tag:crantastic.org,2005:/authors/5470Latest activity for Fred Viole2020-03-17T15:02:42Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/1004622020-03-17T15:02:42Z2020-03-17T15:02:42ZNNS was upgraded to version 0.5.0<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/95451">0.5.0</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/988122020-02-13T17:22:46Z2020-02-13T17:22:46ZNNS was upgraded to version 0.4.9<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/93894">0.4.9</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/966952020-01-08T10:22:32Z2020-01-08T10:22:32ZNNS was upgraded to version 0.4.8<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/91906">0.4.8</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/950052019-11-21T00:22:50Z2019-11-21T00:22:50ZNNS was upgraded to version 0.4.7.1<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/90310">0.4.7.1</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/949612019-11-19T18:42:55Z2019-11-19T18:42:55ZNNS was upgraded to version 0.4.7<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/90276">0.4.7</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/934642019-10-07T16:01:59Z2019-10-07T16:01:59ZNNS was upgraded to version 0.4.6<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/88849">0.4.6</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/925352019-09-09T17:02:36Z2019-09-09T17:02:36ZNNS was upgraded to version 0.4.5<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/87993">0.4.5</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/915622019-08-08T16:02:15Z2019-08-08T16:02:15ZNNS was upgraded to version 0.4.4<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/87054">0.4.4</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/908282019-07-19T17:02:19Z2019-07-19T17:02:19ZNNS was upgraded to version 0.4.3<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/86348">0.4.3</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/894822019-06-11T16:42:18Z2019-06-11T16:42:18ZNNS was upgraded to version 0.4.2<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/85073">0.4.2</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/894542019-06-10T22:02:14Z2019-06-10T22:02:14ZNNS was upgraded to version 0.4.1<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/85045">0.4.1</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/884112019-05-14T18:42:10Z2019-05-14T18:42:10ZNNS was upgraded to version 0.4.0<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/84045">0.4.0</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/873082019-04-15T14:42:10Z2019-04-15T14:42:10ZNNS was upgraded to version 0.3.9<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/82998">0.3.9</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/854112019-03-04T13:42:10Z2019-03-04T13:42:10ZNNS was upgraded to version 0.3.8.8<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/81201">0.3.8.8</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/750282018-05-15T17:01:57Z2018-05-15T17:01:57ZNNS was upgraded to version 0.3.8.7<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/71630">0.3.8.7</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/738062018-04-16T11:22:48Z2018-04-16T11:22:48ZNNS was upgraded to version 0.3.8.6<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/70461">0.3.8.6</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/726892018-03-17T06:41:52Z2018-03-17T06:41:52ZNNS was upgraded to version 0.3.8.4<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/69383">0.3.8.4</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/716742018-02-16T19:21:49Z2018-02-16T19:21:49ZNNS was upgraded to version 0.3.8.3<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/68454">0.3.8.3</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/701752018-01-08T20:01:43Z2018-01-08T20:01:43ZNNS was upgraded to version 0.3.8.2<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/67024">0.3.8.2</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modelling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/692972017-12-08T16:21:30Z2017-12-08T16:21:30ZNNS was upgraded to version 0.3.8.1<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/66251">0.3.8.1</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modelling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/682982017-11-13T05:41:38Z2017-11-13T05:41:38ZNNS was upgraded to version 0.3.8<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/65335">0.3.8</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modelling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/669382017-09-30T19:01:48Z2017-09-30T19:01:48ZNNS was upgraded to version 0.3.7<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/64025">0.3.7</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modelling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/654862017-08-15T22:01:43Z2017-08-15T22:01:43ZNNS was upgraded to version 0.3.6<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/62637">0.3.6</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modelling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/648062017-07-23T16:41:32Z2017-07-23T16:41:32ZNNS was upgraded to version 0.3.5<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/61983">0.3.5</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modelling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/639382017-06-27T16:01:38Z2017-06-27T16:01:38ZNNS was upgraded to version 0.3.4<a href="/packages/NNS">NNS</a> was <span class="action">upgraded</span> to version <a href="/packages/NNS/versions/61170">0.3.4</a><br /><h3>Package description:</h3><p>Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modelling, Normalization and Stochastic dominance. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).</p>crantastic.org