Thu, 29 Aug 2019

Package stochQN updated to version 0.1.1 with previous version 0.1.0 dated 2019-08-29

Title: Stochastic Limited Memory Quasi-Newton Optimizers
Description: Implementations of stochastic, limited-memory quasi-Newton optimizers, similar in spirit to the LBFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) algorithm, for smooth stochastic optimization. Implements the following methods: oLBFGS (online LBFGS) (Schraudolph, N.N., Yu, J. and Guenter, S., 2007 <http://proceedings.mlr.press/v2/schraudolph07a.html>), SQN (stochastic quasi-Newton) (Byrd, R.H., Hansen, S.L., Nocedal, J. and Singer, Y., 2016 <arXiv:1401.7020>), adaQN (adaptive quasi-Newton) (Keskar, N.S., Berahas, A.S., 2016, <arXiv:1511.01169>). Provides functions for easily creating R objects with partial_fit/predict methods from some given objective/gradient/predict functions. Includes an example stochastic logistic regression using these optimizers. Provides header files and registered C routines for using it directly from C/C++.
Author: David Cortes
Maintainer: David Cortes <david.cortes.rivera@gmail.com>

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Package SingleCaseES updated to version 0.4.3 with previous version 0.4.2 dated 2019-06-14

Title: A Calculator for Single-Case Effect Sizes
Description: Provides R functions for calculating basic effect size indices for single-case designs, including several non-overlap measures and parametric effect size measures, and for estimating the gradual effects model developed by Swan and Pustejovsky (2018) <DOI:10.1080/00273171.2018.1466681>. Standard errors and confidence intervals (based on the assumption that the outcome measurements are mutually independent) are provided for the subset of effect sizes indices with known sampling distributions.
Author: James E. Pustejovsky [aut, cre], Daniel M. Swan [aut]
Maintainer: James E. Pustejovsky <jepusto@gmail.com>

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Package LocalControlStrategy updated to version 1.3.3 with previous version 1.3.2 dated 2019-01-07

Title: Local Control Strategy for Robust Analysis of Cross-Sectional Data
Description: Especially when cross-sectional data are observational, effects of treatment selection bias and confounding are revealed by using the Nonparametric and Unsupervised "preprocessing" methods central to Local Control (LC) Strategy. The LC objective is to estimate the "effect-size distribution" that best quantifies a potentially causal relationship between a numeric y-Outcome variable and a t-Treatment variable. This t-variable may be either binary {1 = "new" vs 0 = "control"} or a numeric measure of Exposure level. LC Strategy starts by CLUSTERING experimental units (patients) on their pre-exposure X-Covariates, forming mutually exclusive and exhaustive BLOCKS of relatively well-matched units. The implicit statistical model for LC is thus simple one-way ANOVA. The Within-Block measures of effect-size are Local Rank Correlations (LRCs) when Exposure is numeric with more than two levels. Otherwise, Treatment choice is Nested within BLOCKS, and effect-sizes are LOCAL Treatment Differences (LTDs) between within-cluster y-Outcome Means ["new" minus "control"]. An Instrumental Variable (IV) method is also provided so that Local Average y-Outcomes (LAOs) within BLOCKS may also contribute information for effect-size inferences ...assuming that X-Covariates influence only Treatment choice or Exposure level and otherwise have no direct effects on y-Outcome. Finally, a "Most-Like-Me" function provides histograms of effect-size distributions to aid Doctor-Patient communications about Personalized Medicine.
Author: Bob Obenchain
Maintainer: Bob Obenchain <wizbob@att.net>

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Package LEGIT updated to version 1.3.1 with previous version 1.3 dated 2019-07-26

Title: Latent Environmental & Genetic InTeraction (LEGIT) Model
Description: Constructs genotype x environment interaction (GxE) models where G is a weighted sum of genetic variants (genetic score) and E is a weighted sum of environments (environmental score) using the alternating optimization algorithm by Jolicoeur-Martineau et al. (2017) <arXiv:1703.08111>. This approach has greatly enhanced predictive power over traditional GxE models which include only a single genetic variant and a single environmental exposure. Although this approach was originally made for GxE modelling, it is flexible and does not require the use of genetic and environmental variables. It can also handle more than 2 latent variables (rather than just G and E) and 3-way interactions or more. The LEGIT model produces highly interpretable results and is very parameter-efficient thus it can even be used with small sample sizes (n < 250). Tools to determine the type of interaction (vantage sensitivity, diathesis-stress or differential susceptibility), with any number of genetic variants or environments, are available <arXiv:1712.04058>.
Author: Alexia Jolicoeur-Martineau <alexia.jolicoeur-martineau@mail.mcgill.ca>
Maintainer: Alexia Jolicoeur-Martineau <alexia.jolicoeur-martineau@mail.mcgill.ca>

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Package s2 updated to version 0.4-2 with previous version 0.4-0 dated 2018-04-21

Title: Google's S2 Library for Geometry on the Sphere
Description: R bindings for Google's s2 library for geometric calculations on the sphere.
Author: Ege Rubak [aut, cre], Jeroen Ooms [aut] (configure and cleanup script), Edzer Pebesma [aut] (Interface to sf), Google, Inc. [cph] (Original s2 source code)
Maintainer: Ege Rubak <rubak@math.aau.dk>

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Package mudata2 updated to version 1.0.7 with previous version 1.0.6 dated 2019-03-16

Title: Interchange Tools for Multi-Parameter Spatiotemporal Data
Description: Formatting and structuring multi-parameter spatiotemporal data is often a time-consuming task. This package offers functions and data structures designed to easily organize and visualize these data for applications in geology, paleolimnology, dendrochronology, and paleoclimate. See Dunnington and Spooner (2018) <doi:10.1139/facets-2017-0026>.
Author: Dewey Dunnington [aut, cre] (<https://orcid.org/0000-0002-9415-4582>)
Maintainer: Dewey Dunnington <dewey@fishandwhistle.net>

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Package HelpersMG updated to version 3.7 with previous version 3.6 dated 2019-06-27

Title: Tools for Environmental Analyses, Ecotoxicology and Various R Functions
Description: Contains many functions useful for managing 'NetCDF' files (see <http://en.wikipedia.org/wiki/NetCDF>), get tide levels on any point of the globe, get moon phase and time for sun rise and fall, analyse and reconstruct periodic time series of temperature with irregular sinusoidal pattern, show scales and wind rose in plot with change of color of text, Metropolis-Hastings algorithm for Bayesian MCMC analysis, plot graphs or boxplot with error bars, search files in disk by there names or their content, read the contents of all files from a folder at one time.
Author: Marc Girondot
Maintainer: Marc Girondot <marc.girondot@u-psud.fr>

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Package getCRUCLdata updated to version 0.3.1 with previous version 0.3.0 dated 2019-04-12

Title: 'CRU' 'CL' v. 2.0 Climatology Client
Description: Provides functions that automate downloading and importing University of East Anglia Climate Research Unit ('CRU') 'CL' v. 2.0 climatology data, facilitates the calculation of minimum temperature and maximum temperature and formats the data into a tidy data frame as a 'tibble' or a list of 'raster' 'stack' objects for use. 'CRU' 'CL' v. 2.0 data are a gridded climatology of 1961-1990 monthly means released in 2002 and cover all land areas (excluding Antarctica) at 10 arcminutes (0.1666667 degree) resolution. For more information see the description of the data provided by the University of East Anglia Climate Research Unit, <https://crudata.uea.ac.uk/cru/data/hrg/tmc/readme.txt>.
Author: Adam Sparks [aut, cre] (<https://orcid.org/0000-0002-0061-8359>)
Maintainer: Adam Sparks <adamhsparks@gmail.com>

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Package edgar updated to version 2.0.2 with previous version 2.0.1 dated 2019-03-22

Title: Platform for EDGAR Filing Management and Textual Analysis
Description: In the USA, companies file different forms with the U.S. Securities and Exchange Commission (SEC) through EDGAR (Electronic Data Gathering, Analysis, and Retrieval system). The EDGAR database automated system collects all the different necessary filings and makes it publicly available. Investors, regulators, and researchers often require these forms for various purposes. This package helps in bulk data gathering and textual analysis of EDGAR filings. It downloads filings from SEC server in bulk with a single query. Additionally, it provides various useful functions: extracts 8-K triggering events, extract "Business (Item 1)" and "Management's Discussion and Analysis(Item 7)" sections of annual statements, search filings for desired words, provides sentiment measures, parse filing header information, and provides HTML view of SEC filings.
Author: Gunratan Lonare <lonare.gunratan@gmail.com>, Bharat Patil <bharatspatil@gmail.com>
Maintainer: Gunratan Lonare <lonare.gunratan@gmail.com>

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Package poismf updated to version 0.1.2 with previous version 0.1.1 dated 2019-08-01

Title: Factorization of Sparse Counts Matrices Through Poisson Likelihood
Description: Creates a low-rank factorization of a sparse counts matrix by maximizing Poisson likelihood with l1/l2 regularization with all non-negative latent factors (e.g. for recommender systems or topic modeling) (Cortes, David, 2018, <arXiv:1811.01908>). Similar to hierarchical Poisson factorization, but follows an optimization-based approach with regularization instead of a hierarchical structure, and is fit through either proximal gradient or conjugate gradient instead of variational inference.
Author: David Cortes
Maintainer: David Cortes <david.cortes.rivera@gmail.com>

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Package nord updated to version 1.0.0 with previous version 0.0.1 dated 2018-02-05

Title: Arctic Ice Studio's Nord and Group of Seven Inspired Colour Palettes for 'ggplot2'
Description: Provides the Arctic Ice Studio's Nord and Group of Seven inspired colour palettes for use with 'ggplot2' via custom functions.
Author: Jake Kaupp [aut, cre] (<https://orcid.org/0000-0002-3217-3294>)
Maintainer: Jake Kaupp <jkaupp@gmail.com>

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Package gtrendsR updated to version 1.4.4 with previous version 1.4.3 dated 2019-05-02

Title: Perform and Display Google Trends Queries
Description: An interface for retrieving and displaying the information returned online by Google Trends is provided. Trends (number of hits) over the time as well as geographic representation of the results can be displayed.
Author: Philippe Massicotte [aut, cre], Dirk Eddelbuettel [aut]
Maintainer: Philippe Massicotte <pmassicotte@hotmail.com>

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Package EpiModel updated to version 1.7.3 with previous version 1.7.2 dated 2018-12-18

Title: Mathematical Modeling of Infectious Disease Dynamics
Description: Tools for simulating mathematical models of infectious disease dynamics. Epidemic model classes include deterministic compartmental models, stochastic individual-contact models, and stochastic network models. Network models use the robust statistical methods of exponential-family random graph models (ERGMs) from the Statnet suite of software packages in R. Standard templates for epidemic modeling include SI, SIR, and SIS disease types. EpiModel features an API for extending these templates to address novel scientific research aims.
Author: Samuel Jenness [cre, aut], Steven M. Goodreau [aut], Martina Morris [aut], Emily Beylerian [ctb], Skye Bender-deMoll [ctb], Kevin Weiss [ctb], Shawnee Anderson [ctb]
Maintainer: Samuel Jenness <samuel.m.jenness@emory.edu>

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Package DTSg updated to version 0.1.3 with previous version 0.1.2 dated 2019-03-13

Title: A Class for Working with Time Series Based on 'data.table' and 'R6' with Largely Optional Reference Semantics
Description: Basic time series functionalities such as listing of missing values, application of arbitrary aggregation as well as rolling window functions and automatic detection of periodicity. As it is mainly based on 'data.table', it is fast and - in combination with the 'R6' package - offers reference semantics. In addition to its native R6 interface, it provides an S3 interface inclusive an S3 wrapper method generator for those who prefer the latter.
Author: Gerold Hepp [aut, cre]
Maintainer: Gerold Hepp <ghepp@iwag.tuwien.ac.at>

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New package SECFISH with initial version 0.1.7
Package: SECFISH
Type: Package
Title: Disaggregate Variable Costs
Version: 0.1.7
Author: Isabella Bitetto (COISPA), Loretta Malvarosa (NISEA), Maria Teresa Spedicato (COISPA), Ralf Doering (THUENEN), Joerg Berkenhagen (THUENEN)
Maintainer: Isabella Bitetto <bitetto@coispa.it>
Description: These functions were developed within SECFISH project (Strengthening regional cooperation in the area of fisheries data collection-Socio-economic data collection for fisheries, aquaculture and the processing industry at EU level). They are aimed at identifying correlations between costs and transversal variables by metier using individual vessel data and for disaggregating variable costs from fleet segment to metier level.
License: GPL-2
Depends: R (>= 3.5)
Imports: ggplot2, Hmisc, optimization
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
Packaged: 2019-08-29 12:46:42 UTC; Bitetto Isabella
Repository: CRAN
Date/Publication: 2019-08-29 15:20:02 UTC

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Package runjags updated to version 2.0.4-4 with previous version 2.0.4-2 dated 2016-07-25

Title: Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in JAGS
Description: User-friendly interface utilities for MCMC models via Just Another Gibbs Sampler (JAGS), facilitating the use of parallel (or distributed) processors for multiple chains, automated control of convergence and sample length diagnostics, and evaluation of the performance of a model using drop-k validation or against simulated data. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax. A JAGS extension module provides additional distributions including the Pareto family of distributions, the DuMouchel prior and the half-Cauchy prior.
Author: Matthew Denwood [aut, cre], Martyn Plummer [cph] (Copyright holder of the code in /src/distributions/jags, src/distributions/DPar1.*, configure.ac, and original copyright holder of some modified code where indicated)
Maintainer: Matthew Denwood <md@sund.ku.dk>

Diff between runjags versions 2.0.4-2 dated 2016-07-25 and 2.0.4-4 dated 2019-08-29

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Package Rnightlights updated to version 0.2.4 with previous version 0.2.3 dated 2018-10-13

Title: Satellite Nightlight Data Extraction
Description: Extracts raster and zonal statistics from satellite nightlight rasters downloaded from the United States National Oceanic and Atmospheric Administration (<http://www.noaa.gov>) free data repositories. Both the DMSP-OLS annual and SNPP-VIIRS monthly nightlight raster data are supported. Satellite nightlight raster tiles are downloaded and cropped to the country boundaries using shapefiles from the GADM database of Global Administrative Areas (<http://gadm.org>). Zonal statistics are then calculated at the lowest administrative boundary for the selected country and cached locally for future retrieval. Finally, a simple data explorer/browser is included that allows one to visualize the cached data e.g. graphing, mapping and clustering regional data.
Author: Christopher Njuguna [aut, cre, cph]
Maintainer: Christopher Njuguna <chris.njuguna@gmail.com>

Diff between Rnightlights versions 0.2.3 dated 2018-10-13 and 0.2.4 dated 2019-08-29

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Package HBV.IANIGLA updated to version 0.1.1 with previous version 0.1.0 dated 2019-08-24

Title: Decoupled Hydrological Model for Research and Education Purposes
Description: The HBV (Hydrologiska Byråns Vattenbalansavdelning) hydrological model is decoupled to allow the user to build his/her own model. This version was developed by the author in IANIGLA-CONICET (Instituto Argentino de Nivologia, Glaciologia y Ciencias Ambientales - Consejo Nacional de Investigaciones Cientificas y Tecnicas) for hydroclimatic studies in the Andes. HBV.IANIGLA incorporates modules for precipitation and temperature interpolation, and also for clean and debris covered ice melt estimations.
Author: Ezequiel Toum <etoum@mendoza-conicet.gob.ar>
Maintainer: Ezequiel Toum <etoum@mendoza-conicet.gob.ar>

Diff between HBV.IANIGLA versions 0.1.0 dated 2019-08-24 and 0.1.1 dated 2019-08-29

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More information about HBV.IANIGLA at CRAN
Permanent link

Package enrichR updated to version 2.1 with previous version 2.0 dated 2019-07-25

Title: Provides an R Interface to 'Enrichr'
Description: Provides an R interface to all 'Enrichr' databases. 'Enrichr' is a web-based tool for analysing gene sets and returns any enrichment of common annotated biological features. Quoting from their website 'Enrichment analysis is a computational method for inferring knowledge about an input gene set by comparing it to annotated gene sets representing prior biological knowledge.' See (<http://amp.pharm.mssm.edu/Enrichr/>) for further details.
Author: Wajid Jawaid [aut, cre]
Maintainer: Wajid Jawaid <wj241@alumni.cam.ac.uk>

Diff between enrichR versions 2.0 dated 2019-07-25 and 2.1 dated 2019-08-29

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Package brms updated to version 2.10.0 with previous version 2.9.0 dated 2019-05-23

Title: Bayesian Regression Models using 'Stan'
Description: Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with posterior predictive checks and leave-one-out cross-validation. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Author: Paul-Christian Bürkner [aut, cre]
Maintainer: Paul-Christian Bürkner <paul.buerkner@gmail.com>

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Package periscope updated to version 0.4.4 with previous version 0.4.3 dated 2019-07-25

Title: Enterprise Streamlined 'Shiny' Application Framework
Description: An enterprise-targeted scalable and UI-standardized 'shiny' framework including a variety of developer convenience functions with the goal of both streamlining robust application development while assisting with creating a consistent user experience regardless of application or developer.
Author: Constance Brett [aut, cre], Isaac Neuhaus [aut] (canvasXpress JavaScript Library Maintainer), Ger Inberg [ctb], Bristol-Meyers Squibb (BMS) [cph]
Maintainer: Constance Brett <connie@aggregate-genius.com>

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Package Mercator updated to version 0.9.5 with previous version 0.8.8 dated 2019-08-02

Title: Clustering and Visualizing Distance Matrices
Description: Defines the classes used to explore, cluster and visualize distance matrices, especially those arising from binary data.
Author: Kevin R. Coombes, Caitlin E. Coombes
Maintainer: Kevin R. Coombes <krc@silicovore.com>

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Package survPen updated to version 1.2.0 with previous version 1.1.0 dated 2019-05-02

Title: Multidimensional Penalized Splines for Survival and Net Survival Models
Description: Fits hazard and excess hazard models with multidimensional penalized splines allowing for time-dependent effects, non-linear effects and interactions between several continuous covariates. In survival and net survival analysis, in addition to modelling the effect of time (via the baseline hazard), one has often to deal with several continuous covariates and model their functional forms, their time-dependent effects, and their interactions. Model specification becomes therefore a complex problem and penalized regression splines represent an appealing solution to that problem as splines offer the required flexibility while penalization limits overfitting issues. Current implementations of penalized survival models can be slow or unstable and sometimes lack some key features like taking into account expected mortality to provide net survival and excess hazard estimates. In contrast, survPen provides an automated, fast, and stable implementation (thanks to explicit calculation of the derivatives of the likelihood) and offers a unified framework for multidimensional penalized hazard and excess hazard models. survPen may be of interest to those who 1) analyse any kind of time-to-event data: mortality, disease relapse, machinery breakdown, unemployment, etc 2) wish to describe the associated hazard and to understand which predictors impact its dynamics. See Fauvernier et al. (2019a) <doi:10.21105/joss.01434> for an overview of the package and Fauvernier et al. (2019b) <doi:10.1111/rssc.12368> for the method.
Author: Mathieu Fauvernier [aut, cre], Laurent Roche [aut], Laurent Remontet [aut], Zoe Uhry [ctb], Nadine Bossard [ctb]
Maintainer: Mathieu Fauvernier <mathieu.fauvernier@gmail.com>

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Package Ryacas updated to version 1.0.0 with previous version 0.4.1 dated 2019-02-08

Title: R Interface to the 'Yacas' Computer Algebra System
Description: Interface to the 'yacas' computer algebra system (<http://www.yacas.org/>).
Author: Mikkel Meyer Andersen [aut, cre, cph], Rob Goedman [aut, cph], Gabor Grothendieck [aut, cph], Søren Højsgaard [aut, cph], Grzegorz Mazur [aut, cph], Ayal Pinkus [aut, cph], Nemanja Trifunovic [cph] (UTF-8 part of yacas (src/yacas/include/yacas/utf8*))
Maintainer: Mikkel Meyer Andersen <mikl@math.aau.dk>

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Package nlmixr updated to version 1.1.1-2 with previous version 1.1.1-1 dated 2019-08-23

Title: Nonlinear Mixed Effects Models in Population Pharmacokinetics and Pharmacodynamics
Description: Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the 'RxODE' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).
Author: Matthew Fidler [aut] (<https://orcid.org/0000-0001-8538-6691>), Yuan Xiong [aut], Rik Schoemaker [aut] (<https://orcid.org/0000-0002-7538-3005>), Justin Wilkins [aut] (<https://orcid.org/0000-0002-7099-9396>), Mirjam Trame [aut], Richard Hooijmaijers [aut], Teun Post [aut], Robert Leary [ctb], Wenping Wang [aut, cre], Hadley Wickham [ctb], Dirk Eddelbuettel [cph], Johannes Pfeifer [ctb], Robert B. Schnabel [ctb], Elizabeth Eskow [ctb], Emmanuelle Comets [ctb], Audrey Lavenu [ctb], Marc Lavielle [ctb], David Ardia [cph], Bill Denney [ctb] (<https://orcid.org/0000-0002-5759-428X>), Katharine Mullen [cph]
Maintainer: Wenping Wang <wwang8198@gmail.com>

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Package highfrequency updated to version 0.6.1 with previous version 0.6.0 dated 2019-08-20

Title: Tools for Highfrequency Data Analysis
Description: Provide functionality to manage, clean and match highfrequency trades and quotes data, calculate various liquidity measures, estimate and forecast volatility, detect price jumps and investigate microstructure noise and intraday periodicity.
Author: Kris Boudt [aut, cre], Jonathan Cornelissen [aut], Scott Payseur [aut], Giang Nguyen [ctb], Onno Kleen [ctb] (<https://orcid.org/0000-0003-4731-4640>)
Maintainer: Kris Boudt <Kris.Boudt@econ.kuleuven.be>

Diff between highfrequency versions 0.6.0 dated 2019-08-20 and 0.6.1 dated 2019-08-29

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Package CovTools updated to version 0.5.2 with previous version 0.5.1 dated 2019-01-29

Title: Statistical Tools for Covariance Analysis
Description: Covariance is of universal prevalence across various disciplines within statistics. We provide a rich collection of geometric and inferential tools for convenient analysis of covariance structures, topics including distance measures, mean covariance estimator, covariance hypothesis test for one-sample and two-sample cases, and covariance estimation. For an introduction to covariance in multivariate statistical analysis, see Schervish (1987) <doi:10.1214/ss/1177013111>.
Author: Kyoungjae Lee [aut], Lizhen Lin [ctb], Kisung You [aut, cre] (<https://orcid.org/0000-0002-8584-459X>)
Maintainer: Kisung You <ksyou496@gmail.com>

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New package BayesNSGP with initial version 0.1.0
Package: BayesNSGP
Title: Bayesian Analysis of Non-Stationary Gaussian Process Models
Description: Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) <arXiv:1702.00434v2>). Bayesian inference is carried out using Markov chain Monte Carlo methods via the 'nimble' package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step.
Version: 0.1.0
Date: 2019-08-25
Maintainer: Daniel Turek <dbt1@williams.edu>
Author: Daniel Turek, Mark Risser
Depends: R (>= 3.4.0),nimble
Imports: FNN,Matrix,methods,StatMatch
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-08-29 11:17:48 UTC; dturek
Repository: CRAN
Date/Publication: 2019-08-29 12:50:02 UTC

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Package pct updated to version 0.2.5 with previous version 0.2.3 dated 2019-07-26

Title: Propensity to Cycle Tool
Description: Functions and example data to teach and increase the reproducibility of the methods and code underlying the Propensity to Cycle Tool (PCT), a research project and web application hosted at <https://www.pct.bike/>. For an academic paper on the methods, see Lovelace et al (2017) <doi:10.5198/jtlu.2016.862>.
Author: Robin Lovelace [aut, cre] (<https://orcid.org/0000-0001-5679-6536>), Layik Hama [aut] (<https://orcid.org/0000-0003-1912-4890>)
Maintainer: Robin Lovelace <rob00x@gmail.com>

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Package anytime updated to version 0.3.6 with previous version 0.3.5 dated 2019-07-28

Title: Anything to 'POSIXct' or 'Date' Converter
Description: Convert input in any one of character, integer, numeric, factor, or ordered type into 'POSIXct' (or 'Date') objects, using one of a number of predefined formats, and relying on Boost facilities for date and time parsing.
Author: Dirk Eddelbuettel
Maintainer: Dirk Eddelbuettel <edd@debian.org>

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Package HH updated to version 3.1-37 with previous version 3.1-35 dated 2018-06-05

Title: Statistical Analysis and Data Display: Heiberger and Holland
Description: Support software for Statistical Analysis and Data Display (Second Edition, Springer, ISBN 978-1-4939-2121-8, 2015) and (First Edition, Springer, ISBN 0-387-40270-5, 2004) by Richard M. Heiberger and Burt Holland. This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis. The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print. All chapters have exercises. All functions introduced in the book are in the package. R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package.
Author: Richard M. Heiberger
Maintainer: Richard M. Heiberger <rmh@temple.edu>

Diff between HH versions 3.1-35 dated 2018-06-05 and 3.1-37 dated 2019-08-29

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Package fhidata updated to version 2019.8.27 with previous version 2019.6.24 dated 2019-06-24

Title: Structural Data for Norway
Description: Provides structural data for Norway. Datasets relating to maps, population in municipalities, vaccination coverage for childhood vaccines, municipality/county matching, and how different municipalities have merged/redistricted over time from 2006 to 2019.
Author: Richard White [aut, cre]
Maintainer: Richard White <w@rwhite.no>

Diff between fhidata versions 2019.6.24 dated 2019-06-24 and 2019.8.27 dated 2019-08-29

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New package ExtractTrainData with initial version 5.0.1
Package: ExtractTrainData
Type: Package
Title: Extract Values from Raster
Version: 5.0.1
Date: 2019-08-29
Author: Subhadip Datta
Maintainer: Subhadip Datta <subhadipdatta007@gmail.com>
Description: By using a multispectral image and ESRI shapefile (Point/Polygon), a data table will be generating for classification or regression. The data table will be contained by band wise raster values and class ids.
License: GPL-3
Imports: raster,rgdal,rgeos
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-08-29 09:37:09 UTC; subha
Repository: CRAN
Date/Publication: 2019-08-29 11:00:02 UTC

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Package tsdb updated to version 0.7-1 with previous version 0.7-0 dated 2019-08-27

Title: Terribly-Simple Data Base for Time Series
Description: A terribly-simple data base for numeric time series, written purely in R, so no external database-software is needed. Series are stored in plain-text files (the most-portable and enduring file type) in CSV format. Timestamps are encoded using R's native numeric representation for 'Date'/'POSIXct', which makes them fast to parse, but keeps them accessible with other software. The package provides tools for saving and updating series in this standardised format, for retrieving and joining data, for summarising files and directories, and for coercing series from and to other data types (such as 'zoo' series).
Author: Enrico Schumann [aut, cre] (<https://orcid.org/0000-0001-7601-6576>)
Maintainer: Enrico Schumann <es@enricoschumann.net>

Diff between tsdb versions 0.7-0 dated 2019-08-27 and 0.7-1 dated 2019-08-29

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Package symmetry updated to version 0.1.1 with previous version 0.1.0 dated 2019-08-27

Title: Testing for Symmetry of Data and Model Residuals
Description: Implementations of a large number of tests for symmetry and their bootstrap variants, which can be used for testing the symmetry of random samples around a known or unknown mean. Functions are also there for testing the symmetry of model residuals around zero. Currently, the supported models are linear models and generalized autoregressive conditional heteroskedasticity (GARCH) models (fitted with the 'fGarch' package). All tests are implemented using the 'Rcpp' package which ensures great performance of the code.
Author: Blagoje Ivanović [aut, cre] Bojana Milošević [aut] Marko Obradović [aut]
Maintainer: Blagoje Ivanović <blagoje_ivanovic@matf.bg.ac.rs>

Diff between symmetry versions 0.1.0 dated 2019-08-27 and 0.1.1 dated 2019-08-29

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New package stochQN with initial version 0.1.0
Package: stochQN
Type: Package
Title: Stochastic Limited Memory Quasi-Newton Optimizers
Version: 0.1.0
Date: 2019-08-28
Author: David Cortes
Maintainer: David Cortes <david.cortes.rivera@gmail.com>
URL: https://github.com/david-cortes/stochQN
BugReports: https://github.com/david-cortes/stochQN/issues
Description: Implementations of stochastic, limited-memory quasi-Newton optimizers, similar in spirit to the LBFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) algorithm, for smooth stochastic optimization. Implements the following methods: oLBFGS (online LBFGS) (Schraudolph, N.N., Yu, J. and Guenter, S., 2007 <http://proceedings.mlr.press/v2/schraudolph07a.html>), SQN (stochastic quasi-Newton) (Byrd, R.H., Hansen, S.L., Nocedal, J. and Singer, Y., 2016 <arXiv:1401.7020>), adaQN (adaptive quasi-Newton) (Keskar, N.S., Berahas, A.S., 2016, <arXiv:1511.01169>). Provides functions for easily creating R objects with partial_fit/predict methods from some given objective/gradient/predict functions. Includes an example stochastic logistic regression using these optimizers. Provides header files and registered C routines for using it directly from C/C++.
License: BSD_2_clause + file LICENSE
NeedsCompilation: yes
RoxygenNote: 6.1.1
Packaged: 2019-08-29 05:44:54 UTC; david
Repository: CRAN
Date/Publication: 2019-08-29 09:30:02 UTC

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Package restatapi updated to version 0.3.5 with previous version 0.2.3 dated 2019-07-23

Title: Search and Retrieve Data from Eurostat Database
Description: Eurostat is the statistical office of the European Union and provides high quality statistics for Europe. Large set of the data is disseminated through the Eurostat database (<https://ec.europa.eu/eurostat/data/database>). The tools are using the REST API with the Statistical Data and Metadata eXchange (SDMX <https://sdmx.org>) Web Services (<https://ec.europa.eu/eurostat/web/sdmx-web-services/about-this-service>) to search and download data from the Eurostat database using the SDMX standard.
Author: Mátyás Mészáros [aut, cre]
Maintainer: Mátyás Mészáros <matyas.meszaros@ec.europa.eu>

Diff between restatapi versions 0.2.3 dated 2019-07-23 and 0.3.5 dated 2019-08-29

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New package RAMClustR with initial version 1.0.8
Package: RAMClustR
Type: Package
Title: Mass Spectrometry Metabolomics Feature Clustering and Interpretation
Version: 1.0.8
Date: 2019-08-27
Author: Corey D. Broeckling, Fayyaz Afsar, Steffan Neumann, Asa Ben-Hur, Jessica Prenni.
Maintainer: "Broeckling,Corey" <Corey.Broeckling@ColoState.EDU>
Imports: dynamicTreeCut, fastcluster, ff, InterpretMSSpectrum, BiocManager, httr, jsonlite, preprocessCore, xcms, e1071, gplots, pcaMethods, stringr, xml2, utils, webchem, stringi, RCurl
License: GPL (>= 2)
Description: A feature clustering algorithm for non-targeted mass spectrometric metabolomics data. This method is compatible with gas and liquid chromatography coupled mass spectrometry, including indiscriminant tandem mass spectrometry <DOI: 10.1021/ac501530d> data.
URL: https://github.com/cbroeckl/RAMClustR
Encoding: UTF-8
biocViews: MassSpectrometry, Metabolomics
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-08-28 22:53:15 UTC; cbroeckl
Repository: CRAN
Date/Publication: 2019-08-29 09:20:02 UTC

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New package GPoM.FDLyapu with initial version 1.0
Package: GPoM.FDLyapu
Type: Package
Title: Lyapunov Exponents and Kaplan-Yorke Dimension
Version: 1.0
Date: 2019-08-23
Encoding: UTF-8
Authors@R: c( person("Sylvain", "Mangiarotti", email = "sylvain.mangiarotti@cesbio.cnes.fr",role = c("aut")), person("Mireille", "Huc", email = "mireille.huc@cesbio.cnes.fr", role = c("cre", "aut")), person("Institut de Recherche pour le Développement", role ="fnd"), person("Centre National de la Recherche Scientifique", role ="fnd") )
Maintainer: Mireille Huc <mireille.huc@cesbio.cnes.fr>
Description: Estimation of the spectrum of Lyapunov Exponents and the Kaplan-Yorke dimension of any low-dimensional model of polynomial form. It can be applied, for example, to systems such as the chaotic Lorenz-1963 system or the hyperchaotic Rossler-1979 system. It can also be applied to dynamical models in Ordinary Differential Equations (ODEs) directly obtained from observational time series using the 'GPoM' package. The approach used is semi-formal, the Jacobian matrix being estimated automatically from the polynomial equations. Two methods are made available; one introduced by Wolf et al. (1985) <doi:10.1016/0167-2789(85)90011-9> and the other one introduced by Grond et al. (2003) <doi:10.1016/S0960-0779(02)00479-4>. The package is provided with an interface for a more intuitive usage, it can also be run without the interface. This platform is developed at the Centre d'Etudes Spatiales de la Biosphere (CESBIO), UMR 5126 UPS/CNRS/CNES/IRD, 18 av. Edouard Belin, 31401 TOULOUSE, FRANCE. The developments were funded by the French program Les Enveloppes Fluides et l'Environnement (LEFE, MANU, projects GloMo, SpatioGloMo and MoMu). The French programs Defi InFiNiTi (CNRS) and PNTS (CNRS) are also acknowledged (projects Crops'I Chaos and Musc & SlowFast).
License: CeCILL-2
LazyData: TRUE
RoxygenNote: 6.1.1
Depends: R (>= 2.10), GPoM, deSolve
Suggests: knitr, rmarkdown, rgl, shiny
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-08-29 07:49:40 UTC; hucm
Author: Sylvain Mangiarotti [aut], Mireille Huc [cre, aut], Institut de Recherche pour le Développement [fnd], Centre National de la Recherche Scientifique [fnd]
Repository: CRAN
Date/Publication: 2019-08-29 09:30:05 UTC

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New package OncoBayes2 with initial version 0.4-4
Package: OncoBayes2
Type: Package
Title: Bayesian Logistic Regression for Oncology Dose-Escalation Trials
Description: Bayesian logistic regression model with optional EXchangeability-NonEXchangeability parameter modelling for flexible borrowing from historical or concurrent data-sources. The safety model can guide dose-escalation decisions for adaptive oncology Phase I dose-escalation trials which involve an arbitrary number of drugs. Please refer to Neuenschwander et al. (2008) <doi:10.1002/sim.3230> and Neuenschwander et al. (2016) <doi:10.1080/19466315.2016.1174149> for details on the methodology.
Version: 0.4-4
Date: 2019-08-28
Authors@R: c(person("Novartis", "Pharma AG", role = "cph") ,person("Sebastian", "Weber", email="sebastian.weber@novartis.com", role=c("aut", "cre")) ,person("Andrew", "Bean", email="andrew.bean@novartis.com", role="aut") ,person("Trustees of", "Columbia University", role="cph", comment="src/init.cpp, tools/make_cc.R, R/stanmodels.R, src/Makevars, src/Makevars.win") )
Depends: R (>= 3.4.0), Rcpp (>= 0.12.0), methods
Imports: assertthat, checkmate, Formula, rstan (>= 2.18.1), rstantools (>= 1.4.0), bayesplot (>= 1.4.0), ggplot2 (>= 2.2.1), dplyr (>= 0.7.1), tibble, tidyr, abind, RBesT
LinkingTo: StanHeaders (>= 2.18.0), rstan (>= 2.18.1), BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0)
License: GPL (>= 3)
LazyData: true
NeedsCompilation: yes
Suggests: rmarkdown, knitr, testthat (>= 2.0.0), mvtnorm, tidybayes
VignetteBuilder: knitr
RoxygenNote: 6.1.1
SystemRequirements: GNU make, pandoc (>= 1.12.3), pandoc-citeproc
Encoding: UTF-8
Packaged: 2019-08-28 15:55:54 UTC;
Author: Novartis Pharma AG [cph], Sebastian Weber [aut, cre], Andrew Bean [aut], Trustees of Columbia University [cph] (src/init.cpp, tools/make_cc.R, R/stanmodels.R, src/Makevars, src/Makevars.win)
Maintainer: Sebastian Weber <sebastian.weber@novartis.com>
Repository: CRAN
Date/Publication: 2019-08-29 07:30:06 UTC

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New package oclust with initial version 0.1.0
Package: oclust
Type: Package
Title: Gaussian Model-Based Clustering with Outliers
Version: 0.1.0
Author: Katharine M. Clark, Paul D. McNicholas
Imports: entropy,stats, utils, mclust
Maintainer: Paul D. McNicholas <paulmc@mcmaster.ca>
Description: Provides a function to detect and trim outliers in Gaussian mixture model-based clustering using methods described in Clark and McNicholas (2019) <arXiv:1907.01136>.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: no
Packaged: 2019-08-28 15:01:24 UTC; paul
Repository: CRAN
Date/Publication: 2019-08-29 07:20:02 UTC

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Package IrishDirectorates updated to version 1.4 with previous version 0.2.0 dated 2017-08-02

Title: A Dynamic Bipartite Latent Space Model to Analyse Irish Companies' Boards from 2003 to 2013
Description: Provides the dataset and an implementation of the method illustrated in Friel, N., Rastelli, R., Wyse, J. and Raftery, A.E. (2016) <DOI:10.1073/pnas.1606295113>.
Author: Riccardo Rastelli [aut, cre]
Maintainer: Riccardo Rastelli <riccardoras@gmail.com>

Diff between IrishDirectorates versions 0.2.0 dated 2017-08-02 and 1.4 dated 2019-08-29

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New package iAdapt with initial version 0.1.0
Package: iAdapt
Type: Package
Title: Two-Stage Adaptive Dose-Finding Clinical Trial Design
Version: 0.1.0
Authors@R: c( person("Alyssa", "Vanderbeek", email = "amv2187@cumc.columbia.edu", role = c("aut", "cre")), person("Laura", "Cosgrove", email = "lec2197@cumc.columbia.edu", role = "ctb"), person("Cody", "Chiuzan", email = "cc3780@cumc.columbia.edu", role = "ctb"), person("Elizabeth", "Garrett-Mayer", email = "liz.garrett-mayer@asco.org", role = "ctb"))
Maintainer: Alyssa Vanderbeek <amv2187@cumc.columbia.edu>
Description: Simulate and implement early phase two-stage adaptive dose-finding design developed by Chiuzan et al. (2018) <DOI:10.1080/19466315.2018.1462727>.
Depends: R (>= 3.5.0), shiny, shinydashboard
License: LGPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-08-28 15:06:22 UTC; Alyssa
Author: Alyssa Vanderbeek [aut, cre], Laura Cosgrove [ctb], Cody Chiuzan [ctb], Elizabeth Garrett-Mayer [ctb]
Repository: CRAN
Date/Publication: 2019-08-29 07:30:02 UTC

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New package ExtremalDep with initial version 0.0.3-1
Package: ExtremalDep
Version: 0.0.3-1
Date: 2019-08-08
Title: Extremal Dependence Models
Authors@R: c(person("Boris","Beranger", role = "aut", email="borisberanger@gmail.com"), person("Simone","Padoan", role = c("cre","aut"), email="simone.padoan@unibocconi.it"), person("Giulia", "Marcon", role = "aut", email="giuliamarcongm@gmail.com"), person("Steven G.", "Johnson", role = "ctb", comment = "Author of included cubature fragments"))
Author: Boris Beranger [aut], Simone Padoan [cre, aut], Giulia Marcon [aut], Steven G. Johnson [ctb] (Author of included cubature fragments)
Maintainer: Simone Padoan <simone.padoan@unibocconi.it>
Imports: numDeriv, evd, sn, CompRandFld, quadprog, copula, nloptr, gtools, mvtnorm, rlist, fda
Description: A set of procedures for modelling parametrically and non-parametrically the dependence structure of multivariate extreme-values is provided. The statistical inference is performed with non-parametric estimators, likelihood-based estimators and Bayesian techniques. Adapts the methodologies derived in Beranger et al. (2019) <arxiv:1904.08251>, Beranger et al. (2017) <doi:10.1111/sjos.12240>, Beranger and Padoan (2015) <arxiv:1508.05561>, Marcon et al. (2017) <doi:10.1002/sta4.145>, Marcon et al. (2017) <doi:10.1016/j.jspi.2016.10.004> and Marcon et al. (2016) <doi:10.1214/16-EJS1162>. It also refers to the works of Bortot (2010) <https://pdfs.semanticscholar.org/b0dc/1cb608d35bf515c76e39aacc14b4de82e281.pdf>, Padoan (2011) <doi:10.1016/j.jmva.2011.01.014>, Cooley et al. (2010) <doi:10.1016/j.jmva.2010.04.007>, Husler and Reiss (1989) <doi:10.1016/0167-7152(89)90106-5>, Engelke et al. (2015) <doi:10.1111/rssb.12074>, Coles and Tawn (1991) <doi:10.1111/j.2517-6161.1991.tb01830.x>, Nikoloulopoulos et al. (2011) <doi:10.1007/s10687-008-0072-4>, Opitz (2013) <doi:10.1016/j.jmva.2013.08.008>, Tawn (1990) <doi:10.2307/2336802>, Azzalini (1985) <https://www.jstor.org/stable/pdf/4615982.pdf>, Azzalini and Capitanio (2014) <doi:10.1017/CBO9781139248891>, Azzalini (2003) <doi:10.1111/1467-9469.00322>, Azzalini and Capitanio (1999) <doi:10.1111/1467-9868.00194>, Azzalini and Dalla Valle (1996) <doi:10.1093/biomet/83.4.715>, Einmahl et al. (2013) <doi:10.1007/s10687-012-0156-z>, Naveau et al (2009) <doi:10.1093/biomet/asp001> and Heffernan and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>.
License: GPL (>= 2)
LazyData: yes
NeedsCompilation: yes
Repository: CRAN
Repository/R-Forge/Project: extremaldep
Repository/R-Forge/Revision: 122
Repository/R-Forge/DateTimeStamp: 2019-08-28 15:43:09
Date/Publication: 2019-08-29 07:40:02 UTC
Packaged: 2019-08-28 15:50:08 UTC; rforge
Depends: R (>= 2.10)

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Package pins (with last version 0.1.0) was removed from CRAN

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

2019-08-28 0.1.0

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Package psychReport updated to version 0.7 with previous version 0.4 dated 2018-10-29

Title: Reproducible Reports in Psychology
Description: Helper functions for producing reports in Psychology (Reproducible Research). Provides required formatted strings (APA style) for use in 'Knitr'/'Latex' integration within *.Rnw files.
Author: Ian G Mackenzie
Maintainer: Ian G Mackenzie <ian.mackenzie@uni-tuebingen.de>

Diff between psychReport versions 0.4 dated 2018-10-29 and 0.7 dated 2019-08-29

 psychReport-0.4/psychReport/inst                                        |only
 psychReport-0.7/psychReport/DESCRIPTION                                 |   12 -
 psychReport-0.7/psychReport/MD5                                         |   80 ++++------
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 psychReport-0.7/psychReport/R/fValueString.R                            |    2 
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 psychReport-0.7/psychReport/R/sphericityValueString.R                   |    2 
 psychReport-0.7/psychReport/R/tValueString.R                            |    2 
 psychReport-0.7/psychReport/README.md                                   |    2 
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 psychReport-0.7/psychReport/tests/testthat/test-numValueString.R        |    4 
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 psychReport-0.7/psychReport/tests/testthat/test-tValueString.R          |    4 
 41 files changed, 181 insertions(+), 166 deletions(-)

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New package LBLGXE with initial version 1.4
Package: LBLGXE
Type: Package
Title: Logistic Bayesian Lasso for Rare (or Common) Haplotype Association
Version: 1.4
Author: Xiaochen Yuan, Yuan Zhang, Shuang Xia, Swati Biswas, and Shili Lin
Maintainer: Xiaochen Yuan <xxy142030@utdallas.edu>
Description: This function takes a dataset of haplotypes and environmental covariates with one binary phenotype in which rows for individuals of uncertain phase have been augmented by "pseudo-individuals" who carry the possible multilocus genotypes consistent with the single-locus phenotypes. Bayesian lasso is used to find the posterior distributions of logistic regression coefficients, which are then used to calculate Bayes Factor and credible set to test for association with haplotypes, environmental covariates and interactions. The model can handle complex sampling data, in particular, frequency matched cases and controls with controls obtained using stratified sampling. This version can also be applied to a dataset with no environmental covariate and two correlated binary phenotypes.
Depends: R (>= 2.10), hapassoc, dummies
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
NeedsCompilation: yes
Packaged: 2019-08-25 16:39:18 UTC; xcass
Repository: CRAN
Date/Publication: 2019-08-29 07:00:03 UTC

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Package RxODE updated to version 0.9.1-4 with previous version 0.9.1-3 dated 2019-08-06

Title: Facilities for Simulating from ODE-Based Models
Description: Facilities for running simulations from ordinary differential equation (ODE) models, such as pharmacometrics and other compartmental models. A compilation manager translates the ODE model into C, compiles it, and dynamically loads the object code into R for improved computational efficiency. An event table object facilitates the specification of complex dosing regimens (optional) and sampling schedules. NB: The use of this package requires both C and Fortran compilers, for details on their use with R please see Section 6.3, Appendix A, and Appendix D in the "R Administration and Installation" manual. Also the code is mostly released under GPL. The VODE and LSODA are in the public domain. The information is available in the inst/COPYRIGHTS.
Author: Matthew L. Fidler [aut] (<https://orcid.org/0000-0001-8538-6691>), Melissa Hallow [aut], Wenping Wang [aut, cre], Zufar Mulyukov [ctb], Justin Wilkins [ctb] (<https://orcid.org/0000-0002-7099-9396>), Simon Frost [ctb], Heng Li [ctb], Yu Feng [ctb], Alan Hindmarsh [ctb], Linda Petzold [ctb], Ernst Hairer [ctb], Gerhard Wanner [ctb], J Colinge [ctb], Hadley Wickham [ctb], G Grothendieck [ctb], Robert Gentleman [ctb], Ross Ihaka [ctb], R core team [cph], odepack authors [cph]
Maintainer: Wenping Wang <wwang8198@gmail.com>

Diff between RxODE versions 0.9.1-3 dated 2019-08-06 and 0.9.1-4 dated 2019-08-29

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 src/rxData.cpp                           | 1168 ++++++++++++++-----------------
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 tests/testthat/test-issue-56.R           |    1 
 tests/testthat/test-omega-chol.R         |    2 
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 vignettes/RxODE-events.Rmd               |   46 -
 36 files changed, 1169 insertions(+), 967 deletions(-)

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Package RcppEnsmallen updated to version 0.1.16.0.1 with previous version 0.1.15.0.1 dated 2019-05-20

Title: Header-Only C++ Mathematical Optimization Library for 'Armadillo'
Description: 'Ensmallen' is a templated C++ mathematical optimization library (by the 'MLPACK' team) that provides a simple set of abstractions for writing an objective function to optimize. Provided within are various standard and cutting-edge optimizers that include full-batch gradient descent techniques, small-batch techniques, gradient-free optimizers, and constrained optimization. The 'RcppEnsmallen' package includes the header files from the 'Ensmallen' library and pairs the appropriate header files from 'armadillo' through the 'RcppArmadillo' package. Therefore, users do not need to install 'Ensmallen' nor 'Armadillo' to use 'RcppEnsmallen'. Note that 'Ensmallen' is licensed under 3-Clause BSD, 'Armadillo' starting from 7.800.0 is licensed under Apache License 2, 'RcppArmadillo' (the 'Rcpp' bindings/bridge to 'Armadillo') is licensed under the GNU GPL version 2 or later. Thus, 'RcppEnsmallen' is also licensed under similar terms. Note that 'Ensmallen' requires a compiler that supports 'C++11' and 'Armadillo' 6.500 or later.
Author: James Joseph Balamuta [aut, cre, cph] (<https://orcid.org/0000-0003-2826-8458>), Dirk Eddelbuettel [aut, cph] (<https://orcid.org/0000-0001-6419-907X>)
Maintainer: James Joseph Balamuta <balamut2@illinois.edu>

Diff between RcppEnsmallen versions 0.1.15.0.1 dated 2019-05-20 and 0.1.16.0.1 dated 2019-08-29

 ChangeLog                                                                |   12 
 DESCRIPTION                                                              |    6 
 MD5                                                                      |  154 +++++-----
 NEWS.md                                                                  |   11 
 inst/include/ensmallen.hpp                                               |    1 
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 inst/include/ensmallen_bits/ens_version.hpp                              |    4 
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 inst/include/ensmallen_bits/rmsprop/rmsprop.hpp                          |   13 
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 87 files changed, 601 insertions(+), 301 deletions(-)

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Package chngpt updated to version 2019.8-28 with previous version 2019.3-12 dated 2019-03-13

Title: Estimation and Hypothesis Testing for Threshold Regression
Description: Threshold regression models are also called two-phase regression, broken-stick regression, split-point regression, structural change models, and regression kink models, with and without interaction terms. Methods for both continuous and discontinuous threshold models are included, but the support for the former is much greater. This package is described in Fong, Huang, Gilbert and Permar (2017) chngpt: threshold regression model estimation and inference, BMC Bioinformatics, in press, <DOI:10.1186/s12859-017-1863-x>.
Author: Youyi Fong [cre], Tao Yang [aut], Zonglin He [aut], Adam Elder [aut], Hyunju Son [aut]
Maintainer: Youyi Fong <youyifong@gmail.com>

Diff between chngpt versions 2019.3-12 dated 2019-03-13 and 2019.8-28 dated 2019-08-29

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 chngpt-2019.8-28/chngpt/ChangeLog                               |  101 
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 chngpt-2019.8-28/chngpt/NAMESPACE                               |    5 
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 chngpt-2019.8-28/chngpt/R/sim.chngpt.R                          |   10 
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 22 files changed, 1186 insertions(+), 645 deletions(-)

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Package arules updated to version 1.6-4 with previous version 1.6-3 dated 2019-03-07

Title: Mining Association Rules and Frequent Itemsets
Description: Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). Also provides C implementations of the association mining algorithms Apriori and Eclat. See Christian Borgelt (2012) <doi:10.1002/widm.1074>.
Author: Michael Hahsler [aut, cre, cph], Christian Buchta [aut, cph], Bettina Gruen [aut, cph], Kurt Hornik [aut, cph], Ian Johnson [ctb, cph], Christian Borgelt [ctb, cph]
Maintainer: Michael Hahsler <mhahsler@lyle.smu.edu>

Diff between arules versions 1.6-3 dated 2019-03-07 and 1.6-4 dated 2019-08-29

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 17 files changed, 61 insertions(+), 57 deletions(-)

More information about arules at CRAN
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