tag:crantastic.org,2005:/packages/dynrLatest activity for dynr2018-09-24T18:40:59Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/797962018-09-24T18:40:59Z2018-09-24T18:40:59Zdynr was upgraded to version 0.1.13-4<a href="/packages/dynr">dynr</a> was <span class="action">upgraded</span> to version <a href="/packages/dynr/versions/76125">0.1.13-4</a><br /><h3>Package description:</h3><p>Intensive longitudinal data have become increasingly prevalent in various scientific disciplines. Many such data sets are noisy, multivariate, and multi-subject in nature. The change functions may also be continuous, or continuous but interspersed with periods of discontinuities (i.e., showing regime switches). The package 'dynr' (Dynamic Modeling in R) is an R package that implements a set of computationally efficient algorithms for handling a broad class of linear and nonlinear discrete- and continuous-time models with regime-switching properties under the constraint of linear Gaussian measurement functions. The discrete-time models can generally take on the form of a state- space or difference equation model. The continuous-time models are generally expressed as a set of ordinary or stochastic differential equations. All estimation and computations are performed in C, but users are provided with the option to specify the model of interest via a set of simple and easy-to-learn model specification functions in R. Model fitting can be performed using single- subject time series data or multiple-subject longitudinal data.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/795292018-09-16T16:40:57Z2018-09-16T16:40:57Zdynr was upgraded to version 0.1.13-3<a href="/packages/dynr">dynr</a> was <span class="action">upgraded</span> to version <a href="/packages/dynr/versions/75866">0.1.13-3</a><br /><h3>Package description:</h3><p>Intensive longitudinal data have become increasingly prevalent in various scientific disciplines. Many such data sets are noisy, multivariate, and multi-subject in nature. The change functions may also be continuous, or continuous but interspersed with periods of discontinuities (i.e., showing regime switches). The package 'dynr' (Dynamic Modeling in R) is an R package that implements a set of computationally efficient algorithms for handling a broad class of linear and nonlinear discrete- and continuous-time models with regime-switching properties under the constraint of linear Gaussian measurement functions. The discrete-time models can generally take on the form of a state- space or difference equation model. The continuous-time models are generally expressed as a set of ordinary or stochastic differential equations. All estimation and computations are performed in C, but users are provided with the option to specify the model of interest via a set of simple and easy-to-learn model specification functions in R. Model fitting can be performed using single- subject time series data or multiple-subject longitudinal data.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/795032018-09-15T05:20:54Z2018-09-15T05:20:54Zdynr was upgraded to version 0.1.13-2<a href="/packages/dynr">dynr</a> was <span class="action">upgraded</span> to version <a href="/packages/dynr/versions/75840">0.1.13-2</a><br /><h3>Package description:</h3><p>Intensive longitudinal data have become increasingly prevalent in various scientific disciplines. Many such data sets are noisy, multivariate, and multi-subject in nature. The change functions may also be continuous, or continuous but interspersed with periods of discontinuities (i.e., showing regime switches). The package 'dynr' (Dynamic Modeling in R) is an R package that implements a set of computationally efficient algorithms for handling a broad class of linear and nonlinear discrete- and continuous-time models with regime-switching properties under the constraint of linear Gaussian measurement functions. The discrete-time models can generally take on the form of a state- space or difference equation model. The continuous-time models are generally expressed as a set of ordinary or stochastic differential equations. All estimation and computations are performed in C, but users are provided with the option to specify the model of interest via a set of simple and easy-to-learn model specification functions in R. Model fitting can be performed using single- subject time series data or multiple-subject longitudinal data.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/713862018-02-08T21:40:51Z2018-02-08T21:40:51Zdynr was upgraded to version 0.1.12-5<a href="/packages/dynr">dynr</a> was <span class="action">upgraded</span> to version <a href="/packages/dynr/versions/68179">0.1.12-5</a><br /><h3>Package description:</h3><p>Intensive longitudinal data have become increasingly prevalent in various scientific disciplines. Many such data sets are noisy, multivariate, and multi-subject in nature. The change functions may also be continuous, or continuous but interspersed with periods of discontinuities (i.e., showing regime switches). The package 'dynr' (Dynamic Modeling in R) is an R package that implements a set of computationally efficient algorithms for handling a broad class of linear and nonlinear discrete- and continuous-time models with regime-switching properties under the constraint of linear Gaussian measurement functions. The discrete-time models can generally take on the form of a state- space or difference equation model. The continuous-time models are generally expressed as a set of ordinary or stochastic differential equations. All estimation and computations are performed in C, but users are provided with the option to specify the model of interest via a set of simple and easy-to-learn model specification functions in R. Model fitting can be performed using single- subject time series data or multiple-subject longitudinal data.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/656652017-08-21T08:40:46Z2017-08-21T08:40:46Zdynr was upgraded to version 0.1.11-8<a href="/packages/dynr">dynr</a> was <span class="action">upgraded</span> to version <a href="/packages/dynr/versions/62793">0.1.11-8</a><br /><h3>Package description:</h3><p>Intensive longitudinal data have become increasingly prevalent in various scientific disciplines. Many such data sets are noisy, multivariate, and multi-subject in nature. The change functions may also be continuous, or continuous but interspersed with periods of discontinuities (i.e., showing regime switches). The package 'dynr' (Dynamic Modeling in R) is an R package that implements a set of computationally efficient algorithms for handling a broad class of linear and nonlinear discrete- and continuous-time models with regime-switching properties under the constraint of linear Gaussian measurement functions. The discrete-time models can generally take on the form of a state- space or difference equation model. The continuous-time models are generally expressed as a set of ordinary or stochastic differential equations. All estimation and computations are performed in C, but users are provided with the option to specify the model of interest via a set of simple and easy-to-learn model specification functions in R. Model fitting can be performed using single- subject time series data or multiple-subject longitudinal data.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/635972017-06-17T00:20:38Z2017-06-17T00:20:38Zdynr was upgraded to version 0.1.11-2<a href="/packages/dynr">dynr</a> was <span class="action">upgraded</span> to version <a href="/packages/dynr/versions/60829">0.1.11-2</a><br /><h3>Package description:</h3><p>Intensive longitudinal data have become increasingly prevalent in various scientific disciplines. Many such data sets are noisy, multivariate, and multi-subject in nature. The change functions may also be continuous, or continuous but interspersed with periods of discontinuities (i.e., showing regime switches). The package 'dynr' (Dynamic Modeling in R) is an R package that implements a set of computationally efficient algorithms for handling a broad class of linear and nonlinear discrete- and continuous-time models with regime-switching properties under the constraint of linear Gaussian measurement functions. The discrete-time models can generally take on the form of a state- space or difference equation model. The continuous-time models are generally expressed as a set of ordinary or stochastic differential equations. All estimation and computations are performed in C, but users are provided with the option to specify the model of interest via a set of simple and easy-to-learn model specification functions in R. Model fitting can be performed using single- subject time series data or multiple-subject longitudinal data.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/627732017-05-21T08:20:44Z2017-05-21T08:20:44Zdynr was upgraded to version 0.1.10-10<a href="/packages/dynr">dynr</a> was <span class="action">upgraded</span> to version <a href="/packages/dynr/versions/60030">0.1.10-10</a><br /><h3>Package description:</h3><p>Intensive longitudinal data have become increasingly prevalent in various scientific disciplines. Many such data sets are noisy, multivariate, and multi-subject in nature. The change functions may also be continuous, or continuous but interspersed with periods of discontinuities (i.e., showing regime switches). The package \pkg{dynr} (Dynamic Modeling in R) is an R package that implements a set of computationally efficient algorithms for handling a broad class of linear and nonlinear discrete- and continuous-time models with regime-switching properties under the constraint of linear Gaussian measurement functions. The discrete-time models can generally take on the form of a state- space or difference equation model. The continuous-time models are generally expressed as a set of ordinary or stochastic differential equations. All estimation and computations are performed in C, but users are provided with the option to specify the model of interest via a set of simple and easy-to-learn model specification functions in R. Model fitting can be performed using single- subject time series data or multiple-subject longitudinal data.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/597952017-03-13T18:03:42Z2017-03-13T18:03:42Zdynr was upgraded to version 0.1.9-20<a href="/packages/dynr">dynr</a> was <span class="action">upgraded</span> to version <a href="/packages/dynr/versions/57230">0.1.9-20</a><br /><h3>Package description:</h3><p>Dynamic modeling of all kinds in R. These include models of processes in discrete time or continuous time. They also include processes that are linear or nonlinear. Latent variables can be continuous (e.g. state space models) or discrete (e.g. regime-switching models). The general approach involves maximum likelihood estimation of single- and multi-subject models of latent time series with the extended Kalman filter and Kim filter. The user provides recipes and data which are combined into a model that is then cooked to obtain free parameter estimates.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/579922017-01-09T16:42:43Z2017-01-09T16:42:43Zdynr was upgraded to version 0.1.8-17<a href="/packages/dynr">dynr</a> was <span class="action">upgraded</span> to version <a href="/packages/dynr/versions/55514">0.1.8-17</a><br /><h3>Package description:</h3><p>Dynamic modeling of all kinds in R. These include models of processes in discrete time or continuous time. They also include processes that are linear or nonlinear. Latent variables can be continuous (e.g. state space models) or discrete (e.g. regime-switching models). The general approach involves maximum likelihood estimation of single- and multi-subject models of latent time series with the extended Kalman filter and Kim filter. The user provides recipes and data which are combined into a model that is then cooked to obtain free parameter estimates.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/540702016-08-18T15:15:22Z2016-08-18T15:15:22Zcrantastic_production tagged dynr with TimeSeries<a href="/users/146">crantastic_production</a> <span class="action">tagged</span> <a href="/packages/dynr">dynr</a> with <a href="/task_views/TimeSeries">TimeSeries</a>crantastic_productiontag:crantastic.org,2005:TimelineEvent/519102016-06-09T19:00:32Z2016-06-09T19:00:32Zdynr was released<a href="/packages/dynr">dynr</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Intensive longitudinal data have become increasingly prevalent in various scientific disciplines. Many such data sets are noisy, multivariate, and multi-subject in nature. The change functions may also be continuous, or continuous but interspersed with periods of discontinuities (i.e., showing regime switches). The package 'dynr' (Dynamic Modeling in R) is an R package that implements a set of computationally efficient algorithms for handling a broad class of linear and nonlinear discrete- and continuous-time models with regime-switching properties under the constraint of linear Gaussian measurement functions. The discrete-time models can generally take on the form of a state- space or difference equation model. The continuous-time models are generally expressed as a set of ordinary or stochastic differential equations. All estimation and computations are performed in C, but users are provided with the option to specify the model of interest via a set of simple and easy-to-learn model specification functions in R. Model fitting can be performed using single- subject time series data or multiple-subject longitudinal data.</p>crantastic.org