBN:979-10-699-4075-8), to learn about details and get value from this package.
Encoding: UTF-8
LazyData: true
License: GPL-3
Depends: R (>= 3.5)
Imports: methods, data.table (>= 1.11.8), tidyr, lubridate (>= 1.7.4), wyz.code.offensiveProgramming (>= 1.1.12), crayon, utils, stats
Suggests: testthat, knitr, rmarkdown
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-10-11 09:40:17 UTC; fgelineau
Repository: CRAN
Date/Publication: 2019-10-13 10:00:02 UTC

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New package genvar with initial version 0.0.1.4
Package: genvar
Title: An Imperative Library for Data Manipulation
Version: 0.0.1.4
Authors@R: person("Zach", "Flynn", email = "zlflynn@gmail.com", role = c("aut", "cre"))
Description: Implements tools for manipulating data sets and performing regressions in a way that is familiar to users of a popular, but proprietary, statistical package commonly used in the social sciences. Loads a single dataset into memory and implements a set of imperative commands to modify that data and perform regressions and other analysis on the dataset. Offers an alternative to standard R's function-based approach to data manipulation.
Depends: R (>= 3.5.1.0)
Imports: Formula, foreign, readstata13, sandwich, plm, clubSandwich
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
BugReports: https://github.com/flynnzac/genvar
NeedsCompilation: no
Packaged: 2019-10-10 23:45:30 UTC; fzac
Author: Zach Flynn [aut, cre]
Maintainer: Zach Flynn <zlflynn@gmail.com>
Repository: CRAN
Date/Publication: 2019-10-13 09:40:02 UTC

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Package SourceSet (with last version 0.1.1) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2018-04-24 0.1.1
2018-02-28 0.1.0

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Package mdsstat (with last version 0.3.1) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2019-05-18 0.3.1
2019-04-11 0.3.0
2019-01-24 0.2.2
2018-12-11 0.2.1
2018-09-26 0.2.0
2018-06-10 0.1.0

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Package tryCatchLog (with last version 1.1.4) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2019-03-25 1.1.4
2019-03-20 1.1.2
2018-05-18 1.0.2

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Package simPATHy (with last version 0.2) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2016-09-21 0.2
2016-07-18 0.1

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Package rhierbaps (with last version 1.1.2) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2019-05-09 1.1.2
2018-10-10 1.1.0
2018-07-20 1.0.0

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Package stplanr updated to version 0.4.0 with previous version 0.3.1 dated 2019-09-17

Title: Sustainable Transport Planning
Description: Tools for transport planning with an emphasis on spatial transport data and non-motorized modes. Enables common transport planning tasks including: downloading and cleaning transport datasets; creating geographic "desire lines" from origin-destination (OD) data; route assignment, locally and via interfaces to routing services such as <http://cyclestreets.net/>; calculation of route segment attributes such as bearing and aggregate flow; and 'travel watershed' analysis. See Lovelace and Ellison (2018) <doi:10.32614/RJ-2018-053>.
Author: Robin Lovelace [aut, cre] (<https://orcid.org/0000-0001-5679-6536>), Richard Ellison [aut], Malcolm Morgan [aut] (<https://orcid.org/0000-0002-9488-9183>), Barry Rowlingson [ctb] (Author of overline), Nick Bearman [ctb] (Co-author of gclip), Nikolai Berkoff [ctb] (Co-author of line2route), Scott Chamberlain [rev] (Scott reviewed the package for rOpenSci, see https://github.com/ropensci/onboarding/issues/10), Mark Padgham [ctb], Andrea Gilardi [ctb] (<https://orcid.org/0000-0002-9424-7439>)
Maintainer: Robin Lovelace <rob00x@gmail.com>

Diff between stplanr versions 0.3.1 dated 2019-09-17 and 0.4.0 dated 2019-10-13

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Package flashlight updated to version 0.3.0 with previous version 0.2.0 dated 2019-09-18

Title: Shed Light on Black Box Machine Learning Models
Description: Shed light on black box machine learning models by the help of model performance, permutation variable importance (Fisher et al. (2018) <arxiv:1801.01489>), ICE profiles, partial dependence (Friedman J. H. (2001) <doi:10.1214/aos/1013203451>), accumulated local effects (Apley D. W. (2016) <arXiv:1612.08468>), further effects plots, and variable contribution breakdown for single observations (Gosiewska and Biecek (2019) <arxiv:1903.11420>). All tools are implemented to work with case weights and allow for stratified analysis. Furthermore, multiple flashlights can be combined and analyzed together.
Author: Michael Mayer [aut, cre, cph]
Maintainer: Michael Mayer <mayermichael79@gmail.com>

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Package economiccomplexity updated to version 0.2.3 with previous version 0.2.2 dated 2019-09-17

Title: Methods Used in the Economic Complexity Literature
Description: A wrapper of different indices and networks commonly used in Economic Complexity to explore bipartite relations such as countries and their exported products. These methods are also useful for different kind of relations such as countries and their spoken languages. The functions within this package correspond to code implementations of the equations described in Hausmann, et al. (2005) <doi:10.3386/w11905>, Hausmann, et al. (2014) <doi:10.7551/mitpress/9647.001.0001>, and Mariani, et al. (2015) <doi:10.1140/epjb/e2015-60298-7>.
Author: Mauricio Vargas [aut, cre, cph] (<https://orcid.org/0000-0003-1017-7574>), Carlo Bottai [ctb] (improved the eigenvalues calculation), Diego Kozlowski [ctb] (provided initial RCA and proximity functions he used for his thesis), Mark Padgham [rev] (compared PRODY equations from different articles), Nico Pintar [rev] (suggested improvements to special cases in eigenvalues calculation), The World Bank [dtc], Open Trade Statistics [dtc]
Maintainer: Mauricio Vargas <mvargas@dcc.uchile.cl>

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Package metacom updated to version 1.5.2 with previous version 1.5.1 dated 2018-08-17

Title: Analysis of the 'Elements of Metacommunity Structure'
Description: Functions to analyze coherence, boundary clumping, and turnover following the pattern-based metacommunity analysis of Leibold and Mikkelson 2002 <doi:10.1034/j.1600-0706.2002.970210.x>. The package also includes functions to visualize ecological networks, and to calculate modularity as a replacement to boundary clumping.
Author: Tad Dallas [aut, cre], Tom Pulliam [ctb]
Maintainer: Tad Dallas <tad.a.dallas@gmail.com>

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