Metadata-Version: 2.1
Name: NNS
Version: 0.1.6
Summary: Nonlinear nonparametric statistics using partial moments
Home-page: https://github.com/OVVO-Financial/NNS-Python/
Author: Fred Viole, Roberto Spadim
Author-email: ovvo.financial.systems@gmail.com
License: UNKNOWN
Download-URL: https://github.com/OVVO-Financial/NNS-Python/archive/refs/tags/v_016.tar.gz
Description: # NNS-Python
        Nonlinear Nonparametric Statistics
        
        From Beta R Version of 2021-12-13 (Version: 8.4-Beta, Date: 2021-12-13) 
        
        Implemented Functions:
        
        * ANOVA
            * NNS.ANOVA: TODO (deps: NNS.ANOVA.bin)
            
        * ARMA
            * NNS.ARMA: TODO (deps: NNS.seas, ARMA.seas.weighting, NNS.meboot)
            
        * ARMA_optim:
            * NNS.ARMA.optim: TODO (deps: NNS.ARMA)
            
        * Binary_ANOVA
            * NNS.ANOVA.bin: OK
        
        * Boost
            * NNS.boost: TODO (deps: NNS.caus, NNS.reg, NNS.stack)
            
        * Causal_Matrix
            * NNS.caus.matrix: TODO (deps: NNS.caus)
        
        * Causation
            * NNS.caus: TODO (deps: Uni.caus, NNS.caus.matrix)
        
        * Copula
            * NNS.copula: OK
            
        * Dependence
            * NNS.dep: TODO (deps: NNS.part, NNS.dep.matrix)
            
        * Dependence_matrix
            * NNS.dep.matrix: TODO (deps: NNS.dep)
            
        * dy_d_wrt
            * dy.d_: TODO (deps: NNS.reg)
        
        * dy_dx
            * dy_dx: TODO (deps: NNS.dep, NNS.reg)
        
        * Internal Functions
            * mode: TEST
            * mode_class: TODO
            * gravity: TEST
            * gravity_class: TODO
            * factor_2_dummy: TODO
            * factor_2_dummy_FR: TODO
            * generate_vectors: TODO
            * ARMA_seas_weighting: TODO
            * is.discrete: TODO
            * lag_mtx: TODO
            * NNS_meboot_part: TODO
            * NNS_meboot_expand_sd: TODO
            * alt_cbind: TEST (not in newest version, maybe R related)
            * RP: TODO (not in newest version)
        
        * LPM UPM VaR
            * LPM_VaR: OK
            * UPM_VaR: OK
            * used np.quantile instead of tdigest, and root_scalar instead of optimize
        
        * Multivariate_Regression
            * NNS.M.reg: TODO (deps: NNS.part, NNS.dep, NNS::NNS.distance, NNS.copula, NNS.reg)
        
        * NNS_Distance
            * NNS.distance: TODO (deps: dtw, Rfast)
        
        * NNS_meboot
            * NNS.meboot: TODO (deps: NNS.dep, NNS.meboot.expand.sd)
            
        * NNS_term_matrix
            * NNS.term.matrix: OK
        
        * NNS_VAR
            * NNS.VAR: TODO (deps: NNS.reg, NNS.seas, NNS.ARMA.optim, NNS.ARMA, NNS.stack, NNS.dep, NNS.caus)
        
        * Normalization
            * NNS.norm: TODO (deps: NNS.dep, Rfast)
        
        * Nowcast
            * NNS.nowcast: TODO (deps: Quandl, NNS.VAR)
        
        * Numerical Differentiation
            * NNS.diff: TODO (nodeps)
            
        * Partition_Map
            * NNS.part: TODO (deps: internal functions: gravity_class, gravity, mode_class)
        
        * Partial Moments
            * pd_fill_diagonal: OK (Internal use)
            * LPM: OK Tested
                * numba_LPM: Numba version (Internal use)
                * LPM: Vectorized / pandas / numpy friendly
            * UPM: OK Tested
                * numba_UPM: Numba version (Internal use)
                * UPM: Vectorized / pandas / numpy friendly
            * Co_UPM: OK Tested
                * _Co_UPM: Internal Use
                * _vec_Co_UPM: numpy.vectorized
                * Co_UPM: Vectorized / pandas / numpy friendly
            * Co_LPM: OK Tested
                * _Co_LPM: Internal Use
                * _vec_Co_LPM: numpy.vectorized
                * Co_LPM: Vectorized / pandas / numpy friendly
            * D_LPM: OK Tested
                * _D_LPM: Internal User
                * _vec_D_LPM: numpy.vectorized
                * D_LPM: Vectorized / pandas / numpy friendly 
            * D_UPM: OK Tested
                * _D_UPM: Internal User
                * _vec_D_UPM: numpy.vectorized
                * D_UPM: Vectorized / pandas / numpy friendly 
            * PM_matrix: OK
            * LPM_ratio: OK
            * UPM_ratio: OK
            * NNS_PDF: TODO (deps: d/dx approximation, density)
            * NNS_CDF: TODO (deps: ecdf, density, matplotlib, NNS_reg)
        
        * Regression
            * NNS.reg: TODO (deps: NNS.M.reg, NNS.dep, NNS.part, Uni.caus)
        
        * SD Efficient Set
            * NNS_SD_efficient_set: OK (TODO: numba version?)
        
        * Seasonality_Test
            * NNS.seas: TODO (nodeps)
            
        * Stack
            * NNS.stack: TODO (deps: NNS.reg, NNS::NNS.distance)
            
        * Uni_Causation
            * Uni.caus: TODO (deps: NNS.norm, NNS.dep)
            
        * FSD, SSD, TSD
            * NNS_FSD: OK (TODO: numba version?)
            * NNS_SSD: OK (TODO: numba version?)
            * NNS_TSD: OK (TODO: numba version?)
        
        * Uni SD Routines
            * NNS_FSD_uni: OK (TODO: numba version?)
            * NNS_SSD_uni: OK (TODO: numba version?)
            * NNS_TSD_uni: OK (TODO: numba version?)
        
        * Others Todos:
            * Try to make names equal to R version 
              * R accept $ and . we will replace to underline _
              * TODO: R export functions from modules 
              
Keywords: Nonlinear nonparametric regression classification clustering
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: test
