From mzb@deve| @end|ng |rom gm@||@com Mon Jan 22 03:06:10 2024 From: mzb@deve| @end|ng |rom gm@||@com (Mauricio Zambrano-Bigiarini) Date: Sun, 21 Jan 2024 23:06:10 -0300 Subject: [R-pkgs] hydroTSM back on CRAN (v0.7-0 released) Message-ID: Dear all, After being archived on CRAN on 2023-10-1, hydroTSM is finally back on CRAN since January 18th: https://cran.r-project.org/package=hydroTSM. This new version 0.7-0 has several new functions, improvements, bugfixes, and a new dataset, mostly devoted to work with sub-daily and sub-hourly time series. *) New functions: baseflow, plot_pq, calendarHeatmap, subhourly2hourly, subhourly2nminutes, daily2weekly, subdaily2weekly, cmv, si. *) Improvements: climograph, (sub)daily2monthly, subdaily2daily, daily2annual, daily2monthly, subdaily2annual, hip, hydroplot, sname2plot, izoo2rzoo. A detailed description of the previous changes can be found in the new News.md file: https://cran.r-project.org/web/packages/hydroTSM/news/news.html Two vignettes illustrate the usage of the some of the main functions: 1) Analysis of daily precipitation data: https://cloud.r-project.org/web/packages/hydroTSM/vignettes/hydroTSM_Daily_P_Vignette-knitr.pdf 2) 1) Analysis of daily streamflow data: https://cloud.r-project.org/web/packages/hydroTSM/vignettes/hydroTSM_Daily_Q_Vignette-knitr.pdf If you use this package, please cite it correctly as: Zambrano-Bigiarini, Mauricio (2024). hydroTSM: Time Series Management and Analysis for Hydrological Modelling. R package version 0.7-0. URL: https://cran.r-project.org/package=hydroTSM. doi:110.5281/zenodo.839565 Issues and enhancements can be requested on: https://github.com/hzambran/hydroTSM/issues All the best, Mauricio Mauricio Zambrano-Bigiarini, PhD Associate Professor, Universidad de La Frontera Associate Researcher, (CR)2 FONDAP Center Phone: +56 45 259 2812 <+56+45+259+2812> e-mail: mauricio.zambrano at ufrontera.cl [image: webpage] [image: ORCID] [image: Github] [image: Linkedin] [[alternative HTML version deleted]] From mzb@deve| @end|ng |rom gm@||@com Mon Jan 22 06:30:00 2024 From: mzb@deve| @end|ng |rom gm@||@com (Mauricio Zambrano-Bigiarini) Date: Mon, 22 Jan 2024 02:30:00 -0300 Subject: [R-pkgs] hydroGOF back on CRAN (v0.5-4 released) Message-ID: Dear all, After being archived on CRAN on 2023-10-16 , hydroGOF is finally back on CRAN since January 21th: https://cran.r-project.org/package=hydroGOF. This new version 0.5-4 includes: *) the following new functions: -) KGElf (Garc?a et al., 2017), -) sKGE (Fowler et al., 2018), -) KGEnp (Pool et al., 2018), -) dr (Willmott et al., 2012), -) ubRMSE (Entekhabi et al., 2010), -) wNSE (Hundecha and Bardossy, 2004), -) rSpearman, -) R2 *) improvements: all the functions allow you to apply a user-defined function (e.g., log, sqrt) to both 'sim' and 'obs' before computing the goodness-of-fit measure. A detailed description of the previous changes can be found for the 0.5-0 version in the new News.md file: https://cran.r-project.org/web/packages/hydroGOF/news/news.html A much improved vignette illustrate the usage of some of the main functions: *) Analysis of daily precipitation data: https://cloud.r-project.org/web/packages/hydroGOF/vignettes/hydroGOF_Vignette.pdf If you use this package, please cite it correctly as: Zambrano-Bigiarini, Mauricio (2024). hydroGOF: Goodness-of-fit functions for comparison of simulated and observed hydrological time series. R package version 0.5-4. URL:https://cran.r-project.org/package=hydroGOF. doi:10.5281/zenodo.839854. Issues and enhancements can be requested on: https://github.com/hzambran/hydroGOF/issues All the best, Mauricio Mauricio Zambrano-Bigiarini, PhD Associate Professor, Universidad de La Frontera Associate Researcher, (CR)2 FONDAP Center Phone: +56 45 259 2812 <+56+45+259+2812> e-mail: mauricio.zambrano at ufrontera.cl [image: webpage] [image: ORCID] [image: Github] [image: Linkedin] [[alternative HTML version deleted]] From v|ncent@@ud|g|er @end|ng |rom cn@m@|r Wed Mar 13 10:10:23 2024 From: v|ncent@@ud|g|er @end|ng |rom cn@m@|r (Vincent Audigier) Date: Wed, 13 Mar 2024 10:10:23 +0100 Subject: [R-pkgs] clusterMI: Cluster Analysis with Missing Values by Multiple Imputation Message-ID: Dear all, I am pleased to announce the release of a new package named 'clusterMI' on CRAN. clusterMI allows clustering of incomplete observations by addressing missing values using multiple imputation. For achieving this goal, the methodology consists in three steps: 1. missing data imputation using tailored imputation models: four multiple imputation methods are proposed, two are based on joint modelling (JM-GL and JM-DP) and two are fully sequential methods (FCS-homo and FCS-hetero). 2. cluster analysis of imputed data sets: six clustering methods are available (kmeans, pam, clara, hierarchical clustering, fuzzy c-means and gaussian mixture), but custom methods can also be easily used. 3. partition pooling: the set of partitions is aggregated using NMF based method. An associated instability measure is computed by bootstrap. Among applications, this instability measure can be used to choose a number of clusters with missing values. The package also offers several diagnostic tools for tuning the number of imputed data sets, for checking convergence in sequential imputation, for checking the fit of imputation models, etc. This is the first version of the package, your feedback and suggestions are welcome! Please find more details and download the package from the following link:https://cran.r-project.org/package=clusterMI Best regards, V. Audigier -- Vincent AUDIGIER Associate Professor, CNAM 2 rue Cont? 75003 Paris Office 35.3.21 Tel: 01 40 27 27 19 Website:http://vincentaudigier.weebly.com/ From RYAN@A@PETERSON @end|ng |rom CUANSCHUTZ@EDU Thu Mar 28 22:24:11 2024 From: RYAN@A@PETERSON @end|ng |rom CUANSCHUTZ@EDU (Peterson, Ryan) Date: Thu, 28 Mar 2024 21:24:11 +0000 Subject: [R-pkgs] New package - fastTS Message-ID: Hi R enthusiasts, I am happy to announce a new package available on CRAN: fastTS (https://cran.r-project.org/web/packages/fastTS/). fastTS is especially useful for large time series with exogenous features and/or complex seasonality (i.e. with multiple modes), allowing for possibly high-dimensional feature sets. The method can also facilitate inference on exogenous features, conditional on a series' autoregressive structure. The regularization-based method is considerably faster than competitors, while often producing more accurate predictions. See our open-access publication for more information: https://doi.org/10.1177/1471082X231225307 The package has several vignettes, one of which is an detailed walkthrough of an application to an (included) data set consisting of an hourly series of arrivals into the University of Iowa Emergency Department with concurrent local temperature. If you encounter any issues or would like to make contributions, please do so via the package's GitHub page: https://github.com/petersonR/fastTS Best, Ryan Ryan Peterson Assistant Professor Department of Biostatistics and Informatics University of Colorado - Anschutz Medical Campus [[alternative HTML version deleted]] From v|to@muggeo @end|ng |rom un|p@@|t Thu May 16 10:47:33 2024 From: v|to@muggeo @end|ng |rom un|p@@|t (Vito Muggeo) Date: Thu, 16 May 2024 10:47:33 +0200 Subject: [R-pkgs] segmented 2.1-0 is released Message-ID: dear R users, I am pleased to announce that segmented 2.1-0 is now available on CRAN. segmented focuses on estimation of breakpoints/changepoints of segmented, i.e. piecewise linear, relationships in (generalized) linear models. Starting with version 2.0-0, it is also possible to model stepmented, i.e. piecewise constant, effects. In the last release both models may be fitted via a formula interface, such as segreg(y ~ seg(x1, npsi=2) + seg(x2) + z) stepreg(y ~ seg(x1, npsi=2) + seg(x2) +seg(x3, npsi=3) + z, family=poisson) There is virtually no limit in the number of covariates and corresponding number of changepoints to be estimated. thank you, kind regards, Vito -- ================================================= Vito M.R. Muggeo, PhD Professor of Statistics Dip.to Sc Econom, Az e Statistiche Universit? di Palermo viale delle Scienze, edificio 13 90128 Palermo - ITALY tel: 091 23895240; fax: 091 485726 http://www.unipa.it/persone/docenti/m/vito.muggeo Assoc Editor: Statist Modelling, Statist Meth Appl past chair, Statistical Modelling Society coordinator, PhD Program in Econ, Businss, Statist From gb@rc@ro|| @end|ng |rom gm@||@com Sat Jun 1 13:27:16 2024 From: gb@rc@ro|| @end|ng |rom gm@||@com (Giulio Barcaroli) Date: Sat, 1 Jun 2024 13:27:16 +0200 Subject: [R-pkgs] QGA 1.0 is released Message-ID: <7ce4d41b-92e3-435c-a9e8-2e238f74c52e@gmail.com> Dear R users, I am pleased to announce that QGA 1.0 is now available on CRAN. QGA implements the Quantum Genetic Algorithm, as proposed by Han and Kim in 2000, and is an R implementation derived from the Python one by Lahoz-Beltra in 2016. Under this approach, each solution is represented as a sequence of (qu)bits. Simulating the quantum paradigm, these qubits are in a superposition state: when measuring them, they collapse in a 0 or 1 state. After measurement, the solution's fitness is calculated as in usual genetic algorithms. The evolution at each iteration is oriented by the application of two quantum gates to the amplitudes of the qubits: (1) a rotation gate (always); (2) a Pauli-X gate (optionally). The rotation is based on the theta angle values: higher values allow a quicker evolution, and lower values avoid local maxima. The Pauli-X gate is equivalent to the classical mutation operator and determines the swap between alfa and beta amplitudes of a given qubit. The package has been developed in such a way as to permit a complete separation between the 'engine', and the particular problem subject to combinatorial optimization. This is evident in the available examples, that come with the package, illustrating the application of QGA to different problems: knapsack, traveler salesman, and clustering. Thank you, kind regards, Giulio Barcaroli [[alternative HTML version deleted]]